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Speaker 1: Will Lucas here, Black Tech, Green Money. Hey, I got

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Speaker 1: an episode for you guys. So this guy Renumber when

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Speaker 1: I've been on stages with Cliff or you know, in

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Speaker 1: some of the biggest conferences in the country, and he

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Speaker 1: spoke at AFRO Tech last year. Cliff Worley is an

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Speaker 1: AI expert and was until recently senior director of Portfolio

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Speaker 1: Growth Marketing at kp or Capital, and his newsletter, if

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Speaker 1: you are not a subscriber, is required reading for anybody

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Speaker 1: in tech, business, family, wealth management, you know, get out

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Speaker 1: of college faster, like, get in bid with better grades,

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Speaker 1: all the things. So Cliff Worley, welcome to the show.

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Speaker 2: Thank you, I appreciate it. We'll get it to.

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Speaker 1: You absolutely absolutely. So I want to start this off

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Speaker 1: by just kind of getting from you a sentiment on

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Speaker 1: how you feel about AI in general. So it's what

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Speaker 1: I mean, because there are some doomsday people and then

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Speaker 1: there are some people who are super hopeful. So I

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Speaker 1: just want to know generally how you feel about what's

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Speaker 1: going on in the world and the economy and job

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Speaker 1: opportunities in et cetera.

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Speaker 3: Yeah, yeah, I think it's a mixed bag. It's it's complicated.

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Speaker 3: The analogy I usually try to give people is that

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Speaker 3: there are some really dark places of the Internet, and

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Speaker 3: there's some really good spots, and the majority of us

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Speaker 3: focused on the good spots. Kind of the same thing

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Speaker 3: with AI is that there are some you know, I

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Speaker 3: can paint a whole doomsday picture of AI, and I

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Speaker 3: can also paint a productivity picture of AI as well.

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Speaker 3: And so I try to focus on the optimization side

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Speaker 3: of AI, and has helped me tremendously over the years.

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Speaker 3: And I try to show people, you know, how they

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Speaker 3: can use it in their daily life, how they can

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Speaker 3: use it at work, and that usually will help kind

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Speaker 3: of address some of that fear.

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Speaker 1: Yeah. So I've had this sentiment that people who get

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Speaker 1: the most out of it are better at asking questions,

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Speaker 1: and the quality of your questions determines the quality of

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Speaker 1: the output. And there are people who are like, you know,

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Speaker 1: very surface level uss like hey, you know, chat or

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Speaker 1: hey Claude, or hey, you know, pick pick one, you know, Jim,

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Speaker 1: and I make this paragraph better or make this letter better.

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Speaker 1: And so can you talk to me about how generally

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Speaker 1: people should approach you know, their AI tools?

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Speaker 2: Yeah.

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Speaker 3: Uh, the best way to describe it is the word delegation,

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Speaker 3: and so at the end of the day, you have

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Speaker 3: thoughts in your mind and you are looking to give

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Speaker 3: those thoughts to a technology and expect an output to

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Speaker 3: be given to you in the format that you want.

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Speaker 3: And so think of AI as like an intern and

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Speaker 3: where you know that your intern is new to your company,

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Speaker 3: they have no context. They're really really smart, but you've

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Speaker 3: got to give it as much context as possible. And

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Speaker 3: so where people fail on AI is just not giving

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Speaker 3: enough information to AI to give you the output that

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Speaker 3: you want. The other thing is that I think is

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Speaker 3: becoming more normal is that you might prompt AI and

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Speaker 3: it doesn't give you that response that you're looking for.

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Speaker 3: But it's actually okay to go back and forth with

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Speaker 3: AI until you get what you want. And what you'll

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Speaker 3: start to realize is the more and more you go

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Speaker 3: back and forth with AI, you start to say, well,

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Speaker 3: if I would have just given all of that information

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Speaker 3: to AI in the front end, then I probably could

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Speaker 3: have got a better response in the back end.

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Speaker 2: And you're just learning how this new technology works. Now.

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Speaker 3: The challenge is is that AI is updating every single

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Speaker 3: day too. So the way you used to prompt two

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Speaker 3: years ago may not be the same way you prompt today.

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Speaker 3: So it's just one of those things where you have

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Speaker 3: to get your hands dirty and continuously play around with

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Speaker 3: AI until you actually figure out how is the best

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Speaker 3: way to talk to this technology.

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Speaker 1: Yeah, this is cold from you, and I found where

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Speaker 1: you said AI is like a tool, is useless without instructions,

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Speaker 1: dangerous without supervision, and brilliant when guided. Well, what are

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Speaker 1: the some of the biggest mistakes you see people making

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Speaker 1: when they first get started.

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Speaker 3: Yeah, I think, uh, there's there's so much hype out there,

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Speaker 3: excuse me about AI. Where AI is going to change

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Speaker 3: your life, It's going to grow your business, it's gonna

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Speaker 3: do all these different things. And I think we have

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Speaker 3: this expectation that, uh, as soon as soon as we prompted,

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Speaker 3: it's going to give us this major output that is

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Speaker 3: going to change our life, and that is just not true.

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Speaker 3: Depending on how you work with it and how long

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Speaker 3: you work with it, really, you know, you kind of

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Speaker 3: you build up a muscle over time, but it's not

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Speaker 3: something that as soon as you use it it's going

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Speaker 3: to be life changing. I think you have to figure

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Speaker 3: out what parts of your life you want changed and

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Speaker 3: work at it. Until you figure out how to do that.

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Speaker 3: For example, if you want to use AI for email, well,

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Speaker 3: in your mind you might say, well, AI is a robot.

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Speaker 3: This is going to handle all my email. I'm never

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Speaker 3: going to have to do email ever again. And that's

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Speaker 3: not necessarily true yet. But there are different ways to

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Speaker 3: speed up that process. So you could have it read

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Speaker 3: your email to you in an audio format. You can

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Speaker 3: have it write your replies in a certain format. You

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Speaker 3: can have it proofread your writing, you can have it

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Speaker 3: work on your grammar. I mean, there's all different types

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Speaker 3: of ways that you can use it for email. But

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Speaker 3: if you're just gonna sit down and you're just like,

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Speaker 3: I'm never gonna do email anymore, we are not there yet,

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Speaker 3: even though it's being sold as we are.

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Speaker 1: Yeah. Now, just this morning, I was on threads and

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Speaker 1: I saw like this debate about, you know, the sophistication

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Speaker 1: of Claude, and I'm gonna give you an example that

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Speaker 1: I'm sure you've seen before. So, like, there's this guy

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Speaker 1: who posted these screenshots of this claud conversation and he's

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Speaker 1: like only chat and gem and I got it right,

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Speaker 1: Claude isn't ready yet. And he was saying that he

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Speaker 1: gave the car wash test, like, hey, Claude, I've got

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Speaker 1: my car is dirty. It needs to go to the

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Speaker 1: car wash. I live, you know, less than a mile

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Speaker 1: away from the car wash. Should I walk or should

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Speaker 1: I drive? And in his screen shot, Claude was like,

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Speaker 1: you know, you should definitely walk, it's not very far,

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Speaker 1: and you know chat and Gemnire like, well, obviously you're

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Speaker 1: gonna drive because the car's gotta get washed and so.

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Speaker 1: But then other people were sharing their screenshots of asking

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Speaker 1: the same TAC question, no changes in you know, the

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Speaker 1: grammar or the punctuation or the wording, and they're getting

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Speaker 1: an actual appropriate response, only their Claude like gave a

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Speaker 1: more contextual answer like, hey, it is a shorter distance,

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Speaker 1: but obviously you need to wash the car, so take it. So, like,

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Speaker 1: what is happening in these debates So not you don't

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Speaker 1: have to take up that particular conversation, but what's happening

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Speaker 1: in our social discourse where yes, we may be getting

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Speaker 1: different inns, but something else I think may be going

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Speaker 1: on And I just want to get your opinion on this.

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Speaker 3: Yeah, I think it's it's funny because you know, I've

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Speaker 3: been in the space for I think it's like three

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Speaker 3: and a half years now.

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Speaker 2: And I remember when.

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Speaker 3: Chad GBT came out, I was like, you know, this

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Speaker 3: is the best platform ever for writing.

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Speaker 2: And then Claude came out and.

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Speaker 3: I said, actually, no, no, this is the best platform

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Speaker 3: for writing. And then everybody kind of started to recognize

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Speaker 3: the M dashes that are going on in in uh

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Speaker 3: in AI or in chat GBT, where people are like, ah,

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Speaker 3: these M dashes, you can tell it's AI writing. Then

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Speaker 3: over time, I think it was last year chad GBT

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Speaker 3: got rid of the M dash.

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Speaker 2: Where it doesn't come up as much, I would say.

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Speaker 3: And so these models are trained on different writing and

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Speaker 3: so I guess my my advice to everyone would be

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Speaker 3: use all of the models and figure out which one

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Speaker 3: is best for whatever task that you're that you're doing. So,

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Speaker 3: for example, because I'm a speaker, I wanted to after

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Speaker 3: every speech, I wanted AI to analyze all my results

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Speaker 3: and figure out where I can improve. And so I

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Speaker 3: ended up giving the same prompt to CHATGYBT, Gemini and

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Speaker 3: Claude and through it in there and looked at the

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Speaker 3: report and at the end of the day, Claude was

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Speaker 3: the best report and the reason why is because it

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Speaker 3: was giving me things that I didn't ask for but

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Speaker 3: I actually needed. And I think that's where these models

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Speaker 3: are starting to move. Chat GYBT five point four just

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Speaker 3: came out on Friday or Thursday or Friday last week,

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Speaker 3: and I've been playing with it, and as of today,

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Speaker 3: it's asking me things that I didn't ask for but

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Speaker 3: that I need that could be useful. So I was

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Speaker 3: doing an exercise this morning where I gave AI some Yeah,

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Speaker 3: I was working on a statement of work and at

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Speaker 3: the very end of it, it said, you're doing a

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Speaker 3: lot of statement of works, like should you want me

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Speaker 3: to just build you a temple for that? And I'm talking, well, yeah,

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Speaker 3: let's do that, And so it built the template and

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Speaker 3: so now I have the template. So it's like this

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Speaker 3: proactive thing that's happening, which is which is really interesting.

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Speaker 1: Yeah. So we were talking before we started recording here about,

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Speaker 1: you know, just kind of what was going on in

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Speaker 1: your world was going on in my world. And first

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Speaker 1: before I before I get into like they, hey, what

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Speaker 1: we're doing, I want to talk about this leap you

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Speaker 1: took recently of you know, you you were at Apoor

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Speaker 1: Capital doing your thing that's where we met. When you

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Speaker 1: were there, and you know this speaking opportunity, you've had

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Speaker 1: this consulting thing, You've you've had an opportunity to have this,

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Speaker 1: you know, building a personal brand around AI. Like, talk

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Speaker 1: to me about the journey from like when you just

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Speaker 1: thought it was interesting to where now it's like your

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Speaker 1: full time job, Like how does that happen?

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Speaker 3: Yeah, yeah, that's a great, great question. It's been a journey.

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Speaker 3: So the interesting thing, I'll actually start back when I

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Speaker 3: used to work for Damon John from ABC Shark Tank

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Speaker 3: and he he is a public speaker, one of the

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Speaker 3: best out there, and I would go watch him speak,

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Speaker 3: and so that kind of got me interested in speaking.

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Speaker 3: And I remember a day where Damon couldn't make a

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Speaker 3: speaking engagement and I asked him, I said, can I

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Speaker 3: do it? And He's like, yeah, go ahead, And I

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Speaker 3: got on stage and crushed the conference and I was like, Okay,

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Speaker 3: I actually have it in me. So that's kind of

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Speaker 3: how I first got interested in speaking. But I didn't

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Speaker 3: do that much speaking after that. And then when I

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Speaker 3: got into venture capital in twenty twenty, I started speaking

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Speaker 3: at all the VC conferences, but I was speaking on

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Speaker 3: marketing tools specifically, and so in November of twenty twenty two,

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Speaker 3: when Chad Gibt came out, I instantly was like, this

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Speaker 3: is about to be big.

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Speaker 2: And I think it was like January or February or.

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Speaker 3: March twenty twenty three, I said it's time to get

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Speaker 3: back on the road and start sharing the story around AI.

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Speaker 3: And so I think that first year I did, I

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Speaker 3: think it was a probably fourteen gigs, and actually the

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Speaker 3: gig that we did at the end of the year

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Speaker 3: was the Whole Global Form in Atlanta where we did

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Speaker 3: the first podcast, and I remember going to that and

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Speaker 3: you know, just thinking that AI was kind of like

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Speaker 3: not a big deal. And then I remember on the

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Speaker 3: prep call they said, well, you're going to be speaking

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Speaker 3: before Sam, and I'm thinking Sam, and it was a

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Speaker 3: Sam Am and I was like, oh wow, Okay the

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Speaker 3: owner of Okay, got It, got It, got It. And

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Speaker 3: so that was the first moment where I said, you know,

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Speaker 3: I might be onto something.

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Speaker 2: And just kept continuing to speak.

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Speaker 3: That following year twenty twenty four, I did about twenty

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Speaker 3: two gigs, and then last year what ended up happening

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Speaker 3: is that in twenty twenty five, I feel like was

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Speaker 3: the first year that everybody started to understand, wait a minute,

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Speaker 3: AI is about to be big, and then they go,

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Speaker 3: wait a minute, we've been seeing Cliff talk about it,

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Speaker 3: So let's have Cliff come in and speak to us.

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Speaker 3: And so I got so many gigs last year that

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Speaker 3: I had to make a decision the end of the

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Speaker 3: year to say, all right, I'm running out of PTO.

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Speaker 3: I've got to make a decision. Do I want to

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Speaker 3: go all in on AI or do I want to

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Speaker 3: kind of keep doing this as a hobby. And you know,

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Speaker 3: AI has only gotten better and better, and so I

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Speaker 3: said it's time to take the leap. And I wish

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Speaker 3: I would have taken the leap about a year ago.

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Speaker 3: But here we are, and it's it's been, it's been.

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Speaker 3: It's been amazing. I've started a consultancy where I'm actually

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Speaker 3: training Fortune five hundred employees on how to use AI

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Speaker 3: and then also working with them in a ten week

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Speaker 3: kind of program to help them accelerate their employees and

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Speaker 3: help them really optimize their workflows with AI as well,

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Speaker 3: which has been super exciting.

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Speaker 1: Yeah, so I'm interested in your take on this because

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Speaker 1: you know, I've been doing talks on AI, but more

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Speaker 1: for like students in educational platforms and districts, and as

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Speaker 1: you've been like, there's forty thousand tools out there and

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Speaker 1: neither you or I can be proficient at all of them.

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Speaker 1: So I wonder how you start to narrow your lens,

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Speaker 1: And maybe narrow is the wrong where, but I think

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Speaker 1: you get what I'm saying. How do you focus on

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Speaker 1: the ones that matter so that when you get a

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Speaker 1: question or you get approached by, you know, somebody who

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Speaker 1: wants you to speak on the thing, you are at least

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Speaker 1: knowledgeable enough to know what's in the ecosystem.

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Speaker 2: Yeah, great question, you know. I think one is just

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Speaker 2: being in an early understanding what.

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Speaker 3: Tools that aren't the big kind of big lllms like chatgybt, Gemini,

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Speaker 3: Claude co Pilot, which ones have been around for a while.

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Speaker 3: So I understand that because they've received tons of funding

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Speaker 3: back in twenty twenty three, they're now building these robust platforms.

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Speaker 2: So that's that's that's one piece of it.

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Speaker 3: But I do think what's happening is that these large

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Speaker 3: language models like the CHATGYBT, Gemini's Claude co Pilot, they're

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Speaker 3: starting to get better and so they're building features where

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Speaker 3: you might have used a one off.

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Speaker 2: Tool to do.

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Speaker 3: Let's just say for images, I used to use a

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Speaker 3: tool called ideo grap that I would use for images. Well,

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Speaker 3: now Gemini has Nano Banana two and that's really really good.

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Speaker 3: And also Chad GBT they updated their image model a

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Speaker 3: last year. This now almost as good as Gemini's model.

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Speaker 3: So now it's like, I don't necessarily need that tool anymore.

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Speaker 3: So I think everybody's working towards building this all in

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Speaker 3: one tool. So I kind of tell people if you're

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Speaker 3: gonna buy, if you're gonna buy a tool, or if

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Speaker 3: you're gonna get it, use a tool. I would say,

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Speaker 3: you know, use one of the big four and then

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Speaker 3: actually pay the twenty dollars a month because that gives

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Speaker 3: you access to features. And what I tell people is

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Speaker 3: that would you pay twenty dollars a month to have

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Speaker 3: access to Albert Einstein knowledge?

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Speaker 2: Right?

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Speaker 3: And so but I pay for all four, so I'm like, okay, well,

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Speaker 3: eighty dollars a month gives me four Einsteins, so I

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Speaker 3: don't mind.

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Speaker 2: I don't mind paying that all right.

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Speaker 1: So I I consider my self like a pro like

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Speaker 1: claud Used, like I'm on Max, So I pay two

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Speaker 1: hundred dollars a month and I'm like, I spend half

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Speaker 1: of my day on claw Code. And so right before

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Speaker 1: we got on the air today we were talking about

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Speaker 1: just some of the stuff I was working on, and

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Speaker 1: I'm kind of gonna, you know, use this moment to

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Speaker 1: like kind of get into actual practical things founders should

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Speaker 1: be doing. Because the first thing you said when I

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Speaker 1: say you know, hey, you know how you doing, you

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Speaker 1: was like, hey, I help you use the AI for

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Speaker 1: everything you got going on, and I'm like, yes, i am.

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Speaker 1: So I'm gonna take this moment to I dig into

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Speaker 1: founder tips and advice that you have for people who

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Speaker 1: are building medium sized businesses. This might be I'll pick

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Speaker 1: a small businesses, small and medium sized businesses. Also, So,

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Speaker 1: so I told you about Tohow. So we are twenty

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Speaker 1: five thousand square feet a private social club, you know,

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Speaker 1: five bars, five lounges of coffee house and jazz club

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Speaker 1: is in here. I'm building a pizza company. So if

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Speaker 1: you guys follow me on Instagram or et set or

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Speaker 1: you'll see all of the stuff I'm doing. A dirty

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Speaker 1: soda company called phiz These drinks whereas it's non alcoholic.

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Speaker 1: It's just dirty solders. So you take a coke and

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Speaker 1: put some cream in and pineapples whatever, you know, do

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Speaker 1: the thing, and you're like, I hope you're using AI

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Speaker 1: to build all this stuff. And the challenge I see

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Speaker 1: in myself and other founders like me is number one,

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Speaker 1: I'm self funded, so I have no investors in any

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Speaker 1: of the stuff I'm doing. And so what that means

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Speaker 1: is there's not always like a war chest to go

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Speaker 1: hire the best people or mediocre people even to go

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Speaker 1: take off some of these operator tasks so I can

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Speaker 1: focus on CEO tasks. How do you see founders like

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Speaker 1: myself get stuck in smallness because they don't have the

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Speaker 1: resources to scale beyond those limitations of being one person.

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Speaker 3: Yeah, no, that's a great question. I actually just had

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Speaker 3: a breakthrough yesterday on this. And so you know a

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Speaker 3: lot of people are saying scared that, you know, AI

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Speaker 3: is going to take jobs and things like that. But

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Speaker 3: the other byproduct that's happening for me specifically, it's actually

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Speaker 3: given me more work. Right, if I can do things faster, now,

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Speaker 3: I got a whole bunch of work. And so I

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Speaker 3: actually had this moment yesterday where I'm like, coming into

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Speaker 3: this week, I'm like, I got a lot of things

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Speaker 3: to do and it's like almost impossible. And so I said, well,

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Speaker 3: and I'm a one man show as well. And so

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Speaker 3: I went to Chad JBT and I just turned on

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Speaker 3: my voice mode and I rambled for about five minutes,

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Speaker 3: and I said, basically, the what I talked about was

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Speaker 3: is that here are my goals, right, this is what

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Speaker 3: I want to do, and here are the things that

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Speaker 3: are coming up that you should know about. And then three,

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Speaker 3: here are all the tasks that are random that I've

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Speaker 3: been giving and that's on my plate. Can you help

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Speaker 3: me prioritize this? Man? When I tell you this thing?

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Speaker 3: Broke this thing down. It said, look for the next week,

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Speaker 3: you need to work on these three these things. They

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Speaker 3: gave it by day and then they said, don't worry

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Speaker 3: about the rest of this stuff because these are just

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Speaker 3: operational tasks that if you don't get them done this week,

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Speaker 3: it doesn't really matter. But if you want to meet

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Speaker 3: your goal, you need to focus on this on these days.

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Speaker 3: And so then I thought about it. I said, I

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Speaker 3: need to keep AI up to date with everything I'm

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Speaker 3: doing every single day, because that's where it becomes really

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Speaker 3: useful is that when it knows exactly what you have

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Speaker 3: going on, and it can help you adjust your plan

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Speaker 3: as things come up.

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Speaker 2: So for example, if I say.

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Speaker 3: Tomorrow I get an offer for to be on a podcast, right,

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Speaker 3: I can then go, hey, I just got this offer

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Speaker 3: to be on this podcast. This is why I want

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Speaker 3: to go on it. What compare that to what I

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Speaker 3: have going on in my plan? Should I do this?

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Speaker 3: And then it can say yes or no. If you're

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Speaker 3: going to do it, do it after this date because

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Speaker 3: you've got to focus on this. You said that this

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Speaker 3: was your goal, right, and you can go back and

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Speaker 3: forth with it. And I think that's one thing that

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Speaker 3: is is getting really really good with AI is is

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Speaker 3: it's memory, so it understands who you are, what you're

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Speaker 3: trying to accomplish. But you've got to give it that

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Speaker 3: those goals and your task lists for it to really

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Speaker 3: help you help you do that. And then you know,

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Speaker 3: I'm I'm going to go through this week and if

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Speaker 3: it doesn't work, then I'll adjust. I'll tell AI, hey,

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Speaker 3: this is what worked, is what didn't work, and we'll

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Speaker 3: just keep working from there until we dial it in.

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Speaker 2: So I think for entrepreneurs.

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Speaker 3: You know, give it, give it your task list, give

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Speaker 3: it all the things, ask the questions, how can I

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Speaker 3: make this go faster? Or you know, how can I

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Speaker 3: save money? Who can I go after? I mean I've

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Speaker 3: gone in used AI to find contractors where I say,

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Speaker 3: you know, who's the best in copywriting on this kind

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Speaker 3: of thing or and it will find those things. So

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Speaker 3: I would I'm always encouraging everybody to lean on IT

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Speaker 3: for all the tasks that you have. So like as

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Speaker 3: a as an owner, you might have like you might

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Speaker 3: have come up with a tagline that you like, but

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Speaker 3: you're like, how can.

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Speaker 2: I make this stronger? Well put that in AI? Right, see,

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Speaker 2: give me give me ten more taglines.

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Speaker 3: Or you might say, you know, I'm thinking of these colors,

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Speaker 3: mock up four different ad campaigns for you know that

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Speaker 3: are in this format that you can do for my

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Speaker 3: for my soda brand, and you know, so you can

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Speaker 3: start versus having to hire a designer.

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Speaker 2: Right.

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Speaker 1: Yeah, So there's there's so much there, And the first

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Speaker 1: thing I want to do is mention something I've heard

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Speaker 1: other people say. When you do things like this, it

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Speaker 1: Chat and Claude have a tendency to want to appease you,

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Speaker 1: and so it'll like, hey, you know, Will, that's a

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Speaker 1: fantastic idea. Hey Cliff, you're the greatest ever that I

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Speaker 1: and you just say and you're like, hey man, I

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Speaker 1: got this challenge, and it's like you're you're so fantastic.

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Speaker 1: Here's you know, how you should think about things. And

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Speaker 1: so I wonder how what part of that is helpful

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Speaker 1: in your from your perspective, and how to tamp it

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Speaker 1: down if what I really need is somebody to challenge

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Speaker 1: me on the things that I'm saying.

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Speaker 2: Yeah, no, it's a good question.

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Speaker 3: A lot of people are getting around that by putting

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Speaker 3: in their custom instructions, which is like the instructions you're

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Speaker 3: giving for the entire model chat Gut or Claude, where

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Speaker 3: you might say, before you give me a response, challenge me,

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Speaker 3: or don't agree with everything that I asked you to do.

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Speaker 2: If it doesn't make sense, challenge me.

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Speaker 3: Or it might be if you don't have the answer,

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Speaker 3: say you don't know, right, because yeah, AI is really

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Speaker 3: good at saying yeah, you're brilliant.

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Speaker 2: That's a great idea. Da da da da da.

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Speaker 3: But that's its default setting, and so if you want

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Speaker 3: it to be challenging you more. You just need to

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Speaker 3: be able to customize it to however you want it

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Speaker 3: to respond to you by. And that's the cool thing

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Speaker 3: about AI is that they give us this default model,

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Speaker 3: but we can program it however we want to receive

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Speaker 3: those responses.

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Speaker 1: Where are we yet on it's ability? You talked about

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Speaker 1: the memory that it can store, So where are we

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Speaker 1: yet on context?

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Speaker 2: So?

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00:22:07,400 --> 00:22:10,760
Speaker 1: If I give chat or claude, you know, this is

435
00:22:10,800 --> 00:22:14,359
Speaker 1: a goal that I have, But three weeks ago I

436
00:22:14,400 --> 00:22:17,960
Speaker 1: said something else is So is it still or depending

437
00:22:17,960 --> 00:22:20,800
Speaker 1: on how much I'm not using it, is it still remembering? Yeah,

438
00:22:20,840 --> 00:22:22,879
Speaker 1: you said this is a goal, but remember what you

439
00:22:22,920 --> 00:22:25,280
Speaker 1: said three weeks ago too. How are you going to,

440
00:22:25,400 --> 00:22:27,000
Speaker 1: you know, do both of these things? If both of

441
00:22:27,000 --> 00:22:29,280
Speaker 1: these are the goals, and they may have competing interests,

442
00:22:29,320 --> 00:22:32,359
Speaker 1: so how are where are we yet in that spectrum?

443
00:22:32,440 --> 00:22:32,800
Speaker 2: Yeah?

444
00:22:32,920 --> 00:22:37,000
Speaker 3: I think that there's a memory context Max. I don't

445
00:22:37,000 --> 00:22:40,400
Speaker 3: know what the number is. But if you go into

446
00:22:40,560 --> 00:22:44,240
Speaker 3: your settings on Chad, shep, BT, Claude, whatever, you can

447
00:22:44,280 --> 00:22:49,000
Speaker 3: actually see what it's memorizing, and so you can update that.

448
00:22:49,119 --> 00:22:50,480
Speaker 3: If you say, hey, I don't want to I don't

449
00:22:50,520 --> 00:22:52,119
Speaker 3: want to take that I don't want you to use

450
00:22:52,160 --> 00:22:56,280
Speaker 3: that memory anymore that goal was old. The other thing

451
00:22:56,359 --> 00:22:58,480
Speaker 3: is that it also comes down to the prompt. You

452
00:22:58,520 --> 00:23:02,000
Speaker 3: can say, you know, this is my goal for this

453
00:23:02,080 --> 00:23:05,679
Speaker 3: year or for this this year. Now please ignore the

454
00:23:05,720 --> 00:23:08,320
Speaker 3: previous goals that we had talked about. So once again,

455
00:23:08,359 --> 00:23:10,639
Speaker 3: it still takes you do that. The other thing is

456
00:23:10,800 --> 00:23:13,639
Speaker 3: you could ask it what goals do you know about me?

457
00:23:14,000 --> 00:23:17,520
Speaker 3: Before I tell you my thing, and it'll tell you

458
00:23:17,640 --> 00:23:18,919
Speaker 3: this is what I know about you, this is what

459
00:23:18,960 --> 00:23:21,480
Speaker 3: you said. And so, like I said, once again, it's

460
00:23:21,520 --> 00:23:25,720
Speaker 3: like I think one of the misconceptions with AI is

461
00:23:25,720 --> 00:23:30,000
Speaker 3: that it's like it works really really well on all things,

462
00:23:30,320 --> 00:23:33,199
Speaker 3: and then I always want to warn people is to

463
00:23:33,240 --> 00:23:36,040
Speaker 3: say is that it is getting better every single day,

464
00:23:36,520 --> 00:23:38,840
Speaker 3: but still use it as like an intern where this

465
00:23:38,920 --> 00:23:41,080
Speaker 3: person might not know that much and you get a

466
00:23:41,119 --> 00:23:43,280
Speaker 3: new intern every single day and you're like, okay, I

467
00:23:43,280 --> 00:23:46,159
Speaker 3: got to explain to you the context. I just find

468
00:23:46,160 --> 00:23:49,040
Speaker 3: it that the more I sit there and explain what

469
00:23:49,080 --> 00:23:51,639
Speaker 3: I'm trying to do with here's the background information. I

470
00:23:51,720 --> 00:23:53,919
Speaker 3: just got this text message from this person and this

471
00:23:54,000 --> 00:23:55,639
Speaker 3: is what they said. This is how it makes me

472
00:23:55,680 --> 00:23:58,720
Speaker 3: feel about it. Da da da da is the best

473
00:23:58,720 --> 00:24:00,160
Speaker 3: way to go every.

474
00:24:00,080 --> 00:24:03,320
Speaker 1: Yeah, you know you dropped. I said this every Monday

475
00:24:03,359 --> 00:24:04,919
Speaker 1: that you drop a newsletter, and I want you to

476
00:24:04,920 --> 00:24:07,040
Speaker 1: talk about that a little bit. But I really liked

477
00:24:07,200 --> 00:24:09,880
Speaker 1: I think it was today's I'm gonna read. I'm gonna

478
00:24:09,880 --> 00:24:12,320
Speaker 1: read the first little bit, maybe the first quarter, and

479
00:24:12,320 --> 00:24:14,879
Speaker 1: it said you are a strategic Oh, this is a

480
00:24:14,920 --> 00:24:18,760
Speaker 1: prompt that you were giving examples of what people can,

481
00:24:19,119 --> 00:24:21,479
Speaker 1: you know, prompt their chat or their claud or whatever

482
00:24:22,119 --> 00:24:25,160
Speaker 1: and so. And I've seen other people do this. They're

483
00:24:25,200 --> 00:24:28,280
Speaker 1: really cool because you come to these prompts with perspectives

484
00:24:28,280 --> 00:24:30,639
Speaker 1: that I never even thought about, you know, given my

485
00:24:30,720 --> 00:24:34,920
Speaker 1: chat or giving my claud. So today's was you're telling

486
00:24:35,040 --> 00:24:37,520
Speaker 1: chat or you're encouraging us to tell chat. You are

487
00:24:37,520 --> 00:24:42,560
Speaker 1: a strategic career advisor, business strategists and future forecaster based

488
00:24:42,600 --> 00:24:45,320
Speaker 1: on everything you know about me from this conversation and

489
00:24:45,400 --> 00:24:49,520
Speaker 1: my work patterns, and analyze my current trajectory, assume I

490
00:24:49,680 --> 00:24:53,000
Speaker 1: continue with roughly the same approach, habits and level of

491
00:24:53,080 --> 00:24:56,399
Speaker 1: effort that I have today. Where do you realistically see

492
00:24:56,400 --> 00:24:58,119
Speaker 1: me in five years? And then it goes on to

493
00:24:58,160 --> 00:25:01,600
Speaker 1: give a bunch of detail and context, and so I'm going.

494
00:25:02,119 --> 00:25:04,400
Speaker 1: There's a lot in there, But the one I really

495
00:25:04,400 --> 00:25:07,359
Speaker 1: want to pick on is there's a lot of context

496
00:25:07,359 --> 00:25:09,440
Speaker 1: to this question because I didn't even give you guys

497
00:25:09,440 --> 00:25:12,800
Speaker 1: the full you know, prompt and it probably goes on

498
00:25:13,040 --> 00:25:16,399
Speaker 1: for you know, two times what I just read. So

499
00:25:17,040 --> 00:25:22,480
Speaker 1: when you are giving prompts to your chat based on

500
00:25:22,520 --> 00:25:24,640
Speaker 1: what I see people like you who are experts in this,

501
00:25:24,920 --> 00:25:27,520
Speaker 1: they are never like really just short brief questions. It's

502
00:25:27,560 --> 00:25:31,000
Speaker 1: like giving you giving it a enough stuff so that

503
00:25:31,080 --> 00:25:34,760
Speaker 1: it has perspective in context. Is that always required? Or

504
00:25:35,200 --> 00:25:38,760
Speaker 1: or are there easier ways or simpler ways because it

505
00:25:38,800 --> 00:25:42,240
Speaker 1: may know me, you know, to give me good responses.

506
00:25:43,080 --> 00:25:48,040
Speaker 2: Yeah. Yeah, I would say that there's two things.

507
00:25:47,760 --> 00:25:52,199
Speaker 3: That are happening right now in AI that are interesting. So,

508
00:25:53,400 --> 00:25:56,400
Speaker 3: like I said, I wrote at that long prompt right,

509
00:25:56,640 --> 00:25:58,639
Speaker 3: which takes a lot of time.

510
00:25:58,920 --> 00:26:00,320
Speaker 2: You got to fill out a lot offormation.

511
00:26:00,880 --> 00:26:04,119
Speaker 3: But the more you give AI up front, the better

512
00:26:04,160 --> 00:26:05,840
Speaker 3: the output is going to be on the back end.

513
00:26:06,720 --> 00:26:10,040
Speaker 3: Second thing is that there's this thing happening specifically in

514
00:26:10,160 --> 00:26:12,840
Speaker 3: Claude and starting to happen a little bit in Chad

515
00:26:12,840 --> 00:26:16,239
Speaker 3: GBT as well, where you can now package up that

516
00:26:16,320 --> 00:26:20,879
Speaker 3: whole entire prompt as a skill, right, so I can say,

517
00:26:21,119 --> 00:26:23,480
Speaker 3: let's just say we call that prompt. Let's say it's

518
00:26:23,520 --> 00:26:27,600
Speaker 3: a you know, five hundred word prompt, and we name

519
00:26:27,680 --> 00:26:32,760
Speaker 3: it career Advisor. Now what's happening with prompting is I

520
00:26:32,800 --> 00:26:38,080
Speaker 3: can say, hey, Chad GBT, run the career advisor prompt,

521
00:26:38,200 --> 00:26:41,000
Speaker 3: and it now knows that as a skill, so you

522
00:26:41,040 --> 00:26:43,760
Speaker 3: don't have to go back and forth typing all that

523
00:26:43,800 --> 00:26:45,560
Speaker 3: information out every single time.

524
00:26:46,040 --> 00:26:47,920
Speaker 2: And that is where AI is moving.

525
00:26:48,080 --> 00:26:50,840
Speaker 3: I think Claude is the first one to integrate that,

526
00:26:51,440 --> 00:26:53,840
Speaker 3: but probably within the next couple of months, we're going

527
00:26:53,880 --> 00:26:56,919
Speaker 3: to start to see all of these other platforms copy

528
00:26:57,440 --> 00:27:01,640
Speaker 3: each other and have that so where now it's kind

529
00:27:01,640 --> 00:27:03,040
Speaker 3: of like one of these things where you want to

530
00:27:03,080 --> 00:27:05,880
Speaker 3: spend the most time as you can writing that prompt

531
00:27:06,160 --> 00:27:07,800
Speaker 3: because it's going to give you that output. But I

532
00:27:07,800 --> 00:27:10,359
Speaker 3: will also say it's like it's I do short prompts

533
00:27:10,359 --> 00:27:12,960
Speaker 3: as well. It's not necessarily for everything, but for something

534
00:27:13,080 --> 00:27:16,040
Speaker 3: like that's important that needs that context. I don't think

535
00:27:16,040 --> 00:27:19,000
Speaker 3: you can go wrong with giving AI too much context.

536
00:27:19,840 --> 00:27:23,600
Speaker 1: You know, you talked about the Big four and I'm

537
00:27:23,640 --> 00:27:25,520
Speaker 1: not sure if you mentioned perplexity in there. Maybe it

538
00:27:25,560 --> 00:27:30,679
Speaker 1: was just a Gemini Claude chat and was it X?

539
00:27:31,280 --> 00:27:35,480
Speaker 1: I forgot what copilot co pilot? Okay, I've never touched copilt.

540
00:27:35,480 --> 00:27:38,080
Speaker 1: I'm not a you know, not a big Microsoft person.

541
00:27:39,200 --> 00:27:41,560
Speaker 1: But no, no shade on Microsoft. I just don't have

542
00:27:41,720 --> 00:27:45,119
Speaker 1: a lot of the products. So when you talk about

543
00:27:45,160 --> 00:27:48,760
Speaker 1: those big floor, big four, what are other tools that

544
00:27:48,840 --> 00:27:53,360
Speaker 1: you found, particularly you as an entrepreneur who doesn't have

545
00:27:53,520 --> 00:27:56,360
Speaker 1: you know, a huge team, Like, what have you found?

546
00:27:56,480 --> 00:27:59,320
Speaker 1: Are the tools, the specifically AI tools that have that

547
00:27:59,359 --> 00:28:02,240
Speaker 1: have allowed you to over leverage where you could not have,

548
00:28:02,440 --> 00:28:04,520
Speaker 1: you know, two years ago, even because you didn't have

549
00:28:04,560 --> 00:28:05,520
Speaker 1: these other things.

550
00:28:06,040 --> 00:28:10,199
Speaker 3: Yeah, I think I'm trying to think about my my

551
00:28:10,640 --> 00:28:16,120
Speaker 3: current AI tech stack. So I think one is another one,

552
00:28:16,119 --> 00:28:18,359
Speaker 3: I mean it's actually free is Notebook LM, which is

553
00:28:18,400 --> 00:28:22,240
Speaker 3: made by Google. The reason why I love this one,

554
00:28:22,359 --> 00:28:25,480
Speaker 3: I mean this one. It started off as this this

555
00:28:25,520 --> 00:28:28,200
Speaker 3: tool where you can put in you know, a link

556
00:28:28,320 --> 00:28:32,480
Speaker 3: or any document and it'll turn that into an two

557
00:28:32,520 --> 00:28:36,159
Speaker 3: person audio podcast. And so because I have a newsletter,

558
00:28:36,280 --> 00:28:38,560
Speaker 3: I was like, okay, well how do I reach people

559
00:28:38,600 --> 00:28:40,680
Speaker 3: on LinkedIn who aren't reading my newsletter? I was like,

560
00:28:40,680 --> 00:28:43,280
Speaker 3: I should turn it into a two person podcast. So

561
00:28:43,440 --> 00:28:45,520
Speaker 3: early days, I was feeding that into that for that,

562
00:28:45,640 --> 00:28:49,959
Speaker 3: but over time they started adding more features where you

563
00:28:50,000 --> 00:28:55,200
Speaker 3: can now turn any link or multiple documents into a PowerPoint,

564
00:28:55,480 --> 00:29:01,160
Speaker 3: a video that got the audio overview and infographic mind map.

565
00:29:01,600 --> 00:29:04,360
Speaker 3: And so the cool thing about notebook LM is that

566
00:29:04,720 --> 00:29:07,360
Speaker 3: if you learn in a certain way, like if you're

567
00:29:07,400 --> 00:29:10,280
Speaker 3: a visual learner or you're an audio learner, or you're

568
00:29:10,320 --> 00:29:14,640
Speaker 3: like a visual and audio learner, it can now give

569
00:29:14,680 --> 00:29:17,600
Speaker 3: you the format of whatever it is that you're trying

570
00:29:17,600 --> 00:29:19,720
Speaker 3: to learn, so you don't have to sit there and

571
00:29:19,760 --> 00:29:22,640
Speaker 3: read a long document anymore. And they do it in

572
00:29:22,680 --> 00:29:24,360
Speaker 3: a way where it's like, you know, the video might

573
00:29:24,400 --> 00:29:25,840
Speaker 3: be like eight minutes long.

574
00:29:25,960 --> 00:29:26,280
Speaker 2: And so.

575
00:29:28,400 --> 00:29:31,520
Speaker 3: I think they have really taken a way to take

576
00:29:31,680 --> 00:29:34,240
Speaker 3: learning and making it very very simple.

577
00:29:34,320 --> 00:29:36,120
Speaker 2: So I can go through a whole lot of information

578
00:29:36,160 --> 00:29:40,200
Speaker 2: now I throw it into notebook LM.

579
00:29:40,200 --> 00:29:41,840
Speaker 3: And there's sometimes where I'm like, there might be a

580
00:29:42,000 --> 00:29:45,600
Speaker 3: complicated article that I'm reading on AI and I'm like,

581
00:29:45,640 --> 00:29:47,880
Speaker 3: I have no idea what this guy's talking about. I

582
00:29:47,920 --> 00:29:49,800
Speaker 3: will throw it in a notebook LM. It'll create the

583
00:29:49,880 --> 00:29:53,400
Speaker 3: visual and I'm like, oh, I get it. Yeah, you know.

584
00:29:53,760 --> 00:29:58,280
Speaker 1: Yeah, So when you take because I'm very interested in this,

585
00:29:58,520 --> 00:30:01,680
Speaker 1: you know, idea of you being a solo founder, like

586
00:30:01,720 --> 00:30:03,640
Speaker 1: you've got your thing that you're building, don't have a

587
00:30:03,640 --> 00:30:05,720
Speaker 1: big team yet, because I see more and more people

588
00:30:05,760 --> 00:30:10,680
Speaker 1: becoming many versions of you. What is it that concerns

589
00:30:10,720 --> 00:30:14,920
Speaker 1: you about the previous thinking in your VC world of hey,

590
00:30:14,920 --> 00:30:16,760
Speaker 1: do you have a team? I remember when you know,

591
00:30:16,800 --> 00:30:19,000
Speaker 1: when you were going to the vcs and trying to

592
00:30:19,000 --> 00:30:21,960
Speaker 1: get funding for your startup. The first question is about

593
00:30:21,960 --> 00:30:25,080
Speaker 1: your team? You know, you know, we know you, Cliff,

594
00:30:25,120 --> 00:30:27,840
Speaker 1: You're great, But who's coming on this journey with you?

595
00:30:27,880 --> 00:30:31,280
Speaker 1: Can you talk about what this new day of entrepreneurship

596
00:30:31,320 --> 00:30:34,160
Speaker 1: is like because you have these other things.

597
00:30:34,440 --> 00:30:39,120
Speaker 3: Yeah, it's an interesting time in VC. I think if

598
00:30:39,120 --> 00:30:42,440
Speaker 3: you look at I don't remember the stats, but the

599
00:30:44,440 --> 00:30:47,040
Speaker 3: companies right now that are doing one hundred million dollars

600
00:30:47,280 --> 00:30:53,600
Speaker 3: arr right, they're like ten, eleven, fifteen person teams. So

601
00:30:53,720 --> 00:30:57,960
Speaker 3: that changes the whole entire narrative about how many people

602
00:30:58,040 --> 00:31:01,479
Speaker 3: do you need to hire to do a specific task.

603
00:31:02,160 --> 00:31:05,800
Speaker 3: And so what you're starting to see is that you know,

604
00:31:05,840 --> 00:31:09,960
Speaker 3: with AI is allowing someone who the way I kind

605
00:31:09,960 --> 00:31:12,400
Speaker 3: of describe it is that our work is going to change,

606
00:31:12,440 --> 00:31:15,200
Speaker 3: because now you're going to be able to have skill

607
00:31:15,240 --> 00:31:17,800
Speaker 3: sets that you would have never had before.

608
00:31:18,000 --> 00:31:21,560
Speaker 2: Right, So, like, if I want this like.

609
00:31:21,720 --> 00:31:26,320
Speaker 3: Really cool opening like video shot with like smoke coming

610
00:31:26,360 --> 00:31:29,360
Speaker 3: out and then it's like coming off the basketball court,

611
00:31:29,560 --> 00:31:32,800
Speaker 3: like I'd have to go hire a videographer, find the

612
00:31:32,880 --> 00:31:37,040
Speaker 3: right court, find the right lighting, spend a ton of money.

613
00:31:37,320 --> 00:31:40,440
Speaker 3: But now I can just prompt that same thing into AI.

614
00:31:41,240 --> 00:31:44,040
Speaker 3: And so do we actually need to hire a videographer

615
00:31:44,080 --> 00:31:44,920
Speaker 3: to do that now?

616
00:31:45,080 --> 00:31:48,320
Speaker 2: Right? Which is an interesting interesting thing.

617
00:31:48,560 --> 00:31:52,160
Speaker 3: And I would say that AI is not good at everything,

618
00:31:52,200 --> 00:31:54,880
Speaker 3: but there are certain things that is actually really really

619
00:31:54,880 --> 00:31:57,120
Speaker 3: good at and I think that's where you're starting to

620
00:31:57,120 --> 00:32:00,760
Speaker 3: see and even coding for example, and this is what

621
00:32:00,760 --> 00:32:02,200
Speaker 3: what what happened.

622
00:32:02,560 --> 00:32:05,840
Speaker 2: Towards the end of December is that coding.

623
00:32:05,520 --> 00:32:10,160
Speaker 3: Got really really good and with clawed code, specifically to

624
00:32:10,280 --> 00:32:14,440
Speaker 3: where people that I know that are that are coders,

625
00:32:15,240 --> 00:32:18,600
Speaker 3: they're excited because they're like, I don't have to sit

626
00:32:18,800 --> 00:32:21,560
Speaker 3: down and code for hours anymore. I can just tell

627
00:32:21,600 --> 00:32:23,720
Speaker 3: AI to do this. I can walk away from my

628
00:32:23,800 --> 00:32:26,440
Speaker 3: computer and I can come back and the code is done,

629
00:32:26,720 --> 00:32:29,080
Speaker 3: and it is done faster than I would have done,

630
00:32:29,200 --> 00:32:30,200
Speaker 3: and it's done better than I.

631
00:32:30,200 --> 00:32:30,680
Speaker 2: Would have done.

632
00:32:30,680 --> 00:32:32,520
Speaker 1: It's production quality, it's not just.

633
00:32:33,760 --> 00:32:36,880
Speaker 3: Yeah, and I would say, you know, if there's an

634
00:32:37,000 --> 00:32:39,640
Speaker 3: industry that has been impacted the most right now, it

635
00:32:39,680 --> 00:32:41,640
Speaker 3: is it is coding. And so you see a lot

636
00:32:41,640 --> 00:32:43,560
Speaker 3: of engineers that are fighting backs saying I don't want

637
00:32:43,560 --> 00:32:46,200
Speaker 3: to use that AI like I like coding, but people

638
00:32:46,200 --> 00:32:48,160
Speaker 3: are starting to say, well, it's actually doing it faster

639
00:32:48,280 --> 00:32:50,320
Speaker 3: and better, so you might as well just do this

640
00:32:50,520 --> 00:32:55,000
Speaker 3: because it can do it faster and better. So but

641
00:32:55,120 --> 00:32:59,320
Speaker 3: every engineer I know right now that is uh that

642
00:32:59,320 --> 00:33:02,680
Speaker 3: that loves a I coding. They're not sleeping and they

643
00:33:02,680 --> 00:33:05,120
Speaker 3: are pushing out products daily.

644
00:33:05,320 --> 00:33:07,480
Speaker 2: Yeah, you know what I mean, and they love it.

645
00:33:07,680 --> 00:33:11,360
Speaker 3: And so it's just nice to see that that uh,

646
00:33:11,960 --> 00:33:12,520
Speaker 3: that change.

647
00:33:12,600 --> 00:33:15,480
Speaker 1: Yeah, And that's it's it's interesting you said that because

648
00:33:15,720 --> 00:33:17,640
Speaker 1: I did. I had something I wanted to say I

649
00:33:17,680 --> 00:33:20,040
Speaker 1: didn't know how to answer get a question around it. It

650
00:33:20,120 --> 00:33:23,200
Speaker 1: is the challenge that I have now being a semi

651
00:33:23,200 --> 00:33:25,360
Speaker 1: technical person like I can I can do front end.

652
00:33:25,360 --> 00:33:26,840
Speaker 1: I'm not a back end developer, but I can do

653
00:33:26,840 --> 00:33:29,840
Speaker 1: front end. I can do design. The challenge I've always

654
00:33:29,920 --> 00:33:32,440
Speaker 1: had is I've got these ideas and I can't get

655
00:33:32,440 --> 00:33:35,959
Speaker 1: them made fast enough because I need somebody else to

656
00:33:36,000 --> 00:33:38,960
Speaker 1: come on along and build the back end. Now, the

657
00:33:39,000 --> 00:33:42,200
Speaker 1: biggest challenge I have is I my eyes just will

658
00:33:42,200 --> 00:33:44,880
Speaker 1: close at some point. You know. It's just like I

659
00:33:45,520 --> 00:33:46,920
Speaker 1: don't want to go to sleep, but I have to

660
00:33:46,920 --> 00:33:49,480
Speaker 1: go to sleep because now I'm to be typing garbage

661
00:33:49,480 --> 00:33:52,440
Speaker 1: into this computer because I'm just so tired, because I

662
00:33:52,800 --> 00:33:54,960
Speaker 1: really just want to build stuff, and so I don't

663
00:33:55,000 --> 00:33:56,440
Speaker 1: even have a question around that. But I kind of

664
00:33:56,480 --> 00:33:58,880
Speaker 1: wanted you to just you know, yeah, no.

665
00:33:59,640 --> 00:34:02,880
Speaker 3: I literally had this conversation with someone the other day.

666
00:34:03,040 --> 00:34:07,080
Speaker 3: I said, you come through this moment where you're like, hey,

667
00:34:07,080 --> 00:34:08,640
Speaker 3: AI is not that big of a deal. You might

668
00:34:08,719 --> 00:34:11,560
Speaker 3: try it, But then there's people who pushed through that

669
00:34:12,000 --> 00:34:15,160
Speaker 3: and they finally get it. And when you finally when

670
00:34:15,200 --> 00:34:20,279
Speaker 3: it finally clicks for you, sleeping is very hard. It's

671
00:34:20,360 --> 00:34:22,440
Speaker 3: like and I mean like when I wake up in

672
00:34:22,440 --> 00:34:24,800
Speaker 3: the morning, I'm like straight to the computer.

673
00:34:24,920 --> 00:34:25,440
Speaker 1: So let's go.

674
00:34:25,560 --> 00:34:27,359
Speaker 3: I got something else. I mean, cause it's like I

675
00:34:27,400 --> 00:34:30,480
Speaker 3: am not I am not a coder. So through this example,

676
00:34:30,520 --> 00:34:32,760
Speaker 3: my cousin, he's a coder, he's been coding thirty years.

677
00:34:33,120 --> 00:34:35,600
Speaker 3: And I watched him he said when he picked it up,

678
00:34:35,600 --> 00:34:38,200
Speaker 3: he was like, I'm going to be coding this new

679
00:34:38,239 --> 00:34:40,040
Speaker 3: idea I have. He said, it'll probably take me like

680
00:34:40,080 --> 00:34:41,880
Speaker 3: three months. He called me three days.

681
00:34:41,760 --> 00:34:43,320
Speaker 2: Later said it's done.

682
00:34:43,360 --> 00:34:45,320
Speaker 3: And so I was like, well, can I try it?

683
00:34:45,360 --> 00:34:46,960
Speaker 3: And he's like, yeah, you should try it. And I

684
00:34:47,000 --> 00:34:48,239
Speaker 3: was like, well, what do I say? He's like, just

685
00:34:48,280 --> 00:34:50,400
Speaker 3: tell it. You don't know how to code. And so

686
00:34:50,480 --> 00:34:53,080
Speaker 3: I sat up there and I wanted to build this app.

687
00:34:53,200 --> 00:34:55,240
Speaker 3: This thing said do you want to build a macap

688
00:34:55,320 --> 00:34:56,759
Speaker 3: or do you want me to build a website? And

689
00:34:56,800 --> 00:34:59,520
Speaker 3: I was like, oh, let's build a macap and it

690
00:34:59,560 --> 00:35:03,000
Speaker 3: built it and so then Claud came out with this

691
00:35:03,120 --> 00:35:06,840
Speaker 3: non technical version of it called cloud cowork. And the

692
00:35:06,880 --> 00:35:09,560
Speaker 3: cloud Cowork thing was so cool because I'm not really

693
00:35:09,640 --> 00:35:13,800
Speaker 3: good at building graphs and airtable. I'm really good at airtable,

694
00:35:13,840 --> 00:35:15,560
Speaker 3: but I just haven't done the grass version. So I

695
00:35:15,560 --> 00:35:18,399
Speaker 3: have cloud Cowork. I said, can you show me how

696
00:35:18,440 --> 00:35:21,600
Speaker 3: to build graphs and airtable? And it gave me the instructions.

697
00:35:21,640 --> 00:35:24,120
Speaker 3: But then it said do you want me to build.

698
00:35:23,920 --> 00:35:24,399
Speaker 2: It for you?

699
00:35:24,760 --> 00:35:25,600
Speaker 1: Yeah?

700
00:35:25,640 --> 00:35:27,920
Speaker 3: And I said well yeah, and it said just give

701
00:35:27,960 --> 00:35:30,000
Speaker 3: me access to you to your browser.

702
00:35:30,040 --> 00:35:31,440
Speaker 2: I gave it access to my browser.

703
00:35:31,600 --> 00:35:34,040
Speaker 3: It opened up my browser and I watched it build

704
00:35:34,400 --> 00:35:39,680
Speaker 3: the graphs. So we're in this new time where I

705
00:35:39,719 --> 00:35:42,719
Speaker 3: saw another quote yesterday where it says like, you know,

706
00:35:43,920 --> 00:35:47,000
Speaker 3: first it was like computers and then humans using computers,

707
00:35:47,000 --> 00:35:49,560
Speaker 3: But what happens when computers use computers.

708
00:35:50,560 --> 00:35:56,480
Speaker 1: Be interesting? So outside of you know, we know the

709
00:35:56,719 --> 00:36:00,239
Speaker 1: ll ms, you know the clause the chat GPT use

710
00:36:00,280 --> 00:36:04,400
Speaker 1: the geminis in THETC, what is like the next iteration

711
00:36:04,600 --> 00:36:07,239
Speaker 1: of LLM, Because that's just one sector of AI. Like,

712
00:36:07,280 --> 00:36:10,160
Speaker 1: so is there a different part that you're also paying

713
00:36:10,200 --> 00:36:14,160
Speaker 1: attention to that is, you know, maybe not beyond the LLM,

714
00:36:14,200 --> 00:36:15,520
Speaker 1: but it's a different side of AI.

715
00:36:16,320 --> 00:36:20,920
Speaker 3: Yeah, I would say the one that I'm keeping an

716
00:36:20,960 --> 00:36:23,279
Speaker 3: eye on. I'm not paying attention to it, but I

717
00:36:23,320 --> 00:36:24,359
Speaker 3: peek in every now and then.

718
00:36:24,400 --> 00:36:25,160
Speaker 2: Are robots.

719
00:36:26,280 --> 00:36:30,279
Speaker 3: I don't think people understand how close we are to

720
00:36:30,719 --> 00:36:36,400
Speaker 3: having robots in our home that are good and because

721
00:36:36,400 --> 00:36:39,160
Speaker 3: they're just getting better and better as time goes on.

722
00:36:39,280 --> 00:36:41,960
Speaker 3: So if you think about it, you know, what does

723
00:36:42,600 --> 00:36:45,719
Speaker 3: chad GPT look like in a physical form, right in

724
00:36:45,760 --> 00:36:48,759
Speaker 3: a robot form, right where you're like, hey, I need

725
00:36:48,800 --> 00:36:51,239
Speaker 3: you to go wash the dishes. I need you to

726
00:36:51,280 --> 00:36:54,200
Speaker 3: go to the laundry and fold the laundry. Right, I

727
00:36:54,239 --> 00:36:56,560
Speaker 3: need you to teach my kids this subject. I don't

728
00:36:56,560 --> 00:36:59,799
Speaker 3: know if I'm gonna do that, but you could essentially right.

729
00:36:59,840 --> 00:37:03,279
Speaker 3: And so I think robotics is very very interesting when

730
00:37:03,320 --> 00:37:09,080
Speaker 3: you start putting these lms kind of in a physical format.

731
00:37:10,400 --> 00:37:13,560
Speaker 1: So I want to talk more about how you decide

732
00:37:13,600 --> 00:37:16,160
Speaker 1: what you use for what because we kind of talked,

733
00:37:16,200 --> 00:37:18,840
Speaker 1: we kind of vollied in this conversation of like I

734
00:37:18,920 --> 00:37:21,120
Speaker 1: might use chat for that, I might use claw for that.

735
00:37:21,520 --> 00:37:24,520
Speaker 1: So can you walk me through like a typical day

736
00:37:24,600 --> 00:37:27,680
Speaker 1: or weekend cliss life where you're making decisions on I've

737
00:37:27,719 --> 00:37:29,319
Speaker 1: got this thing coming up, I'm going to use this

738
00:37:29,440 --> 00:37:32,160
Speaker 1: tool for that. How do you make those decisions? Yeah?

739
00:37:32,760 --> 00:37:37,240
Speaker 2: It comes with them, with trial and error and sometimes

740
00:37:37,320 --> 00:37:38,439
Speaker 2: just mood. Right.

741
00:37:39,440 --> 00:37:41,200
Speaker 3: But so, like for today I had to work on

742
00:37:41,239 --> 00:37:43,440
Speaker 3: a statement of work. I use chat GBT for that

743
00:37:43,560 --> 00:37:46,920
Speaker 3: because I've done all my statements of work in chat gybt,

744
00:37:47,160 --> 00:37:50,600
Speaker 3: so it understands what I'm asking. Does it not mean

745
00:37:50,640 --> 00:37:53,800
Speaker 3: that I could build a statement of work thing in Claude?

746
00:37:54,160 --> 00:37:56,759
Speaker 3: Probably can, I just don't want to take the time

747
00:37:56,800 --> 00:37:59,279
Speaker 3: to do that. The other thing I'm thinking about constantly

748
00:37:59,640 --> 00:38:02,160
Speaker 3: as I you know, because I have my newsletter, I

749
00:38:02,200 --> 00:38:04,000
Speaker 3: have to stay on the latest and grades of what's

750
00:38:04,040 --> 00:38:04,840
Speaker 3: going on in AI.

751
00:38:06,200 --> 00:38:07,959
Speaker 2: You know, I find out about.

752
00:38:08,040 --> 00:38:11,279
Speaker 3: New technologies that I'm like, oh, I want to try

753
00:38:11,320 --> 00:38:11,600
Speaker 3: that out.

754
00:38:11,640 --> 00:38:12,920
Speaker 2: So, for example, perplexity.

755
00:38:13,680 --> 00:38:16,080
Speaker 3: Perplexity is another model out there, and I would say

756
00:38:16,200 --> 00:38:19,879
Speaker 3: they would be number five that's gotten really really good

757
00:38:19,920 --> 00:38:22,279
Speaker 3: over the last couple of weeks. It's always been good,

758
00:38:22,280 --> 00:38:23,799
Speaker 3: but it's got even better. A lot of people use

759
00:38:23,840 --> 00:38:27,480
Speaker 3: it for research. I actually have replaced Google Search with

760
00:38:27,600 --> 00:38:29,440
Speaker 3: Perplexity Search because I just find it to be a

761
00:38:29,440 --> 00:38:32,560
Speaker 3: little better. But one of the things they came out

762
00:38:32,600 --> 00:38:35,120
Speaker 3: with is this thing called the model Council. And what

763
00:38:35,200 --> 00:38:38,440
Speaker 3: the model council is is that when you give Perplexity

764
00:38:38,480 --> 00:38:43,040
Speaker 3: a prompt, it will call all of the four models

765
00:38:43,200 --> 00:38:45,759
Speaker 3: and they'll argue against each other for who has the

766
00:38:45,800 --> 00:38:51,520
Speaker 3: best answer. And so now imagine CHATGBT, Gemini co pilot

767
00:38:52,120 --> 00:38:57,520
Speaker 3: Claude getting the same prompt and then them figuring out

768
00:38:57,560 --> 00:38:59,600
Speaker 3: who has the best answer, and then you finally get

769
00:38:59,640 --> 00:39:04,360
Speaker 3: the best answer. That's a very interesting, you know concept

770
00:39:04,440 --> 00:39:07,840
Speaker 3: that Perplexity may win on because they're they're able to

771
00:39:07,880 --> 00:39:11,279
Speaker 3: do that they also just release the ability for you know,

772
00:39:11,360 --> 00:39:13,399
Speaker 3: kind of like claug Cowork, where it can take over

773
00:39:13,440 --> 00:39:16,279
Speaker 3: your computer and build things for you. So part of

774
00:39:16,320 --> 00:39:19,719
Speaker 3: my job is also wanting to test those things out.

775
00:39:19,840 --> 00:39:22,160
Speaker 3: And then when I test it out, there's this decision

776
00:39:22,200 --> 00:39:23,200
Speaker 3: that happens, is that.

777
00:39:23,680 --> 00:39:25,640
Speaker 2: One was this good? Uh?

778
00:39:25,800 --> 00:39:29,160
Speaker 3: Two was the was the output quality enough that is

779
00:39:29,160 --> 00:39:31,480
Speaker 3: better than what the output that I'm currently getting? And

780
00:39:31,520 --> 00:39:34,120
Speaker 3: then if so, integrated into the tool stack. If not,

781
00:39:34,719 --> 00:39:36,520
Speaker 3: move on to the move on to the next thing.

782
00:39:36,600 --> 00:39:40,680
Speaker 3: And so I mean I probably, I mean I'm overdoing it,

783
00:39:40,680 --> 00:39:43,440
Speaker 3: but I probably subscribe to at least twenty AI tools.

784
00:39:43,920 --> 00:39:46,360
Speaker 3: I am not loyal to any tool. I will cancel

785
00:39:46,600 --> 00:39:48,839
Speaker 3: the tool. If another tool comes out that's better, that's

786
00:39:48,880 --> 00:39:52,200
Speaker 3: brand new on the market, I'm I'm gonna try it out.

787
00:39:52,239 --> 00:39:55,239
Speaker 3: And so but you know, I kind of have my

788
00:39:55,560 --> 00:39:57,600
Speaker 3: the main ones that I that I use, and like

789
00:39:57,640 --> 00:40:00,960
Speaker 3: I said, these these big lms are get better, So

790
00:40:02,200 --> 00:40:04,000
Speaker 3: you can't go wrong with just paying for those.

791
00:40:04,320 --> 00:40:08,279
Speaker 1: Yeah, one more question about you know, when you had asked,

792
00:40:08,320 --> 00:40:10,239
Speaker 1: you know, you hope I'm using AI for these things,

793
00:40:10,239 --> 00:40:13,440
Speaker 1: and what I remember just six months ago, maybe is

794
00:40:14,239 --> 00:40:18,479
Speaker 1: the level of it just made up things that would

795
00:40:18,520 --> 00:40:20,880
Speaker 1: be in Chat or Claude or et cetera. And if

796
00:40:20,920 --> 00:40:23,319
Speaker 1: that's getting a lot better. It's not completely gone yet,

797
00:40:23,320 --> 00:40:27,040
Speaker 1: but it's a lot better. And I wonder how So,

798
00:40:27,520 --> 00:40:30,880
Speaker 1: for instance, I'm building a pizza company called Commons, so Commons,

799
00:40:31,239 --> 00:40:34,200
Speaker 1: I'm building the business plan. Let's say I'm using Claude.

800
00:40:34,400 --> 00:40:38,680
Speaker 1: And what I found remarkable now is as I'm building

801
00:40:38,719 --> 00:40:41,920
Speaker 1: the business plan, the questions are much more sophisticated that

802
00:40:42,000 --> 00:40:45,360
Speaker 1: it's asking me about how I thought about the business operation.

803
00:40:46,080 --> 00:40:50,920
Speaker 1: So because it's asking me these better questions, and I'm saying, okay,

804
00:40:51,040 --> 00:40:53,360
Speaker 1: give me an estimate on like what my performer should

805
00:40:53,360 --> 00:40:56,040
Speaker 1: look like in et cetera, do you can you speak

806
00:40:56,080 --> 00:41:01,759
Speaker 1: to how it's developing? The responses saying okay, you know,

807
00:41:02,000 --> 00:41:03,960
Speaker 1: at what price point are you going to sell pizzas?

808
00:41:03,960 --> 00:41:06,880
Speaker 1: And I say, okay, pizza's going to average be twelve

809
00:41:06,920 --> 00:41:10,200
Speaker 1: to seventeen dollars. I'm just make this up. Wait, what

810
00:41:10,280 --> 00:41:13,080
Speaker 1: information is it pulling from to be able to support

811
00:41:13,080 --> 00:41:15,960
Speaker 1: that for my market? And so not for LA, not

812
00:41:16,080 --> 00:41:18,879
Speaker 1: for New York, but in Ohio, where I'm at. How

813
00:41:19,000 --> 00:41:23,719
Speaker 1: is it giving me a market specific context versus what

814
00:41:23,840 --> 00:41:26,480
Speaker 1: it would if I was in a different market. Yeah,

815
00:41:26,520 --> 00:41:29,399
Speaker 1: And I think one is I'm asking does that make sense?

816
00:41:29,480 --> 00:41:31,400
Speaker 2: Yeaheah, yeah, I think. I think I'm gonna try to

817
00:41:31,400 --> 00:41:32,200
Speaker 2: answer it two ways.

818
00:41:32,239 --> 00:41:37,760
Speaker 3: One is, you know, AI is constantly trained on data

819
00:41:38,120 --> 00:41:40,560
Speaker 3: out there that's that's in the Internet, So it could

820
00:41:40,640 --> 00:41:43,000
Speaker 3: be that's something that is just standard already built in

821
00:41:43,080 --> 00:41:45,400
Speaker 3: and they know these things. The other thing is that

822
00:41:45,880 --> 00:41:48,680
Speaker 3: you know all this AI has access to the Internet,

823
00:41:48,760 --> 00:41:51,439
Speaker 3: so it could say, okay, well, I know you're based here,

824
00:41:51,520 --> 00:41:54,440
Speaker 3: so I'm gonna go pull all of the restaurants in

825
00:41:54,520 --> 00:41:55,080
Speaker 3: this area.

826
00:41:55,320 --> 00:41:58,480
Speaker 2: Now. It could be smart enough to do that, or

827
00:41:58,719 --> 00:42:02,800
Speaker 2: you could actually say, hey, pull everything in just this area.

828
00:42:02,840 --> 00:42:03,279
Speaker 2: I don't want to.

829
00:42:03,360 --> 00:42:06,840
Speaker 3: I don't want you to look at Nebraska pizza, California pizza.

830
00:42:07,280 --> 00:42:09,480
Speaker 3: I just want you to look at my city, my

831
00:42:09,680 --> 00:42:12,160
Speaker 3: zip code, the county.

832
00:42:12,480 --> 00:42:13,040
Speaker 2: You know you can.

833
00:42:13,080 --> 00:42:16,360
Speaker 3: You can do it however you want the state, and

834
00:42:16,360 --> 00:42:17,560
Speaker 3: and it'll go out and do.

835
00:42:17,560 --> 00:42:19,279
Speaker 2: That research research for you.

836
00:42:19,360 --> 00:42:22,040
Speaker 3: So I think it's like it just depends on if

837
00:42:22,040 --> 00:42:24,960
Speaker 3: you're just pulling it, because you know, AI models get better. So,

838
00:42:25,000 --> 00:42:27,759
Speaker 3: like I said, chat Gibt five point four came out

839
00:42:27,800 --> 00:42:31,759
Speaker 3: on Friday. It's either Thursday, or Friday last week. Five

840
00:42:31,760 --> 00:42:35,560
Speaker 3: point three came out on Monday. So it's getting it's

841
00:42:35,600 --> 00:42:39,160
Speaker 3: getting better and better, and they're not necessarily saying.

842
00:42:38,960 --> 00:42:41,160
Speaker 2: Hey, these are the things that we changed.

843
00:42:40,840 --> 00:42:43,359
Speaker 3: In this model, but they're just like try it out

844
00:42:43,600 --> 00:42:45,040
Speaker 3: and so, like, you know, one of the things that

845
00:42:45,160 --> 00:42:47,319
Speaker 3: chat Gibt came out with is five point four mile,

846
00:42:47,360 --> 00:42:50,120
Speaker 3: which I'm excited about is they now have an Excel

847
00:42:50,200 --> 00:42:53,640
Speaker 3: plug in where now you can use Excel and say, hey,

848
00:42:53,760 --> 00:42:58,600
Speaker 3: create a financial profile for my business and a profit

849
00:42:58,640 --> 00:42:59,240
Speaker 3: and laws.

850
00:42:59,000 --> 00:43:01,480
Speaker 2: Statement and do some estimate estimates.

851
00:43:01,480 --> 00:43:04,759
Speaker 3: And where are we losing you know, margins on, you know,

852
00:43:04,760 --> 00:43:07,440
Speaker 3: where are the opportunities that now chat gbt will be.

853
00:43:07,440 --> 00:43:08,560
Speaker 2: In your Excel file.

854
00:43:08,719 --> 00:43:12,640
Speaker 3: Yeah, and that's different. That just happened on Friday. And so,

855
00:43:12,760 --> 00:43:14,759
Speaker 3: like I said, it's getting it's getting better and better.

856
00:43:14,800 --> 00:43:17,040
Speaker 3: But yeah, a lot of information is just pulled either

857
00:43:17,080 --> 00:43:18,320
Speaker 3: from the web or from the model.

858
00:43:19,400 --> 00:43:21,440
Speaker 1: Lastly, only because it's top of mind. Just in our

859
00:43:22,080 --> 00:43:24,239
Speaker 1: current social discourse, there are a lot of people who

860
00:43:24,320 --> 00:43:27,480
Speaker 1: left open Ai canceled their subscriptions over the last two

861
00:43:27,520 --> 00:43:32,879
Speaker 1: weeks or so. Can you talk about decisions that get

862
00:43:32,920 --> 00:43:35,760
Speaker 1: made at those levels of to working with the government

863
00:43:35,920 --> 00:43:38,319
Speaker 1: not working with the government, and because you talked about

864
00:43:38,320 --> 00:43:40,640
Speaker 1: you're not romantic about any you're not loyal to any

865
00:43:40,680 --> 00:43:43,000
Speaker 1: of these. So can you talk about how we as

866
00:43:43,120 --> 00:43:46,799
Speaker 1: consumers people using these products can think about it now,

867
00:43:46,800 --> 00:43:48,560
Speaker 1: how we should think about it, but how can we

868
00:43:48,600 --> 00:43:52,160
Speaker 1: think about how we engage with these different platforms?

869
00:43:52,280 --> 00:43:56,640
Speaker 4: Yeah, yeah, I think that the Claude chat GBT government

870
00:43:56,760 --> 00:43:59,800
Speaker 4: issue is is I call it, it's above it's above

871
00:43:59,800 --> 00:44:02,560
Speaker 4: my pay grade, right unless I'm sitting in the pentagon.

872
00:44:03,120 --> 00:44:05,759
Speaker 3: I actually don't know what's going on. I actually heard

873
00:44:05,800 --> 00:44:09,719
Speaker 3: they used Claude in the strikes for Venezuela. And then

874
00:44:09,800 --> 00:44:13,319
Speaker 3: also I guess from what I can see is that

875
00:44:15,280 --> 00:44:20,600
Speaker 3: the government was mad that Claude had asked specifics about there.

876
00:44:21,000 --> 00:44:23,640
Speaker 3: I guess top secret information is what they were pissed

877
00:44:23,680 --> 00:44:26,200
Speaker 3: off about, and so they pulled out of the deal.

878
00:44:26,800 --> 00:44:31,759
Speaker 3: CHADGBT said no, we will do the deal. There's rumors that,

879
00:44:32,719 --> 00:44:35,400
Speaker 3: you know, that was a bad thing. There's also rumors

880
00:44:35,400 --> 00:44:38,520
Speaker 3: that it has has the same exact clause knowledge that

881
00:44:38,719 --> 00:44:42,120
Speaker 3: Chad or that Claude was using am I in that

882
00:44:42,239 --> 00:44:46,880
Speaker 3: legal coret room. Absolutely absolutely not. So I don't know

883
00:44:47,120 --> 00:44:51,160
Speaker 3: fully exactly what's going on, But I think it's you know,

884
00:44:51,400 --> 00:44:55,799
Speaker 3: I think we'll probably get to the point where we

885
00:44:55,920 --> 00:44:59,000
Speaker 3: will be able to run our own personal models on

886
00:44:59,040 --> 00:45:01,680
Speaker 3: our own laptop and we won't have to rely on

887
00:45:02,200 --> 00:45:04,839
Speaker 3: CHATEGBT someway. Maybe it's more of an open source thing

888
00:45:04,880 --> 00:45:06,560
Speaker 3: that we're that we're doing, so we're just kind of

889
00:45:06,600 --> 00:45:09,800
Speaker 3: in this beginning moment. But yes, I mean, you definitely

890
00:45:09,800 --> 00:45:11,800
Speaker 3: need to pay attention to the people who were building

891
00:45:11,840 --> 00:45:12,360
Speaker 3: your tools.

892
00:45:12,480 --> 00:45:12,640
Speaker 4: Right.

893
00:45:13,840 --> 00:45:16,080
Speaker 3: Some people love Elon and they use groc. Some people

894
00:45:16,120 --> 00:45:18,719
Speaker 3: hate Elon and they don't use rocks. Some people love

895
00:45:18,920 --> 00:45:21,799
Speaker 3: the government and they use Claude or they use tategbts.

896
00:45:21,800 --> 00:45:23,680
Speaker 3: Some people hate the government, say we're not use JGBT.

897
00:45:23,840 --> 00:45:25,440
Speaker 3: So I think it you know, you do have you

898
00:45:25,480 --> 00:45:28,520
Speaker 3: do have options. Always look at the CEO and what

899
00:45:28,560 --> 00:45:31,640
Speaker 3: their decisions are, uh, and pick which one you know

900
00:45:31,719 --> 00:45:33,399
Speaker 3: you want to you want to go with. But yeah,

901
00:45:33,440 --> 00:45:37,120
Speaker 3: it's definitely a complicated, uh situation, and you know you

902
00:45:37,160 --> 00:45:38,799
Speaker 3: can always ask AI to help you.

903
00:45:43,239 --> 00:45:46,440
Speaker 1: All right. So this we've only really scratched the surface

904
00:45:46,600 --> 00:45:49,720
Speaker 1: of the level of depth you have. And I tried

905
00:45:49,760 --> 00:45:52,319
Speaker 1: my best to give people a good taste of you know,

906
00:45:52,400 --> 00:45:56,279
Speaker 1: who the cliff is and So can you talk about

907
00:45:56,320 --> 00:45:59,759
Speaker 1: where people can hear more about what you got going on,

908
00:46:00,000 --> 00:46:02,520
Speaker 1: where they can subscribe to the newsletter, because that's probably

909
00:46:02,560 --> 00:46:06,120
Speaker 1: the most important thing it is for you, is that newsletters.

910
00:46:06,120 --> 00:46:08,000
Speaker 1: Because I get it and I'm like, this is the

911
00:46:08,000 --> 00:46:11,000
Speaker 1: best newsletter I get every week. Yeah, can you talk

912
00:46:11,000 --> 00:46:12,600
Speaker 1: about where people can keep up?

913
00:46:13,440 --> 00:46:17,200
Speaker 3: Yeah, So I would say the newsletter is number one.

914
00:46:17,239 --> 00:46:20,560
Speaker 3: It's in the website's newsletter dot cliff notes dot AI.

915
00:46:21,120 --> 00:46:23,760
Speaker 3: And the reason why I built that is that AI

916
00:46:24,320 --> 00:46:26,480
Speaker 3: is moving so fast and I had a feeling that

917
00:46:26,560 --> 00:46:29,240
Speaker 3: it was going to move faster, and so I built

918
00:46:29,239 --> 00:46:31,560
Speaker 3: this website or this newsletter where I said, I'm just

919
00:46:31,600 --> 00:46:33,680
Speaker 3: going to give you the ten to fifteen things you

920
00:46:33,680 --> 00:46:36,359
Speaker 3: need to know that happened this week, and also here's

921
00:46:36,400 --> 00:46:39,200
Speaker 3: a really good tool, one tool that you should play

922
00:46:39,239 --> 00:46:40,800
Speaker 3: with this week. And then I give you a prompt

923
00:46:40,800 --> 00:46:42,640
Speaker 3: as well. I added prompts in there that's been a

924
00:46:42,719 --> 00:46:45,000
Speaker 3: hit because if you don't know how to prompt, I'll

925
00:46:45,040 --> 00:46:47,680
Speaker 3: give you some ideas things you can actually use. And

926
00:46:48,120 --> 00:46:50,319
Speaker 3: then I would also say on LinkedIn, my LinkedIn is

927
00:46:50,360 --> 00:46:54,080
Speaker 3: just LinkedIn Cliffworley, where I do break down a lot

928
00:46:54,120 --> 00:46:57,000
Speaker 3: of the news on there as well, so I would

929
00:46:57,000 --> 00:46:58,440
Speaker 3: say those are the two best places

930
00:47:00,040 --> 00:47:03,440
Speaker 1: War So glad to be what you man appreciate you

931
00:47:03,560 --> 00:47:04,920
Speaker 1: absolutely m