Talking to a billionaire about how he uses ChatGPT

0:00 So that's my advice is Every day. Every day. You should be in Chat GPT. I don't care what your job is, right? You could be a sommelier at a restaurant and you should be using ChatGPT every day to make yourself better. Um whatever it is you do.

0:19 Can I ask you about the story really quick? And you have like you have like a list of stuff here that's like all amazing. It's actually a lot of it's very actionable, but the reason I want to ask you about the story is for the listener, Darmash founded HubSpot thirty billion dollar company. You're the CTO, so you and you're you're an OG for uh Web One point oh, Web Two Po. And your first uh round or one of your first rounds was funded by Sequoia. Your partner, Brian, is an investor at Sequoia. So you are in the insider. Yeah, you're an insider, I believe. Uh you may not acknowledge it. I don't know if you do it or not. You are an insider. The cool part is that you're accessible to us. When did you first See what Sam was working on. And how long have you felt that this is gonna change everything? So I actually have um known Sam before he started open AI.

1:03 And I got access to um The GPT API. It was a toolkit for developers to be able to kinda build AI applications, right? Effectively, um And so I built

1:14 uh this little chat application that used the API And so I could have a conversation with it. So I actually built that thing uh that night. Uh it was it was a Sunday. Full transcript, two years before Chat CPT came out. So that's four years ago? Uh it was twenty twenty, so five years ago. Wow, okay. Uh the summer.

1:31 And so even then it's like And like as soon as I like You sort of have that moment the same that all of us have with Chad GPT. I just had it two years earlier, and then I'm showing everyone like Brian. You are not going to believe look, I have this thing. you know, through this company called Open AI and watch me like type stuff into it and see like see what happens. It's

1:50 And we would ask it like strategic questions about HubSpot. It's like how should it like who are the top competitor like And they were sh even then. Two years before chat, it was shockingly good, right? But the thing you sort of have to understand about the constraints of how a large language model actually works. Is that You type and you have a limited just imagine this if we're gonna just use the uh the physical analog or uh

2:11 There a sheet of paper can only fit a certain number of words on it. And that certain number of words includes both what you write on it that says, I want you to do this, and the response has to fit on that sheet of paper. And that sheet of paper is what Uh in technical terms will be called a context window. And you'll hear this tossed around. It's like oh this

2:31 Yeah, ChatGPT has a context window of whatever, or this model has a context window, whatever. That's what they're talking about. All right, so why is that why does anybody care about the context window? It's like well Sometimes you want to provide Um A large piece of text and say summarizes for me. Well, in order for you to do that, it has to fit in the context window. So if you want to take two books worth of information.

2:50 And say, Yeah, I want you to summarize this in fifty words. Those two books worth of information have to fit. inside the context window in order for the LM to process it. the the frontier models are roughly a hundred thousand to two hundred thousand They measured in tokens, which is like point seven five of a word, but

3:06 That's like a book. So yeah, is that a book? I think it's an average. I think the average book is like 240,000 words, uh, I think, but I'm not sure. That's not a lot. So when I the way that I use ChatGPT is I'll like uh let's say a fun ways I'll I'll put a historical book that I loved reading and I'll be like summarized this so I remember the details. So you're telling me that if it's a thousand page book, it's not even going to uh accurately summarize that book. It won't fit. Yeah, like if you paste something large enough into chat GPT or whatever um AI application you're using,

3:36 It will come back and say, Sorry, that doesn't fit. Effectively what they're saying is that does not fit in the context window. So you're gonna have to do something different. Alright, a few episodes ago I talked about something And I got thousands of messages asking me to go deeper and to explain. And that's what I'm about to do. So I told you guys how I use chat GPT as a life coach or a thought partner. And what I did was I uploaded all types of amazing information. So I uploaded my personal finances, my net worth, my goals.

4:06 Different books that I like. issues going on in my personal life and businesses. I uploaded so much information. And so the output is that I have this GPT that I can ask questions that I'm having Issues with in my life, like how should I respond to this email? What's the right decision, knowing that you know my goals for the future, things like that. And so I worked with HubSpot to put together a step by step process showing the audience, showing you The software that I used to make this, the information that I had Chat GPT ask me. All this stuff. So it's super easy for you to use. And like I said, I use this like 10 or 20 times a day. It's literally changed my life. And so if you want that, it's free. There's a link below. Just click it, enter your email, and we will send you everything you need to know to set this up in just about 20 minutes, and I'll show you how I use it again, 10 to 20 times a day.

4:53 Um all right, so check it out. The link is below in the description. Back to the episode. I usually use projects and I have like let's say a health project and I'll upload tons and tons of books or tons of blood blood work and I hope I'm hoping that it's gonna pull from all those books in my project. Is that true? That that is true. So here and and this is a perfect segue, right? Because this is the next Big unlock. So number one thing to like understand in our heads.

5:17 is there's this thing called a context window, here's why it matters. Um So let's we're gonna take a Um we're gonna pop that on the stack and we're gonna uh push down the stack and we're gonna come back to it. So the thing we have to remember is two things. Um number one It doesn't know what it's never been trained on. That's one of the limitations, right? So if you ask it something that only you, Sam, have in your

5:36 in your files in your email, whatever that train the training model was I mean the LM was never trained on, it's not going to know those things. Doesn't matter how smart it is, it's just information it's never seen. So it's not gonna know that. That's kinda problem number one. Problem number two. So let's say your website for Hampton was actually on um

5:55 Uh, in the training side, right? Because it's on the public internet or whatever. But the training happened at a particular point in time. Like they ran the training, ran the training, ran their training and said, Okay We're done with the training now. The machine is done.

6:07 Let's let the customers in, right? Now if the website changes it's not gonna know about those new updates that you make to your website because the training was done at a particular date, if completed It's kinda training course, right? So those are two things we sort of have to remember. Is that it doesn't know what it doesn't know.

6:23 And number two, that the things it did know were frozen at that put particular point in time. Right? So it has a scene new information. And those are Relatively large limitations, right? So especially if you're gonna use it for business use or personal fact, well I've got a bunch of stuff that I wanted to be able to kinda answer questions about or whatever, I inside my company or inside m uh

6:41 In my own personal life. How do I get it to do that? Um and so here's the hack that and this is this was a brilliant uh brilliant discovery. So what they figured out is to say, Okay Let's say you have a hundred thousand documents. That

6:55 We're never on the internet. That's in your company. It's all your Employee hiring practices, your model, here's how we do compensation, all of it, right? It's like all of you have a hundred thousand documents. And obviously you can't ask questions about those hundred thousand documents straight to Chad GPT, doesn't know anything about those, never seen those documents. So this is and we talked about this two episodes ago, um this thing called vector embeddings and Rag, retrieval augmented generation. Um and I'll I recommend you folks go listen to that. I think it's uh it's a it's a fun episode, but uh I'll s kind of summarize it, which is what you can do

7:24 And what we do is to say we're gonna take those hundred thousand documents And we're gonna put them in this special database called a vector store. A vector database. And what we can do now is when someone asks a question We can go to the vector store, not the LM.

7:37 go to the vector store and say, give me the five documents out of the hundred thousand that are most likely to answer this question based on the meaning of the question, not keywords, based on the actual meaning of the question. So it's called a semantic search is what the vector store is doing. So it comes back with five documents, let's just say. Now as it turns out, five documents do fit inside the context window.

7:59 So effectively we said, Okay, well, yeah, it would have been nice had you trained on the hundred thousand documents, but that was not practical because I didn't want to expose all of that. I'm gonna give you the five documents that you actually need. I'm just gonna give it to you. In the context window, and now, as you can imagine. It does an exceptionally good job at answering the question when it knows the five documents that should be looking. You just gave it to them, right? So it's having this uh So we'll kinda uh jump metaphors here. It's like hiring a really, really good intern that has a PhD in everything.

8:25 Right. They went to school, they read all the things, read all the internet. The intern knows everything about everything that ever was publicly accessible. They train Show up for the first day of work. That's all they know. They're not learning anything new and they know nothing about your business. Now it's like okay, well

8:39 I know you know everything about everything. I have this question about my business. Here are five documents. But you can read. Right now.

8:48 And answer my question. It's like oh I can do that. I like that analogy, the intern with the PhD and everything. That's so much how it is, right? It's as it's as helpful and available as an intern. Yeah, but it's as knowledgeable as somebody with a PhD in everything. Yeah. And then like you said, my m the another analogy for that is like it's a store.

9:08 You have shelf space, which is kinda limited, but they do have a back and you can always get send the employee to the back And see if they can find it in the back for you, right? That's kinda like what you're saying. Put it in the database, they can go fetch the specific thing that you're asking for. Uh, because you know, you gave it access to the back. You gave it a badge that lets it go in there. Have you uploaded all of H like Have you figure first of all, I wanna know what your chat GPT looks like. I wanna know how you use it on a like I just want you just screen share, just like show me exactly what you do.

9:35 But also have you uploaded your entire life, like have you uploaded all of HubSpot to chat GPT where you could just ask it any question? Yeah, multiple times, right? Um so in what format? Tell me how you did that. Uh so I did so OpenAI has um called this uh it's called an embeddings algorithm that takes any piece of text, a document, an email, whatever it happens to be, and creates this kind of point in the high dimensional space um called you know called a vector embedding. And

10:01 You know, uh a point in high dimensional space. So in three-dimensional space, physical space that we know of, we think of points being in three dimensions, x, y, and z axis. Like, oh, here's where this point is in space. High dimensional space. You can have a hundred dimensions, you can have a thousand dimensions, and describe each document as this kind of point in space. So What I've done so It used to be, um in the early

10:21 kind of GPT world, the number of dimensions you had access to was roughly like 100 to 200 dimensions. And so we would lose a lot of the meaning of a document, right? They would sort of get it right. It was sort of capture the meaning Uh and then uh Then we went to like a thousand dimensions. It's like oh well now we can much more accurately sort of represent um and and capture um a a document of kinda arbitrary length and and be able to find it. uh give it a prompt or give it some sort of search query.

10:45 Uh and then recently within the last year, we've gone the the latest algorithm uh from OpenAI Embeddings algorithm is like three thousand and Seventy two, I think, uh dimensions. But but where where do you do this? Do you just literally upload it as a project or you d you had to do an uh API connection? What what did you how do you actually do this? I'm doing an API connection, right? In fact I'm running the m least let me see where it is now. And anyone could do this, or you have special access because your friends. No, anyone can do this. The uh the uh uh the API for um the embeddings model they have two versions. They have the

11:15 three thousand dimension version, they have a one thousand dimension version. And is the results of this like are you driving a NASCAR and I'm driving like a scooter? Like is that the difference? Like if I just like for example Uh what I will do is I'll just like Download my company's financials and I'll upload it and then I'll like explain what my company does. But the way that you do it is a lot different. Now are we talking a massive gap in results that you get versus what I get?

11:39 Um Yes. Uh and and the reason is like so I do I do that as well in terms of I'll describe a company or whatever, I try to provide it context, and that's why it's called the context windows. You try to provide the LM uh context for what it you're asking it to do. Um

11:57 Yeah, the difference is that You know, because I can go through like and by the way, the richest and I'm working on a kind of nice and weekends project right now, um that takes uh email uh which you know So you'd be

12:10 Amazed. Like if you had to write no other words right now, if you did nothing but say I'm gonna take all of my emails I've ever written. Uh that are still stored. and give it uh to a vector store, use the embeddings algorithm, and then use Chad GPT. to let me kinda answer questions. So if I wanna say oh I want you to give me a a timeline for when we start first started using Hub.

12:29 to name products or whatever, and how'd that come about? Or what were the winning arguments against doing that versus whatever Like It's shocking how good The responses are when you give it access to that kind of rich data, right? Somebody needs to create just like a ten dollars a month s a single website that's like, Hey, make your chat GPT smarter. It's a website where it's like connect your Gmail, connect your Slack, connect your everything. And I would pay them happily twenty bucks a month to just

12:56 Set this up for me so that my chat to to give my chat GPT like The ekstra pill That says you now have access to my data. Is this'cause'cause you're talking about like I have the API to the vector embeddings and like Well I have the flux capacitor too, but I don't know what to do with it, right? Like I need a button on a website with a stripe payment button that I could just connect the stuff.

13:18 Uh there's I mean, there are uh tools out there to do and there's startups working on it, right? Uh there's two pieces of good news. One is there are startups working on it. The challenge here Is uh not that they're doing a you know, bad job. The challenge actually comes down to Uh if you were a startup and a startup came to you it's like oh we just started last last week. But we've got this thing it it really works. Uh in fact our mention maybe uh investor

13:41 How willing would you be to hand over literally your entire life and everything that's in your email over to this startup? Like so part of the challenge we have. is that the access control that let's say you're using Gmail, which uh a lot of us use. When you provide the keys to your Gmail account to a third party uh there is no degree of real granularity. You can say, Oh, I wanted to read m the metadata. That's like level one. Level two access, I wanted to read my full email, and level three is I want to be able to write and delete emails on my behalf. But if you wanted to like read like the actual body of the email, you can't say I only wanted to read

14:14 messages that are from HopSpot.com or I only I wanna ignore all messages from my wife and my family or whatever in the thing. There's no way to control that, right? So you sort of have to have trust. Is there any product that you would trust right now? Or that you can recommend that guys like Sean and I should use as chat GPT add ons or accelerators. No, not I n not that I don't trust them. But it's like I wouldn't trust

14:37 Really anyone right now with that. And it's one of the reasons I sort of run it locally, even though I know these things are out there. Um I predict what's going to happen is we're going to have uh any of the major players. And you can see this happening already, right? We see this with um No, you have the ability to create custom GPTs and open AI and do projects in Claude, you have Google Gems which Are essentially like a small baby version of this, right? That says, Oh, you can upload ten documents, a hundred documents, and it'll let you ask questions uh against the what it's really doing behind the scenes is creating a vector store. That's effectively what it's what's happening.

15:08 Um my expectation is all the major companies, um will actually have a variation of this, uh, starting with Google should be the first one because they already have the data. There is absolutely zero reason why dual Gemini Does that let you have a Q and A With your own email account.

15:26 That's just like Insanely stupid, right? Like I'll just go ahead and say it. It's just it's just there's something not right with the world. Uh, when they already have the data and it's like and they have the algorithm. They have Gemini 2.5 Pro, which is an exceptionally good model, right? So there you have all the pieces. Uh but y have not yet delivered, but I hope it's not. We're early adopters, but neither of us are technical.

15:48 What can we do to I want to get it on this, baby. All right. So give me give me two weeks. Here's here's one week. So the one thing I do trust, and I trust myself. Um I'm I'm an honest guy. Uh I'll give you like this internal app that I'm building. Let you put your Gmail to it. It'll go and it'll run for a day or two days or something like that. And then you will be amazed. You will be able to ask questions. Um and by the way, like

16:11 And the thing I'm like working on now is once you have this This capability, right? Like step one is just being able to do Q and A, right? It's like oh just I I'm gonna Step two, like imagine kind of fast forwarding, like it has access to all of your kind of history. So imagine you're able to say, you know what? I I'm not doing this, by the way, but if I were it's like I want to write a book.

16:29 about HubSpot and all the lessons learned and all like everything. It's all in my email. Do the best possible job you can writing a book. If you have questions along the way, ask me. Other than that, write the book. I think you'll be able to write the book. Wow.

16:41 What what else are you doing with AI? So uh w give me your day to day I like, for example, the CEO of Microsoft had this great thing where he goes I think with AI then I work with my co workers and that really shifted the way I work'cause I used to brainstorm or have a meeting to talk about stuff like coworkers, which was honestly always Like a little disappointing. I felt like I'm the one bringing the energy and the ideas and the questions and I'm hoping that they're gonna But dude, just sparring with AI first and then taking the

17:08 Kind of like distilled thoughts to my team of like here's how we're gonna execute. Has been way better. Like that little s one sentence he said. Shifted the way I was doing it. W how were you kind of using this stuff? Yeah, so a a couple of things. Um so I'll let let's start at the high level and we'll drill in a little bit. So

17:24 Uh what we're used to with uh chat GPT, this is sort of your kind of a kind of early evolution of most people's use. is because it's called generative AI, you use it to generate things, right? Uh generate a blog post, generate an image, generate A video generate audio, all those things. That's kind of the generation kind of aspect. Uh and that's Part of what it's good at.

17:42 Then you sort of get into the oh, but it can also kinda summarize and synthesize things for me. It's like, oh, take this large body of text, take this blog post, take this academic paper, and summarize it in this way, or like so a seven year old would understand it kind of thing, right? So that's the Kind of step number um step number two. Step number three, and we're gonna get into how this is now possible. Um is you can do um effectively you can take action. Um have the LM actually do things for you.

18:07 Uh and I've always kind of put it broadly in the kind of automation bucket. Like I can automate things that I was doing manually before. And then the fourth thing is around orchestration. Like can I just have it Меж

18:18 A a set of AI agents and we'll talk about agents a little bit. and just do it all for me. I just want to give it a super high order goal. It has access to an army of agents that are good at varying different things. I don't want to know about any of that. I just wanted to go do this thing for me, right? And and that's sort of where we are on the slope of the curve. Uh the first three things are possible today.

18:37 And work well today, right? So you can generate as we know, it can generate blog posts, it can write really well, uh it can generate great images now. Including images with text. They can do great video now with Uh, you know, higher fidelity, higher character cohesion, all these things. Uh Sean, so the thing the the vision you had three years ago when I was on was around creating the next Disney, the next kind of media company. You have the tools now, my friend, to to finally start to approach that, right? But then you should sort of move into

19:01 And this is what we were just talking about, this kind of synthesis and analysis thing that says okay, this is where deep research kinds of features come in. It's like, okay, well, I want you to take the entire of the internet or entirety of what uh Sean has written about copywriting. And I want you to write a book just for me. That summarizes all of that in ways I enjoy because I like I like analogies and I like jokes and I like this and I like that. Write a custom version of Sean Puri's book on copyright, right? That kind of synthesis.

19:23 Um I think would be uh super interesting. And then automation. is now possible. So agent.ai is one of those things. There's other yeah tools out there that says hey I wanna take this workflow or this thing that I do and I want you just do it for me. Give us a specific what's a specific specific automation that your view views that's like, you know, useful, helpful, saves you time. I'll I'll tell you a couple. One is around domain names. Which is okay, so I have an idea for a domain name.

19:46 Um and I'm gonna Type words in. And these things exist and I'll tell you the the manual flow that I used to go to is like okay, first of all I can brainstorm myself and come up with possible words and very simple words wherever here's the things. Then I'll say, Okay, which domains are available? Absolutely zero of them uh that are good that will uh pop into my mind are like freely available to kinda just register that no one's registered before. Okay, fine. Then I'll say, Okay, well, which ones are available for sale?

20:09 Okay, what's the price tag? Is that a fair approximation of the value? Is it like below market, above market? We don't know because there's no zillo for domain names yet. Uh so create that. So I have something that automates all of that. And says oh

20:22 So you have this particular idea for this concept for this business, business, whatever it is. Uh here are names, here are the actual price points. Here's the ones that I think are below market value, above market value. Tell me which ones you want to register. That's in chat GPT. No, it's an agent.ai is where it lives right now, but now there's a connector between Agent.ai and chat GPT through this thing called MCP, which you'll hear about.

20:43 Uh a bunch of if you haven't already. Um One thing I I wanna kinda get out there, just we keep connecting the dots. Um Say what I want everyone to have this framework in their head. Uh so we talked about large language models, it can generate things, we talk about the context window. We talked about faking out the context window by saying, Oh, we can do this vector database and bring in the right five documents, stuff them into the context window.

21:04 Uh here's the other big breakthrough that's happened uh I'll say recently within the last year, year and a half. Is what's called tool calling. And what tool calling is is a really brilliant idea. And the tool calling says, okay, well, the LM was training a certain number of things. But if we had this intern that came in

21:21 It would be like saying, Okay, well, whatever you know, you know, but we're not gonna give you access to the internet. Like that would be stupid, right? We would give the intern access to the internet. It's like if I ask you something that you were trained on, go look it up, right? That that would be like Like thing number one on the first day of work. And as it turns out, the LM world The internal couldn't.

21:38 didn't have access to the internet. All it had was whatever notes it happened to take during its PhD training and all things, right? And so what tool calling allows, and this is a weird um weird approach to it, but this is because of the way LMs work. So remember the the L M It's architected such that you give it the context window in It spits things out. That's it.

21:56 It doesn't have and you can't reprogram the architecture's now all of a sudden we're gonna give you access to tool calling. So here's the hack that they came up with. They said, Okay In the instructions that we give it in the context window. We're going to say You have access to these four tools.

22:11 And it doesn't actually have access to the four tools. It's that I want you to pretend. Like you hacks have access to these four tools. The first tool is this thing called the internet. And the way the internet works is you type in the query and it will give you some things back. You have this other thing called a calculator. And you can give it a mathematical expression and it gives you an answer back. And you have this other tool that lets you do this, and you can have N number of tools.

22:33 And so here's what happens. In the context window happening behind the scenes Chat GPT, which is a the interface right now that is interacting with the LA. You're not clocking with the L M directly, right? It gets a prop and it says okay, by the way, LLM.

22:48 I want you to pretend like you have access to these four tools. When you pass the note back to me. the results, the output, just tell me when you want to use one of those tools. Alright, so we give it a query, it's like okay, well I wanna look up

23:02 Like the historical stock valuation for HubSpot and when it changed as a result of is there any correlation to the weather? Is it seasonal or whatever it is, right? Uh, in terms of market cap of uh HubSpot versus um um season changes. All right, well that's not something we would have access to, but here's what actually happens. This is so cool, right? So the L M gets it.

23:20 And the LMs in the context window that we gave it, we gave it instructions as to pretend like you have these four tools. One of which is stock price look up, let's say, historical stock price look up. It'll pass the output back to the application, not us. And say And it

23:33 In the in the output it says, Oh, please invoke that tool you told me I had access to and look up this result. I want you to search the internet for X, what was the weather, I want you to do this for the stock price. And then we do that. We the Chat GPT application. Fill the context window with whatever it is the LM asked for, and then pass it back in. So the L M effectively has access to those tools, even though it never accessed the internet. It never accessed the stock market.

23:59 But it pretended like it had access to it. And we never see this. This is happening behind the scenes. Now here is the big massive unlock, right, which is Well, everything can be a tool, right? Now you don't have to build this kind of vector store or whatever, because you would never build a vector store of all possible stock prices from the dawn of time. Now I guess you could, but then it's outdated immediately. Now it's like

24:20 What if we just gave it twenty really powerful tools, including browser access to the internet? Well that's like a ten thousand, hundred thousand times increase in that intern's capability, right? And so that's where our brains should be headed now, which was exactly where the world is headed, that says What tools can we give the LM access to?

24:39 That will amplify his ability and cause Zero change the actual architecture. Literally, it doesn't have to know anything about anything. It's like I just want you to pretend. That you have access to these tools. Uh he doesn't need to know how to talk to those tools, doesn't need to know about API, doesn't need any of that stuff. Cutting your sales cycle in half sounds pretty impossible, but that's exactly what Sandler training did with HubSpot.

24:59 They use Breeze, HubSpot's AI tools, to tailor every customer interaction without losing their personal touch. And the results were incredible. Click through rates jumped twenty five percent. Qualified leads quadruple. And people spent three times longer on their landing pages. Go to hubspot.com to see how breeze can help your business grow. Do you think that

25:24 I mean, this is all but mind blowing and you have an interesting perspective because You know, I think three episodes ago that you're on, you created this thing called Wordle. Was it Wordle? Word play.

25:35 That does like eighty grand a month. It was just like a puzzle that you do at your son. It was amazing. Um, but now you have new projects. You have agent AI, you have a few other things. But you still run a thirty billion dollar company. Do you think that the majority of value creation

25:52 Like, am I gonna is my stock portfolio going to go up because I own a basket of tech stocks? Or is the best way to capitalize as an outsider, obviously you start a company. Or is it investing in new startups that are using AI or AI first startups? It's a yeah, it's a good question. I'm neither an economist nor a a stock analyst. But I will say this. The thing I'm most excited about with uh with AI. And I actually said exactly this in a talk I gave um well before GPT.

26:23 on the inbound stage and I said You know, as AI is starting to kinda come up, it's not a you versus AI. That's not the mental model you should have in here. It's like, Oh, well, I didn't take my job because it's me trying to do things that the AI is then eventually going to be able to do. uh the right mental frame of reference you should have it's you to the power of AI. AI is an amplifier.

26:42 of your capability. It will unlock things and let you do things that you were never able to do before, as a result of which it's going to increase your value, not decrease it. Right. But in order for that to be true. You actually have to use it. You have to learn it. You have to experiment with it. And the only real way to get a feel for what it can and can't do.

27:01 is you have to do it. So I'll give you the very, very simple everyone should do this. I do this, uh personally. Is that Anytime you're going to sit down at a computer And do something. Research, whatever he's gonna do.

27:14 You should give Chat GPT or your AI tool of choice A shot at it. Try to describe and pretend like you have access. To this intern that has a PhD in everything. It's like okay, well maybe it doesn't know anything about me or whatever, fine. So then tell it a few things about you.

27:28 But imagine you have access to this all knowing intern that has a PhD and everything. Give it a crack in solving the problem that you're about to sit down and spend some time on. And what you will invariably find Number one is you'll be surprised by the number of times it actually comes up with a helpful response that you would never have expected would be. even remotely able to do, like how can it do that?

27:46 It's because it has a PhD in everything, right? And so now actually we'll talk about reasoning and whether models are actually doing that or not if we have time. But um so that's my advice is Every day. Every day. Um You should be in chat GPT. If you're a knowledge worker at all, it doesn't matter actually, you don't even have to be a knowledge worker. I don't care what your job is, right? You could be a sommelier restaurant.

28:08 And you should be using chat GPT every day um for to make yourself better. Um at whatever it is you do. And that might be the introduction of that orthogonal skill to bring it back to the Which I never explained the word fabulous. I'll do in thirty seconds. So Uh orthogonal means a line that's ninety degree intersection to another line. Uh and the most common use

28:25 is when we have an X and Y axis. Right? It was like oh the X axis. And the y axis are orthogonal to each other because they have ninety degrees separating them. The common usage when you say oh that's an orphanal concept It means it's unrelated. It's completely different. That's like the y and x axis are completely independent of each other. You can say, Oh, you can be here on the x axis.

28:42 But here are the y axis and they're not related to each other. So that's what I mean when I say orthogonal concepts or skills or ideas. Um yeah, anyway. Is there anything you disagree with that's kind of the consensus? Cause a lot of things you're talking about, like, hey, AI is gonna change everything. It's super smart. Agents are coming, they can do some stuff now, more stuff later. These are all

29:01 Probably right. But they're also consensus. I'm just curious, like, is there anything you disagree with that you hear out there that drives you nuts where you're just like how people keep saying this? I think that's either wrong, it's overrated, it's the wrong timeline, it's the wrong frame, it's uh Whatever. Is there anything that you disagree with that you've heard out there? I've heard variations uh two v variations I disagree with. One that I've I think spent still amount of time

29:24 Uh hopefully kinda Talking folks out of which is It's just auto correct, it's not really thinking. Um and And that's a matter of like what do you think thinking is, right? It's like okay, well if it produces The right output.

29:37 to which we think would r require thought. Um so I think that is it's it's flawed reasoning to say, oh well and this often comes from the the smartest people, the most experts in their field because oh, it's really like a stochastic period. You'll hear this phrase, which is it's like a probability driven pattern matching based it just so happens that's been trained on the internet, but it's not really Like human intelligence, and I agree with that. Phrasing, which is it's not like human intelligence, but that does not mean that all it's doing is sort of mimicking stochastically uh you know all the things I just read before because in order to do what it does.

30:09 It is a form of creativity, different from what we uh normally experience. That's kinda thing number one that I gonna uh disagree with. Thing number two is people are thinking I I both disagree with the oh the scaling laws are gonna continue forever indefinitely that The more and more compute we throw at the more knobs we put on the machine, the smarter, smarter it's gonna get. I think

30:27 There's going to be a limit to that at some point. It's like nothing goes on forever. It's going to asymptotically move towards we're gonna have to come up with new algorithms. GPT can't be the duand of all things, right? There will be a new way. Um Um yeah, discovered. So I think that's gonna happen. But I I think the smarter and I did us say this, uh other people have said it. The best way to kind of think about AI right now Is uh as you use it, it's the kind of

30:52 truly find find a frontier of what it's incapable of. It's like okay. It can sort of do this thing. But not very well. If if that's the way you describe its response, you are exactly where you need to be, which is it if it can sort of do it right now, sort of.

31:06 If you have to squint a little bit, it's like ah well it's it's kinda something, but Wait six months or a year, right? Like it's uh uh that's the beauty of an exponential curve. It gets so better so much better so fast. Uh that if it can sort of do it now, it will be able to do it and then it'll be able to do it really well. That's the inevitable Sequence of events that's gonna be.

31:26 kinda the smart money in startups believes that the the right starter to build is basically the thing that AI Kinda can't do right now. That's the company to start today. Because you just have to stay alive long enough, give it the the twelve to eighteen month runway that it needs for the thing to go from Uh, didn't really work very well to like, Oh my god, this is amazing, but you've built your brand, your company, your mission, you've your customer bas you've been building that all along the way, and you're basically just betting you're gonna be able to surf the improvement of the model. By the way, that's

31:56 That's how I feel about my company. My company is not related to us, but at all. But in terms of like our operations, we're like things are very manual. And I'm like, Oh my God, once I'm able to finally implement AI when it it can work for this purpose, my profit margin's gonna go through the roof. I mean, that that's how I that's how I feel about it. But it it which isn't entirely related to that, Sean, but it a a little bit. Uh one one one thing I'll I'll plant out there since uh this is my first million we we like talking about ideas. Uh at a macro level, here's a the entirely new pool of ideas that I think are now available um on a trend that I think is inevitable, which is As agents get better and better, right? Um right now, most of us when we use uh use AI, use chat TPD, uh, we use them as tools.

32:37 Which is great. Perfect. Uh fine. Uh Over time need to shift your thinking and think of them as teammates. Think of them as that intern that just got hired.

32:46 Right. Uh and And as a result of that, so let's let's Assume for a second, let's stipulate that I I I'm right. All we don't know is How long is it gonna take for me to be right? Is that we're going to have effectively digital teammates that are part of all of our teams. Every company is going to someday have a hybrid team consisting of carbon based life forms.

33:05 And these kind of digital AI, um AI agents. Okay. So if you uh accept that The way that's going to happen is not going to be like all of a sudden we one day wake up and every organization now starts kind of mixing them. What's going to happen is it's going to slowly introduce its way, oh I have this one task, whatever that an agent is better at is rel it's reliable enough for the thing and the risk is low enough, I'm gonna have it do that. Right, but we already see elements of that. But here's what's going to happen as a result of that kind of gradual kind of infusion and adoption of that technology.

33:32 uh, the way to win and the opportunities that get created is like, how do I help the world accomplish this end state that I know is going to come. So here I'll give you some examples. Uh if we were to hire if you uh Sam were to hire a new employee Uh tomorrow. Here's what you would do. You would say, Oh

33:49 Well, I'm going to onboard that employee. Spend a couple of days. I'm gonna tell them about the business. Uh, whoever's managing that employee that's say with a direct report of yours, maybe you'll have a weekly one on one or every other week or whatever. That one on one will consist of uh looking at the work they did, whatever's like, Oh over here you did this or whatever, and it could be copy editing, it could be anything, whatever the rule happens to be, you're gonna give them feedback. Right. That's what you would do for a human worker.

34:12 All of those things have a direct, literally a direct analog In the agent world. Right. And what we're doing right now is we're hiring these agents and expecting them to do magic. Just like if we hired an exceptionally smart uh has a PhD in everything employee and expected them to do magic with no training, no onboarding, no feedback, no one on one, no nothing.

34:32 Well, your results are not going to vary. They're gonna be crap, uh, because you do not make the investment In getting that agent. Now the the the big unlock here, so whether you're an HR person or whatever, it's like figure out Well, what does employee training look like for digital workers? What do performance reviews look like for digital workers? How do we do the how do we do recruiting?

34:50 For digital workers. How do we like what are all the mechanisms that need to exist? What is a manager of the future, what are the new roles that will be created as a result of having these hybrid teams? It's like okay, well now Maybe we're gonna need someone that's like the ecentic manager. Human. That knows all the agents that that are on their team or whatever and has kind of built the skill set to how to do recruiting for their team, uh how to do performance reviews, how to do all of that, but for agents or hybrid teams, um yeah, versus just purely human ones. Uh that

35:16 That's just a whole other and we're gonna need the software, we're gonna need the onboarding, we're gonna need training, we're gonna need books written, we're gonna need all of it. to kinda adopt and it's gonna take Uh it's gonna take years, right? It's not uh night. Two years ago I asked you Is it gonna be as bad or I think you said

35:33 I asked, Is it gonna be horrible or is this gonna be amazing? And you said Uh the I saw this with the internet. Nothing is as extreme as the most extreme predictions. I listened to you and I trusted you then I actually think knowing what I know now, I'm actually more fearful, uh than I was a couple of years ago, where I'm like, Oh, this is actually gonna put a lot of people out of work and Um, it's maybe not good or bad, but things are gonna change drastically more than I thought.

35:59 And my So I don't remember how I phrase the question, but Is this going to change the future? More than you thought two years ago or less than you thought two years ago.

36:09 Um, has your opinion on that changed? I still think they're gonna be unrecognizable. My my Um macro level sense, and this is maybe just my inherent uh optimism about things, is that It's going to be kind of a net.

36:23 positive for humanity. And this is the other thing that um you know lots of people would disagree with me on. This like, oh well, is this an existential crisis Yeah, to the species, um And I I've not said this before, but I'm gonna see how it sounds uh as the words leave my mouth. I'm probably gonna regret it, but In a way we are actually, and and Sam um Sean, you said this earlier.

36:43 We're sort of producing a new species, right? So that's like saying, Okay, well, homeosapiens as they exist, absent AI. is likely not going to exist. So the way we know the species as it exists today with where we have a single brain And in in in natural form, you know, four appendages or whatever, maybe that's going to be different. Uh But I think of that as an extension of humanity, not the obliteration of humanity, right? That's the that's

37:04 Yeah, human two point oh or N point O uh of the way we kinda think of the species right now. So I'm Uh I think things are still moving very, very fast and this is the This is why I think humans have uh issues with exponential curves. We're just not used to them. When something is kind of doubling or uh, you know, um every N months. It's hard to wrap our brains around how fast this stuff uh

37:25 Yeah, can move things that we thought were Like the things we have today, Sam, um If we had just described them to someone. A year and a half ago. There's like ah

37:35 Well, chat CPD is cool, whatever, but it's never gonna be able to do that. And now we're like Those are like par for the course, right? Like we we we we can do like um things that were literally like oh there's no way. No way. It's like, yeah, it's good at like text and stuff like that, but that's because it's been trained on text. Now I can do images. Well I can do images.

37:51 But like video is like thirty frames a second, that's like th generating thirty images per frame of like a per second of video. All of that. It's like yeah, but you know, diffusion models, the way they work is because you're not gonna get you get a different image every time. So how are you gonna create a video because it requires the same character, the same setting in subsequent frames? That's not how the thing is arched. That's not how image models work. And we solved all of those things, right? Now we have character cohesion, setting cohesion, video generational anyway. So my answer is It's exactly not exactly, but it's close to like

38:22 Yep, this is what exponential advancement looks like. Uh I'm still of the belief that we're gonna have more net positive That is not to say that in the interim there's not going to be pain. Um and There's two things I'll put out there as cautionary, uh cautionary words. One is In the interim, um Anyone that tells you that there's not going to be job dislocation, they're not gonna be roles that get completely obliterated.

38:43 is lying to you. That is going to happen. It's already happening, right? It's um that there is no world in which that does not uh occur. That's kind of thing number one. Thing number two, and we didn't talk about this, uh, but we should have. Um Is that

38:55 Because of the architecture of how LM's currently work, maybe they'll figure out a way to do that. Uh, they produce hallucinations. And that's just a fancy way of saying it makes things up. Right. And That's sorta okay.

39:08 But not okay because it doesn't know it's making it up. Because of the way the architecture works, it's like the intern. That thinks it's been exposed to all there is to know in the world. It's like I know all the things. Do you ask me a question, I know I know all the things. So I'm gonna tell you the thing that I know. It was like, Well, yeah, but you didn't know this and you what you said is actually factually Like provably, demonstrably wrong. And it has as

39:28 Apply zero lack of confidence in its output. Um which is fine for some things if you're writing a short Yeah, fiction story or something like that. It's not great. at all for other things like healthcare related where you need uh kind of predictable, accurate responses. So I think we need to be aware of the limitations around it when we're doing research and things like that.

39:46 Uh and the problem is when we have Relatively I'll say naive, I don't mean this in a disparaging way. Uh Folks that are naive to a subject area asking ChatGPT for things where it can't judge the response, right? We're just sort of taking it.

40:00 On faith. That it's Chat GPT and our mesh status got a PhD and everything, so of course it's gonna be right. Well, no, it's often not right. Uh and it's kinda up to us to figure out. what our kind of risk tolerance is. It's like what is it okay for it to be wrong? Uh how would I test it? Uh for my domain for my particular use cases, um yeah, so

40:20 So you guys know this, but I have a company called Hampton, joinham.com. It's a vetted community for founders and CEOs. Well, we have this member named Lavon, and Lavon saw a bunch of members talking about the same problem within Hampton, which is that they spent hours manually moving data into a PDF. It's tedious, it's annoying, and it's a waste of time. And so Lavon, like any great entrepreneur, he built a solution. And that solution is called Molu. Mokul uses AI to automatically transfer data from any document into a PDF. And so if you need to turn a supplier invoice into a customer quote or move info from an application into a contract, you just put a file into Moku and it auto fills the output PDF in seconds. And a little backstory for all the tech nerds out there. Slavon built the entire web app without using a line of code. He used something called bubble I. They've added AI tools that can generate an entire app from one prompt. It's pretty amazing, and it means you can build tools like Mulku.

41:12 Very fast without knowing how to code. And so if you're tired of copying and pasting between documents or paying people to do that for you, check out Molku.ai. M-O-L-K-U- Dot AI. All right, back to the pod. What do you think about this situation where Zuck is Mm.

41:29 throw in the bag at every researcher. And he's poaching Basically his own dream team. He's like, Okay, you're not gonna I can't acquire the company Well why don't I go get all the players? If you can keep the team, I'll keep the players. And he's going after them with these crazy Nine figure offers. A hundred million signing bonus and three hundred million over four years, I think is what I saw. Is that true? I think that was like the higher yeah, this is the higher end. And some people have said there's even like billion dollar offers to certain people that are out there. This is

42:01 Like job offers. So Darmas like were you shocked by this?'Cause I mean My reaction to this was That's bullshit. First time I heard it, then I was like, Wait, the source is Sam Altman, why would he say that?

42:11 And then I was like, Okay, that's insane. And then a an hour later I was like, Wait, that's actually genius,'cause for a total of three billion or something, he can acquire The equivalent of one of these labs that's valued at thirty, forty, fifty, or two hundred billion dollars. What a power play. I know obviously you're investor in OpenAI, so you know, maybe you don't like this, maybe you have a d different uh bias here, but I'm just

42:32 From one kind of like leader of a tech company to an to another. Like what's your view of this move? I think it's it's one of the crazier moves. If I had to use one word, I would say diabolical. Uh not stupid, not silly, but diabolical. And here's why, right? This is the Like in the grand scheme of things, so this is not just a, oh, can we use this technology and build a better product that will then drive X billion dollars of revenue through whatever business model we happen to have. There's a meta thing at play here that says whoever gets to this first

43:01 We'll be able to produce companies uh with billions of dollars of revenue or whatever, right? Because that's it's like kind of finding the the secrets of the universe and the mystery of life kind of thing. It's like okay, well whoever wins that And gets there first. Will then be able to use the technology. Internally for a little while.

43:17 And be able to just kinda run the table uh for as long as they want. So there's it's got incalculable value, right? The upside is just so high. That uh no amount of like if you can increase your probability even by a marginal amount If you had the cash, why wouldn't you do it, right? So

43:32 Do you think A, do you think it'll work? Do you think this tactic will work for him? Do you think he will be able to build a super team? Is he just gonna get a bunch of engineers who now have yachts and don't work? Like what's gonna happen when you give somebody hundred million dollars offers. You s you put together this smash together this team of I think he's got a hit list of fifty targets. And I think like, you know, something like nineteen or twenty of them have Come on board already.

43:54 Uh what's your prediction of how this plays out? It feels a little bit like a Hail Mary pass, right? That's okay, they're gonna take this it's like okay, well there's not a whole lot of things we can do. Yeah, the the chips are down. I'm gonna make some metaphors now too. Um but But but that that works sometimes. It works sometimes. See th that's exactly why people do it. Like everything else hasn't worked yet, so

44:14 Let's try this thing. Um But the I think the challenge, um I still think it's a dialogueally smart move, uh we're not I'm not gonna use the word ethics or anything like that. But here's the challenge though, right? If you if we were having this conversation We'll call it uh two years ago, give or take. Um Open AI was so far ahead in terms of the underlying algorithm, uh, and this is even before ChatGPT hit the kind of revenue curve that it's hit. Just just raw the GP GPT algorithm, which is so good, they were so far ahead.

44:40 Uh, it was actually inconceivable for uh folks, including me. that others would catch up. It's like okay, well They'll make progress, they'll get closer, but then open AI is obviously gonna still keep working on and they're gonna be far ahead for a long, long time. That's proven not to be true, right? We've seen open source models come out. We've seen other commercial models come out. There's anthropic out and they have By most measures, comparable large language models, right? Within like one standard deviation, they're they're pretty good. And sometimes they're better at some things worse than others, but it's not this.

45:08 Single horse race anymore. So the thing that I'm a little bit dubious of is that even if you did this, you put all these people together. Like it didn't really work for open AI in the true sense of the word, right? Like they weren't able to create this kind of magical thing that It's like okay, maybe they end up doing it um you know somewhere else, but I think there's More Smart people out there. The technology is kind of deep seek proved that you could actually and they did actually have an actual um

45:32 Innovation in terms of reasoning models and things like that versus kind of the early generation uh large language models. So Jury's still out. How much better is a three hundred million dollar over so a hundred million dollar a year engineer.

45:46 over like a twenty million dollar engineer. Is it like Well, I I followed some of these guys on Twitter and it was they're fantastic follows. And do you think that their IQ is just so much better or is it because they've had experien is it really because they just saw how uh Open AI works and they want that experience. Are they like is this like espionage? What is

46:06 How good could a hundred million or a three hundred million dollar a year engineer be? Well that's the thing though. This is software, right? So this is a you know a world of like Ninety five percent margin. So let's say Yeah, I think part of the value is yes, they're super smart, but uh even hi human IQ it asymptotically moves towards a a certain ceiling, right? You take the smartest people in the world, however you want to measure IQ. Um and so that doesn't explain away the value, right? That's not that.

46:31 Uh, it's not that they've seen the inside of open AI and they have some trade secrets in their head that they can then kinda carry over. It's like, oh, here's how we did it over there and here's how we ran evals and here's how we did you know the engineering process. They'll have some of that because we always carry some amount of uh kinda experience uh in our heads. I think the larger thing, I think um kind of primary kind of vector of value Is they sort of

46:53 have demonstrated the ability to kind of see around corners and see into the future, right? They believed in this thing. that almost no one believed in at the time. they sort of saw where it was headed and they were working at it, shipping away at it, whatever. And that's much rarer than you would think for really smart people to do this stupidly foolish, seemingly stupid, foolish thing. It's like you're gonna do what now? Right? And we're still asking ourselves a variation of that question that we would have asked three years ago. Except now we have Cat GPT and we have the things that it and we're still talking.

47:21 Well, you say that we're gonna have like these kind of digital teammates and they're gonna be able to do all these things and it can't even do this simple thing right, right? Like we sort of keep elevating our expectations and what we believe is or is not possible. They sort of know what's possible and they almost think of What many of us would consider impossible is actually being inevitable. Has HubSpot have you made any of these offers? I don't think so. But that's that's not the game we're in, right? So we're not We're not in that league. We're not trying to build a frontier model. We're not trying to invent AGI. We're at the application layer of the stack. So we wanna

47:50 benefit from it, right? Um you know, we didn't in any uh layer of my Austrial career. I have not been The guy in the center of the universe or the company that's not you're not like, Oh man, I met this person, like we need to offer like an NBA contract in order to secure this this guy. No, it and there's a reason for this, right? It's like for the kinds of problems we're solving. What's the there's a sports term about the uh best ultra to the player or something like that that a replacement costs? Or wins above replacement is the metric they use in sports.

48:18 So yeah, it's just not it's not worth it given our business model and given what we do. I have one last thing on the kind of AI front. This is one of the things um Answering your question, Sean, in terms of things I disagree with folks on Is that there's um Yeah, uh

48:33 group of people, um, very smart that will say, Oh well AI is going to lead to a uh reduction in creativity, broadly speaking, right? Because you're just gonna have AI do the thing. Why do you need to learn to do the thing? And I have a fourteen year old, right? So it's like okay, well if he just uses AI to write his essays and do his homework or whatever, um, it's gonna r you kinda reduce this creativity. I understand that particular kind of line of reasoning that says, Yeah, if you just have it do the thing, um, you're not going to Uh but

48:57 I think the the part F those folks are missing is that Uh Yeah, creativity is Kind of in the literal sense the word is like okay, I have this

49:06 a kind of thing, idea in my head, and I'm going to express it in some creative form, be it music, be it art, be it whatever it happens to be. Um and the problem right now is that um Whatever creative ideas we have in our head are limited in terms of how we can manifest them based on our uh emerging skill set.

49:25 So Sean can have a song in his head right now that like he may be composing things in his head. But until he learns the mechanics of how to actually play an instrument, whatever the instrument happens to be, there's no real way to manifest that, right? We don't we can't Tap into his brain and do that. Um

49:39 So in my mind, AI actually increases creativity because it will increase the percentage of ideas that people have in their heads that they will then be able to manifest regardless of what their skills uh are or or not. Uh I I love that. So my son, uh he's a big Japanese culture fan, uh big manga fan, uh And Japanese comic books and and anime. Um and so He w he's an aspiring uh

50:01 you know, author someday. And what he can do now, right, and he's been able to do this for years, uh, which is So he's always had again, he likes fantasy fiction as well. So he's had these ideas for writing things, but he lacked the writing skills. He doesn't know about character development, doesn't know about any of these things. So what he uses Chad GPT for is he's got this like two thousand word prompt that describes his fictional world. Here are the characters, here's a power structure, here the powers people have, here's what you can and can't do. And then the way he tests the world. is he turns into a role playing game.

50:29 It's like okay. I'm gonna jump into the world. Now you chat GPT, I'm gonna do this. Tell me what happens. Oh, this happened. Okay, now I'm gonna do this. Okay, well now you've got this power. Like and so it will sort of kind of pressure test kind of his world. And so that's an expression of his creativity. 'Cause the world was sitting in his head, but now he can actually share that with friends.

50:46 maybe turn that into a book someday because it's gonna take the ideas that he has and hopefully in the meantime you will kinda develop some of those foundational skills but he doesn't have to wait. until like twelve years of writing education before you can take this idea as a child. He has lots of creativity, uh, but as a practitioner, most of those things that he would love to be able to manifest in the world. He has nothing uh close to the skills required, whether it's drawing or uh writing or anything. So I think that's what

51:11 AI can help us kinda elevate um what And once again, but we have to use it responsibly, uh, but it should be able to elevate our skills. I wanna show you guys a Uh Example of this real quick. So

51:23 I had this idea not long ago, a couple of weeks ago. Of um Creating a game. Using only AI. So Um have you d I don't know if you guys ever played the Monkey Island games.

51:34 From like when I was a kid, I played Monkey Island. It was a Incredible game. This guy basically this guy wants to be a pirate. It's like this very funny, but like eight bit art style game. And so I created A version of that called Escape from Silicon Valley. I didn't create the whole game, but I create like the art, but like check this out. So

51:50 So I go into I go into AI and I basically start creating the game art. And so it's like the story is basically like deep in San Francisco, the year is twenty forty-eight. The block is starting his third term in office. Uh, you know, Nancy Pelosi passes away, the richest woman on earth. And then you know, Elon is promising that self driving cars are coming really, really soon for sh for real this time. And here you are, you're this character, and you're in um the open AI office. And basically the idea is Charlie, look at that. What's that?

52:19 Look at a Charlie bar. Yeah, yeah, exactly. I was I was putting in some references to like you know stuff that I thought was um it would be cool. That is so cool. What did you use to make those images? So that right there was just chat GPT and mid-journey um mix. I tried using, you know, Scenario and a couple of other like game specific tools like this out. So like I created all these like tech like characters. So it's like I create Zuck and Paul Marlucky and like Chamath and Elizabeth Holmes in jail. Awesome.

52:44 And I had it basically write the scenes for the levels with me, like write the dialogue with me, create the character art. Dude, that looks sick. Why didn't you do that? Um, well, because I did the fun part in the first two weeks where I was like, Oh, the concept. The levels.

52:59 The character art, the music, the seeing what AI could do. But then to actually make the game. I can't do that. And so I was like, Oh, now I need to like I mean people who build games and years building it. It's like oh this is like minimum six to twelve months doing this like very, very arbitrary project. But Um I still love the idea and I'm kinda like packaging up the whole the whole idea.

53:21 Dharmash last question. Um Just really quick, like you Where do you hang out on the internet? That

53:29 We and the listener can hang out. To stay on top of some of this stuff. Like are there like who's a reputable handful of people on Twitter to follow or reputable websites or places to to hang out at? That's interesting. So I spend most of my time um On YouTube, as it turns out. Um

53:46 And And I'd l I sort of give into the Given the vibe, so to speak, and let the algorithm sort of figure out what uh it thinks enjoy. Uh Gets it right sometimes, gets it wrong sometimes. Uh so it's a mix of things. But um The the f the person that I think uh if you want to kind of get deeper into like understanding uh AI, there's a guy named uh um Andre Karpathi. I don't know if you've come across him.

54:08 Uh just search for a car poffee. Dude, you don't want to know how I know like I get so many ads that says like Andre Carpathi said this is the best product or Andre Carpathi showed me how to do this. Now I'm gonna show you. Like I don't even know who Andre is other than ads run his name to promote him. Yeah. I mean he's yeah, w one of the to true OGs in in AI, but he has that His orthogonal skill or one of them. I think he's got like Nine you've probably like a nine tool player of some sort, but uh

54:34 He's able to really simplify complicated things. Without making you feel stupid. Right. So he's he's not talking down to you. He's like, Okay. Like here's how we're gonna do this. We're gonna kinda build it brick by brick and and you're gonna understand at the end of this hour and a half. How X works, right? Um and it's

54:50 So him, any other YouTubers or Twitter people or blogs. Aaron Levy from Box is actually very, very thoughtful on the Uh, if you're in software and in business and the AI implications there, I think he's really good. Uh Heat and Shaw, who you both know, uh now at Dropbox uh through the acquisition. has been on fire lately on LinkedIn. Uh, so he's one I would I would go back uh especially over the last like three, four months. uh and read all the stuff he's written, I think he's on point. Uh yeah, so

55:20 Those are awesome. Darmat, thanks for coming on. Thanks for teaching us. One of my favorite teachers, uh, and entertainers. So thank you for uh for coming on, man. My pleasure. It was good to see you guys. It was fun. Likewise, thank you. That's it, that's the pod. I feel like I can rule the world, I know I can be what I want to

55:39 I put my all in it like my day song. On the roadless travel, never looking back. Alright, my friends, I have a new podcast for you guys to check out. It's called Content Is Profit. And it's hosted by Luis and Fonzi Cameo. After years of building content teams and frameworks for companies like Red Bull and Orange Theory Fitness, Louise and Fonsey are on a mission to bridge the gap between content and revenue. In each episode, you're gonna hear from top entrepreneurs and creators, and you're gonna hear them share their secrets and strategies to turn their content into profit. So you can check out content is profit wherever you get your podcasts.

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