338: Build AI Superintelligence with Ganesh Krishnan

https://youtu.be/b_G8krkwKv8 Ganesh Krishnan, CEO of AiHello, is helping Amazon sellers automate advertising, improve profitability, and scale their businesses using AI. Driven by a mission to give entrepreneurs more freedom and enable them to build businesses around products they love, Ganesh shares how AI can eliminate repetitive work while allowing business owners to focus on strategy, innovation, and growth. In this conversation, Ganesh introduces The AiHello Ads Framework: Tap into the Wisdom of Crowds, Find the Right Keywords, Bid at the Right Level, Dynamically Adjust Bids, and Rinse and Repeat. He explains how AI can leverage historical marketplace data to identify profitable keywords, optimize bids automatically, and continuously improve campaign performance. Ganesh also discusses the dangers of AI hallucinations, why Amazon’s incentives differ from sellers’ incentives, how AI has transformed his own company’s operations, and his vision for building zero-hallucination AI systems capable of advancing toward artificial superintelligence. — Build AI Superintelligence with Ganesh Krishnan  Good day, dear listeners. Steve Preda here, and welcome Ganesh Krishnan, the CEO of AiHello, an Amazon Ads automation company helping you grow your revenues, reduce work hours spent on ads management, and decrease your ad costs. Welcome to the show, Ganesh.  Thank you, Steve. Nice to meet you  Well, it’s great to have you here, and let’s jump right in. And my first question is, what is your personal ‘Why,’ and how are you manifesting it in AiHello?  So it started off with my thesis that we all need to do good towards the planet. A long time ago, I started having my own natural things, selling chemical-free, ecological, sustainable, good-for-the-planet, good-for-your-wallet, good-for-your-health items, and I would sell organic items. And eventually, what I realized was that it was taking a lot of my time marketing, managing it, changing the bids, doing everything. I started working more and more on AI because I’ve worked in AI commercially. I worked in AI in my industry. That was my job. So I said, “Why not use, apply that to my own startup, to my own industry for selling organic things?” And once I started selling it, some of my friends reached out and said, “Can we use your AI for our own businesses?” And I said, “Sure, why not?”  And then I started opening it up. And then one person came through and said, “Okay, let’s release it to the general public, see how it goes.” And then as we started earning money, I realized that I don’t need to do a job. I can have this startup, and I can help different people have their own lifestyle. You could have your own lifestyle. You could sell your own stuff that you like, e-commerce, usually on Amazon, and then we help you have your lifestyle. So this is my personal ‘Why’, is we need more equality. We need more people doing stuff they love rather than doing stuff they hate to do, and they hate to wake up and go to work. So do what you love. We are here to empower you.  Wow, that’s amazing. So you are empowering people to start their own e-commerce businesses on Amazon, and you help them with AI tools to get up to speed and compete with the big boys.  That is correct.  Yeah. I love it. So on your LinkedIn profile, you mentioned that you are, I don’t know what the word was that you used, but something to do with superintelligence, AI superintelligence. So what is it that you are doing, and what is your vision of how AI superintelligence can be tapped into?  It’s a very long topic. But to start off with, we used the old form of AI, which is a lot of regression, a lot of statistics, a lot of big data learning, and a lot of neural networks, if you felt fancy. And then LLMs became a huge thing. And we launched AiHello probably six or seven years ago. LLMs became a big thing two or three years ago. And it was pretty fancy. It was very good. It made life easy for us. But we cannot use it within AiHello to give it to clients, primarily because LLMs start hallucinating once you go past a certain context. The problem with hallucination is that it exponentially becomes larger and larger. Because if the previous thesis is wrong, if your previous hypothesis is wrong, then it builds on top of it, and it builds the wrong things.  Hallucination exponentially becomes worse. And when it comes to finance, when it comes to ads, and when you’re working with sensitive data, this can be catastrophic. So you cannot use these large language models for finance, for situations where you need precise data, and especially when you have lots of context. It’s going to lose the context of the first part. Just because you mentioned something at the start of the conversation doesn’t mean it’s not important. It is critical. As humans, we understand what is the most critical part of a conversation, and then we keep that in mind. But LLMs, because of context limitations, just keep on going and start hallucinating.  So a few months ago, we came up with the idea that we could use something like a large language model, but not based on the transformer model. And we could base it on data so that there is almost zero hallucination. So instead of building weights, we build it based on data. And we launched this. We don’t use it on AiHello, but we decided to use it on an email service because we have a lot of emails. We process a lot of emails for clients. We process a lot of emails for specialists. So we could use the zero-hallucination approach within emails, and if it is successful, then we can put it into AiHello.  And we can, of course, release it as an API as well. So this is going to set the basis of artificial superintelligence because what is stopping us right now from reaching or breaching that wall of artificial superintelligence is this hallucination. And of course, there is also logic. LLMs are pretty stup*d. They don’t understand. You can teach them, they learn, but they do not question what you teach them. They always take it on blind faith.  Yeah. Wow. That is genius. I love it. You are going to un-hallucinate AI. And if it stops hallucinating, essentially it becomes a lot more powerful and scalable. AI becomes scalable, or this whole process becomes scalable. That’s fascinating. So your ‘Why’, your mission, is to empower all these people to run their businesses. Do you have a framework for this that you could describe in three to five steps? How do you get someone up and running with their own business on an e-commerce platform? Or do you have any other framework that you could share with the audience? Something simple that they may be able to benefit from? One of the caveats of using AI is that it needs a lot of data. So if you’re just starting out with your e-commerce business, you need to put more of your human intelligence, more of your gut instinct, more of your thoughts, and more of your emotions into building it out. And once you have built up enough data, then you can put it into AiHello and start automating it. So what I would say, if you’re starting an e-commerce business, is hire a specialist who can help you launch off the ground.  Do a bit of the hypothesis work, do a bit of the analysis, and then come to AiHello and start automating it. You can only start automating once you have a good idea of how things work for you. And finding how things work for you is something you need to do on your own. It’s like you can’t start running, or you can’t start driving a car, until you learn how to crawl and until you learn how to walk.  Okay. So basically, it’s the age-old innovation thing that you have to innovate something on your own, and then you can scale it with AI. That is correct.  Yeah. So let’s say I came up with some kind of formula, concept, or product that is currently not being promoted, and I believe it would work. Or maybe I’ve already tested it and I want to scale it. I want to get on Amazon and sell it there. What can you do for me? What are the steps for me to be successful with AiHello’s help? So the first thing when you select a product, is: what are the keywords for it? What keywords do you use for that product? The second would be: what are the bids for that product? For each keyword, what is the right bid to put up? And then you have other things like budgeting. Do you change the bid depending on the time of day? Do you change the bid in total? Those are the things that you need to keep adjusting continuously.  With AiHello, we automatically harvest the right keywords for your product. We change the bid. We optimize the bid. We also do dayparting, where you can change the bid depending on the time of day. So there are different things that you can use AI for. You could certainly do all of it manually, but it’ll probably take you days or weeks to do what AI can do in a couple of minutes.  So a couple of minutes. But doesn’t the AI also need traffic data to be able to define things?  Yeah. So one of the other things about AiHello is that, because we have the wisdom of crowds, if you come up with a keyword, we know exactly how that keyword is going to perform. As you say, you have the wisdom of crowds. Can you extrapolate what you’ve experienced with other products and other customers onto a new product that doesn’t yet have a lot of traffic? Is this what you mean by the wisdom of crowds? Or what do you mean by the wisdom of crowds?  Let me give you an example. Let’s assume you want to sell coffee, and you go to our platform and say, “This is my product. It’s coffee. Help me sell it.” So what we do is, we know this is coffee. What are the keywords around it that are going to help sell it? Because we’ve sold other coffee products, we know that organic coffee sells well. We know coffee in the morning sells well. Black coffee sells well. Caffeine sells well.  And we also know, based on the previous performance of other keywords, what a good bid is for each keyword. If you don’t know the keywords, then of course you have to spend time researching them. And if you don’t know the bids, then you have to spend time researching what bid to put in. But we do all the research for you, and you put it in. And the second part, the bigger part, is that if the bid doesn’t work out, if you’re not selling, then we increase the bid automatically. If you are losing money, then we decrease the bid automatically. So that bid optimization is a critical part of AiHello.  Yeah. We use Amazon ads to promote my books. And yes, it takes a lot of skill to find the keywords, eliminate the negative keywords, adjust the bids, have the right bids, and avoid overspending or underspending. But Amazon also does much of the machine learning. So what is it that Amazon does, and what is it that you have to do? And why doesn’t Amazon do what you have to do?  The most critical piece of information to keep in mind is that your aims and objectives are the opposite of Amazon’s aims and objectives. Amazon’s aim is to make money, and your job is to make money. You don’t care if Amazon makes money or not, and Amazon doesn’t care if you make money or not. So when you put up a bid, when you run ads, Amazon will maximize that ad spend, whatever it is. In some ways, it’s like a casino.  You go to a casino, and the job of the casino is to win money from you, and your job is to win money from the casino. Ads have become a lot like gambling nowadays. You throw money into it. You expect to make money. Ninety percent of people lose money, and they give up. And Amazon always finds fresh sellers to move on. You cannot depend on Amazon because Amazon is not on your side.  Yeah, that makes perfect sense. Yeah, I always thought that on some platforms it was really difficult to make money with ads. Facebook, I think, is so competitive that it’s probably very difficult to make money. I know a lot of people who have spent a lot of money on Facebook, but I don’t know very many who have figured out a formula that continues to work. Okay. So you’ve helped someone find their keywords, the right bids, and how to adjust those bids. But what we’ve found is that at some point, ads die, and then we have to switch things up. It actually happens quite frequently that you have to create new campaigns and new ads. So what’s the dynamic there? How do you optimize so that you’re not still supporting ads that don’t work anymore, and you switch at the right point?  So when we say ads, it’s not technically the campaigns. A campaign is just a container for all of your ads. You have products inside it, and you have keywords inside it. So a campaign is made up of products and keywords. And the question is, when you say ads die, did the keywords die? Then you need to add new keywords, right? You always have to keep adding new keywords and testing new keywords. It’s a continuous job of trying to find the right keywords for your book or your product, and then optimizing the bids constantly to make sure that you’re profitable.  You have to make sure that your ads don’t die because of a lack of fresh keywords. And of course, there’s always a limit to the number of keywords you can add because each product has a limited number of keywords that people are searching for. Maybe there’s a long-tail keyword that’s going to make money, but there’s not enough search volume. Or maybe there’s a high-volume search keyword, but it’s not profitable for you. So you have to figure out what the right strategy is for you. Eventually, if your product is good, you’ll make money. If your product is not good, you won’t make money. That’s the bottom line. With ads, you quickly find out if your product…  So essentially, it’s a cyclical thing. So you find the keywords, you figure out the right bids, you adjust the bids, and then you have to find new keywords and keep doing this.  Yeah.  So why do keywords go stale? Do people not search for certain things anymore?  There could be multiple reasons for it. One reason is that a competitor has come in and taken your search volume. And you have to know: are you losing search volume? Are you gaining search volume? Has your search volume dropped off? The second reason is that people are not searching for that keyword anymore. Is it out of fashion? The third is: are you underbidding? Is the bid too low? Again, you would know by the number of impressions. Have the impressions dropped off?  If the impressions have dropped off, is it because of a competitor? If it’s not because of a competitor, are people searching less? Are your bids too low? If the search volume is the same, are people clicking less? Why are they clicking less? Is it your images? Is it your product? Is your product no longer in fashion? I mean, I don’t know. Maybe a few months ago, fidget spinners were really in fashion, and nowadays no one uses them. So those things go out of fashion.  Yeah. The spinners, I remember. They’ve been out of fashion for a while.  Yeah.  Yeah, that’s fascinating. So it’s a never-ending cycle of innovation and figuring out what works and what doesn’t work. So let me ask you this: What drives growth in your business?  Most of the growth is… There are different ways to put it. Four years ago, we used to create a lot of blogs. We used to create lots of content. We used to create lots of YouTube videos. And then ChatGPT came along. If you ask kids now, “Do you Google that?” They don’t know what Google is. They really don’t know what Google is. And that’s not a cliché. It’s surprising. They’ll be like, “What Google?” Everything goes through ChatGPT.  So for us, growth went from Google to ChatGPT. And we didn’t spend enough time optimizing for LLMs on our site. So what drove growth before was blogs and YouTube. And what drives growth now is large language models like ChatGPT and Claude. People just ask ChatGPT, “What do I do about this on Amazon?” It recommends solutions, and then we go through them.  So how do you leverage large language models or AI applications?  This was one of the biggest boosts to our company. We managed to set the processes right. We managed to create the templates. We managed to bring structure to our company. Development work has become ten times faster. The turnaround is ten times faster. We’re able to release features quickly. We’re able to find bugs in our existing code quickly. There are a lot of things going on. If I were to say that our company is no longer the same company it was even a year ago, that would not be an exaggeration. It would be the truth. What we were a year ago is not at all what we are right now.  So in what way did you change? Is it coding that accelerated and changed everything? I mean, in what other ways did you change as a company?  So the code is all done with AI first. Our developers use AI. They put in the prompt, they check the results. There is a second developer who checks whether everything is okay and whether everything is done. And then finally there’s QA, and then we push it to staging. We used to do roughly one-month or forty-five-day sprints. Now we do weekly sprints. So it has gone four times faster. The biggest hurdle for us was managing clients and how we manage them. We never had any structure.  So we talked a lot with ChatGPT. We talked a lot about what the right way was to bring structure and accountability into the system. We managed to set up all the software required for accountability. It helped us fix those issues. It created structure. It created accountability for all the people, and then we implemented that. Finally, the last one, which was the most debatable, is that we require a lot of content. We require a lot of graphics. We require a lot of videos for clients on Amazon. I actually went to buy something on Amazon a few days back, and what was puzzling was that when I zoomed in on the images, you could see they were AI-generated because they all had these silly AI mistakes—spelling mistakes, random words.  So almost everything on Amazon right now, all the images, are kind of AI-generated. It’s hard to blame them. We ourselves use AI for a lot of the images. We make sure we don’t have the silly mistakes, but we do use AI as well. So the turnaround time for graphics is faster because of AI as well. Though some clients do complain that they don’t like AI-generated assets. And if a person looks a bit too AI-generated, they just reject it outright. So that is the most debatable part of it. But overall, our company is called AiHello. It’s AiHello. And if we don’t say hello to AI, then we’re not AiHello.  Yeah. Love it. I love the head and the one arm.  Yes.  The hello, and that’s it.  Yeah.  So what is one thing that you’re actively trying to figure out in your business right now? We are a remote-first company, and I’m struggling to bring about accountability among all the team members. We do have a good number of employees. Ninety percent of our employees are good. Ten percent still have accountability issues. And for me, that is a bit of a hurdle. It is a bit of a challenge to push those people who are dragging their feet about AI. Yeah. Because they are not comfortable with AI. They want to do what they are good at and don’t want to do something new.  There is also a bit of hesitation that they might lose their jobs because of AI, although we’re not planning to let go of anyone. Rather, we are hiring more people because we’re able to grow faster. There is an old saying that companies won’t go extinct because of AI, but companies that don’t use AI will go extinct because of AI. Because we are using AI a lot, there is a chance for us to scale, for us to expand significantly. And I want to tap into this advantage and grow. I want to hire more people, and I want to grow. I don’t want to let people go.  So this is a very good opportunity. You hear about Coinbase letting people go. You hear about Facebook letting people go because of AI. And I think those are all nonsensical excuses. Those companies are not growing very well, and they are blaming AI for letting people go, which I think is absolutely nonsensical. There is a very good opportunity for people to grow and for companies to grow using AI and increase their hiring. If you’re letting people go because of AI, it’s just a nonsensical excuse.  So what do you think is the mental hang-up for people? What prevents better AI adoption or faster AI adoption? A long time ago, when computers were being introduced into many industries, I remember there were huge protests because people thought computers would take away jobs. And it did happen. People did lose jobs because of computers. There were many people pushing papers who lost their jobs. And a lot of people refused to learn about computers because they said, “This is nonsensical. I can do it better by hand.” Can you imagine telling people right now that it’s better to do things by hand than to use a computer?  I mean, if you want to do calculations, please don’t use Excel or Google Sheets. Use a pen and paper and tell me you can do it better. It would be absurd to think that way. But at that time, people really did have the mentality that it was better to do things by hand than with Excel. Now, the AI revolution is probably a thousand or a million times bigger than that. And you can drag your feet. There will always be people who drag their feet and say, “I can do it better. AI is just nonsensical.” And sure, some of that is true. But the overwhelming majority of tasks are going to be done extremely well with AI.  And it’s not just large language models. It’s everything. Regression analysis, data analytics, big data analytics, forecasting, calculations. I’m not even talking about transformer models. I’m talking about everything related to AI. So much can be automated and done by AI that if you’re not involved with it, you’ll get left behind, just like the people who didn’t use computers. Do you feel like people have to be highly educated to be able to use AI? Or can people with less formal education benefit from it as well?  I don’t think it has anything to do with education. I think the learning curve for AI is smaller than the learning curve for computers. If you’re already using computers, you can just install a command-line interface and have things running. Actually, you can go to ChatGPT and ask some questions, and you can build something. But if you want to build serious applications, you can use a command-line interface and build them out. I think the learning curve is probably just a couple of hours to become proficient with these tools. I’m thinking more about this: As AI tools develop and take many of the routine, repeatable tasks off our shoulders, doesn’t that mean we will spend more of our time on high-level thinking and orchestration? And won’t that require some kind of mental ability to do that? It requires you to understand context, understand the implications of things, and be able to connect the dots. So that’s what I mean. The people who can really use AI tools have this higher level of awareness and thinking. They can combine ideas and create new things. But are there AI tools that people with less advanced analytical skills can also use? Absolutely. And you’re 100% right. You’re 101% right. This is what I’ve been advocating for a very long time. Don’t spend your time doing mundane, repetitive daily activities that can be automated. Let AI handle them. You should focus on the things AI cannot do right now, which is human-level intelligence: Strategizing. Planning. Working on the bigger-picture tasks. So you’re 100% right, and that’s the direction we should be moving in. And this brings me back to the point I made earlier: You should do what you love. The things you don’t love, the repetitive tasks, should be done by AI.  Yeah. Love it. So what is your vision, ultimately, for AiHello?  So my vision for AiHello goes beyond AiHello. We have something called HalZero, which is the engine we want to put behind AiHello. It’s a zero-hallucination LLM. And we are working toward making it happen. We plan to release an API for it soon. If it does happen, then we would probably have a model that can take in data and answer general-knowledge questions with zero hallucination. And we’re building it based on how the human brain works. The human brain is not one-dimensional. ChatGPT is one-dimensional. Transformer models are one-dimensional.  You give them data, they run it through the transformer model—the encoder and decoder—and then they give you an answer. But the human brain is built in layers. What we call the lizard brain sits at the base, and as you go higher, things become more and more complex. So the brain is information and action, and everything is filtered through it. Then we act on the filtered result. Machine learning models right now do not have these kinds of filters. They have something similar, which is called chain of thought, but that’s really thinking out loud. This kind of reasoning should exist within the latent space of the machine learning model. It should be built into the model itself.  I’ll give you an example. If you had been taught all your life that the sun is green, and tomorrow you woke up in Virginia, went outside, and saw that the sun was yellow, you’d say: “Oh my God, I’ve been lied to all my life. The sun isn’t green.” You would question what you had been taught based on a single observation. But if a machine had been trained for years that the sun is green, and then it saw that the sun was yellow, it might conclude: “The sun is wrong today because I’ve been taught that the sun is green.” The real test of intelligence is this: Can it question its training data? And the answer is no. It won’t, because it has been trained on that data. It has been trained on those tokens.  Yeah. So that’s AI superintelligence? The ability to question the training data?  That is correct. Yeah. So we build it based on connections. How strong is this connection? How many people have stated this fact? What is my own observation? Which observation is stronger? There is always conflict. In the human brain, there is always a conflict between what people say and what we think. Then our logical brain chooses what is usually the best answer. That is how we have a collective consciousness. We also have a personal consciousness. We always have to decide which one is best.  Love it. Well, that’s great. So if you’re running a business and you need to sell a product, and you want to figure out how to be successful on Amazon, how to leverage your ads, and how not to overspend, where should you go? How can people get in touch with you, Ganesh, and your team? And what’s the first step for listeners?  You can send me an email at [email protected]. You can connect with me on LinkedIn. I’m always available, and I’m happy to have a chat with you.  All right. So if you’re listening out there and you’re in e-commerce, or you want to get into e-commerce, and you don’t know how to leverage all the tools that are out there, don’t forget: Amazon is in the business of making money, not necessarily making your business profitable. So you can use AiHello to help you. Reach out to Ganesh on LinkedIn and get your team involved. And if you enjoyed listening to this episode, make sure you check back every week because I have successful entrepreneurs sharing their ideas—or at least some of the good ones—with you. So thanks, Ganesh, for coming.  Thank you, Steve.  And thank you for listening. Important Links: Ganesh’s LinkedIn Ganesh’s website Ganesh’s email: [email protected]