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Speaker 1: Earners. What's up.

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Speaker 2: You ever walk into a small business and everything just

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Speaker 2: works like The checkout is fast, the receipts are digital,

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Speaker 2: tipping is a breeze, and you're out the door before

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Speaker 2: at a pop up market, or even one of our

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Speaker 2: merch partners, Square makes it easy for them to take payments,

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Speaker 2: manage inventory, and run their business with confidence, all from

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Speaker 2: one simple system. If you're a business owner or even

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Speaker 2: just thinking about launching something soon, Square is hands down

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Speaker 2: one of the best tools out there to help you start, run,

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Speaker 2: you time back so you can focus.

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Speaker 1: On what matters most Ready.

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Speaker 2: To see how Square can transform your business, visit Square

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Speaker 2: Let's Square handle the back end so you can keep

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Speaker 2: pushing your vision forward. This episode is brought to you

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Speaker 2: by P and C Bank, a lot of people think

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Speaker 2: podcasts about work are boring, and sure they definitely can be,

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Speaker 2: but understanding of professionals routine shows us how they achieve

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Speaker 2: P and C Bank National Association Member FDIC.

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Speaker 3: All right, yeah, yes, welcome back, ladies and gentlemen.

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Speaker 1: Eyl, Happy Thursday, Happy Thursday, thursdow Man.

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Speaker 4: This is round two of our new series that we

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Speaker 4: are trying out is educational live streams, Live in the Flesh.

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Speaker 3: We last week we did real estate.

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Speaker 1: Yes we did.

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Speaker 2: We had a anazing conversation. Shout out at MG Shout

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Speaker 2: to everybody that was in the chat that got to

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Speaker 2: witness that live and other people that watched it the

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Speaker 2: day after or even leading up to this event. So

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Speaker 2: it was an incredible one packed with information. I think

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Speaker 2: tonight is gonna be one of those nights.

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Speaker 3: So, yeah, it's gonna be one of those nice so tonight.

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Speaker 4: You know, it's one of these things that a lot

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Speaker 4: of people have been asking for for a long time

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Speaker 4: as far as artificial intelligence. And you know, we talked

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Speaker 4: about it a variety of different times on Market, Mondays,

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Speaker 4: even on EYL, but I think we had like a

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Speaker 4: thorough breakdown on what AI actually is from a very

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Speaker 4: easy to understand respective but more more importantly, how to

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Speaker 4: actually utilize AI for your own personal benefit as far

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Speaker 4: as your life, how to make money in your business,

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Speaker 4: how to you know, just be more efficient.

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Speaker 3: That's important.

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Speaker 4: A lot of time people talk about the negative size

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Speaker 4: of artificial intelligence, but obviously there's a lot of positive

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Speaker 4: things that you can do. So I think that this

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Speaker 4: is something that's obviously going to be very finally, considering

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Speaker 4: the times that we're in, this.

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Speaker 2: Is the talk of every room, every time we walk

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Speaker 2: into a room, and I think it's fitting, you know,

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Speaker 2: every time we meet somebody.

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Speaker 1: The awareness on AI it varies.

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Speaker 2: Some people have heard of it, don't really know, some

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Speaker 2: people kind of have heard of it, some people are

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Speaker 2: using it, some people don't even know what GBT stands for.

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Speaker 2: And so we've talked about it, like you said numerous

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Speaker 2: times about how we're using it, right, like you talk

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Speaker 2: about how you're using chat GBT nearly on a daily basis.

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Speaker 2: I know Ian sung about how it's made him an

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Speaker 2: even more efficient CEO for him. I've talked about how

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Speaker 2: I've been using it to help me when I'm investing,

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Speaker 2: but like people still haven't been saying, I've been using it,

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Speaker 2: Here's how I'm using it. So today hopefully we will

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Speaker 2: unpack some information so that the next time we run

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Speaker 2: into some people have conversations like I watched that and

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Speaker 2: this is how I'm using it.

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Speaker 1: That's the fact.

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Speaker 4: And we got an expert in the field, X, who

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Speaker 4: we got connected with through Van Jones and spoke at

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Speaker 4: Investmentest and X got one of the best receptions from

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Speaker 4: anybody that spoke at invest Fest, which is speaks a

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Speaker 4: lot because obviously there's a lot of true, very highly

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Speaker 4: regarded people at invest Fest. So without further ado, let's

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Speaker 4: let's bring X up and let's let's get this going.

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Speaker 1: Hey, Hey, what's going on? Evening man?

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Speaker 5: Even it's a pleasure to be here pleasure to be

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Speaker 5: back on this stage.

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Speaker 3: Pleasure to have you.

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Speaker 2: It's an absolute honor to have you. And like you said,

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Speaker 2: it's very rare. I think this might have been the

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Speaker 2: first time. As somebody was speaking on stage, our phones

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Speaker 2: started blowing up, like, who is this person? Who is

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Speaker 2: this woman? She is killing it? I need to meet

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Speaker 2: her as soon as she gets off the stage. That

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Speaker 2: must have happened four or five times in my phone.

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Speaker 2: I'm sure it happened in his phone as well. I

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Speaker 2: was watching. I'm like, oh, oh, oh, this is incredible,

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Speaker 2: So congrat you. It's an honor to have you here tonight.

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Speaker 2: Everybody's in for a tree.

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Speaker 5: Yeah. Man, it's an honor to be here. I feel

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Speaker 5: like as someone who's been in AI for you know,

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Speaker 5: almost twelve years now, this stuff comes like second nature

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Speaker 5: to me. But as it starts to go from AI

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Speaker 5: being behind the scenes to AI in our face, it's

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Speaker 5: critical that folks like me who understand this stuff come

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Speaker 5: back and give that game to our communities in a

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Speaker 5: way that's accessible but also you know, accessible from an

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Speaker 5: information standpoint, but also accessible from a cost standpoint. Right,

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Speaker 5: there's a lot of folks out there who will put

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Speaker 5: this knowledge behind money and may not have the same quality.

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Speaker 5: There's no guarantee. So it's an honor to be able

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Speaker 5: to be on EILS platform to put our people on

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Speaker 5: game ultimately.

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Speaker 4: And not let's get it. And you are award winning

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Speaker 4: AI expert. You know, you worked in AI policy at

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Speaker 4: UC Berkeley, worked with some companies that you might have

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Speaker 4: heard of, like Google and Microsoft's Real and variety of

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Speaker 4: other things. So you are actually qualified to speak on this.

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Speaker 4: You're not just somebody that we just randomly picked off

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Speaker 4: the street, right.

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Speaker 5: Nah, definitely not.

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Speaker 1: I mean.

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Speaker 5: I've been an engineer, like an actual software engineer for

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Speaker 5: you know, almost twenty years now, but I've been specializing

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Speaker 5: in AI for the past twelve. I'm not only like

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Speaker 5: someone who worked in industry. When I was at Microsoft

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Speaker 5: building AI, bringing it to big companies like at Google,

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Speaker 5: I worked across all their algorithms, helping to identify when

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Speaker 5: they were being biased or sexist or racist or messed

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Speaker 5: up towards our communities and coming up with fixes for that.

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Speaker 5: I created two research teams on my own. One of

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Speaker 5: them was around teaching AI how to measure who is

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Speaker 5: and isn't represented in content, so like how much is

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Speaker 5: someone who's more masculine presenting on screen versus someone who's

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Speaker 5: more feminine, and how much are they seeing versus speaking? Right,

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Speaker 5: So we would use that to like do diversity studies

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Speaker 5: of all of Google advertising, and we made those tools

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Speaker 5: available to like third party partners also created the skin

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Speaker 5: tone Research team at Google, which literally taught AI how

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Speaker 5: to see black and brown skin tones. So myself is

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Speaker 5: someone who is a high school dropout, a two time

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Speaker 5: college dropout. I'm a published researcher in this space. I've

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Speaker 5: created tools that now you know, major companies all over

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Speaker 5: the world, you specifically in AI. So I definitely hope

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Speaker 5: I know what I'm talking about. I know I don't

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Speaker 5: know everything, but I know I know what I'm talking about.

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Speaker 1: That's a fact.

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Speaker 3: So all right, let's get into this YouTube.

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Speaker 1: Like and share, please please do to everyone I check in.

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Speaker 4: Let's run this ub. This is very very important information.

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Speaker 4: So okay, we're gonna we're gonna get right to it.

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Speaker 4: We want to talk about like actual items and stuff

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Speaker 4: like that. But before, let's just spend a couple of

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Speaker 4: minutes just to kind of break down that you've been

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Speaker 4: working in AI for years, So tell us you know,

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Speaker 4: kind of give us the framework of artificial intelligence so

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Speaker 4: we can have an understanding before we go into actually

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Speaker 4: how to utilize it and make money from it.

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Speaker 5: All right, let's get into it. So can you guys

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Speaker 5: see my screen here?

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Speaker 1: Now? They can? They can see it now.

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Speaker 5: I don't need y'all to see all of that that

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Speaker 5: looks like Schrodinger's cat. This is a you know, a

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Speaker 5: conversation that I think is super important, and it's one

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Speaker 5: that I have with my clients day to day. And

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Speaker 5: I work with like big companies like Loril and Mozilla,

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Speaker 5: And it's kind of like what Van Jones said on

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Speaker 5: stage and investments. It's like nobody really understands this stuff, right,

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Speaker 5: So you might hear like AI, deep learning, machine learning,

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Speaker 5: and the large language models, all these different terms to

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Speaker 5: talk about AI. So I want to start off by

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Speaker 5: making and giving y'all my framework for how to think

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Speaker 5: about what artificial intelligence is. So first, AI is not

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Speaker 5: just one thing, right, just like when you think of

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Speaker 5: the word car, car is not one thing. You can

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Speaker 5: have a two door car, you could have a four

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Speaker 5: door car, you could have a sports car, you could

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Speaker 5: have a truck. And even though there are all these

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Speaker 5: you know, different types of cars, like whether they're electric

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Speaker 5: or whether they're gas. Even though they literally end up

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Speaker 5: different in the way that they look, all cars pretty

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Speaker 5: much do the same thing, which is that they get

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Speaker 5: you from point A to point B faster than you

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Speaker 5: would get there if you were walking right, unless you're

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Speaker 5: in LA traffic at for it. Similarly, artificial intelligence covers

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Speaker 5: a whole bunch of different tools and technology across different disciplines.

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Speaker 5: It touches tools that are being built in computer science

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Speaker 5: and mathematics and electrical engineering and mechanical engineering. All AI

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Speaker 5: aims to do pretty much the same thing. Artificial intelligence

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Speaker 5: is trying to teach machines how to think and act

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Speaker 5: like humans. So all those different technologies and all those

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Speaker 5: different disciplines are just trying to teach machines how to

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Speaker 5: think and act like humans. Well, what does it mean

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Speaker 5: to think and act like a human? Well, humans have

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Speaker 5: five senses. We have hearing, we have sight, we have touched,

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Speaker 5: we have smell, and we have taste. And artificial intelligence

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Speaker 5: kind of has five domains. So teaching an AI, teaching

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Speaker 5: a machine how to hear and how to speak is

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Speaker 5: called natural language processing. That's what's behind tools like SyRI

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Speaker 5: inside of your phone. When you talk to her and

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Speaker 5: she writes the text down, that's called text to speech.

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Speaker 5: Then when she understands what you just said to her,

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Speaker 5: that's called natural language understanding. Then when she comes up

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Speaker 5: with a response to you, that's called natural language generation.

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Speaker 5: And then when she reads that response back I aloud

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Speaker 5: to you, that's called text to speech right where she

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Speaker 5: reads it out loud to you. So NLP, or natural

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Speaker 5: language processing, is teaching machines how to hear and how

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Speaker 5: to speak. Teaching machines how to see is called computer vision.

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Speaker 5: So if you have a ring camera in your house

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Speaker 5: and it's like, oh, person detected or animal detected, that's

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Speaker 5: using computer vision. That's also behind stuff like face ID

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Speaker 5: inside of your phone. When you go to the airport

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Speaker 5: and TSA asks you to stand there and it checks

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Speaker 5: the picture of your face. All of that stuff is

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Speaker 5: computer vision. Teaching a machine how to touch is called haptics. Now,

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Speaker 5: this is a field that you mostly see in robotics,

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Speaker 5: where they're doing things like creating artificial skin that's as

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Speaker 5: sensitive as human skin. Now why would they need that? Well,

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Speaker 5: think about it. If you have like a robot and

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Speaker 5: an Amazon factory. You want it to be able to

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Speaker 5: understand things like pressure and weight, so that when it

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Speaker 5: picks up whatever you ordered off an Amazon that might

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Speaker 5: be fragile, it doesn't pick it up and like shake

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Speaker 5: it around and mishandle it. Right. So they need that

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Speaker 5: artificial skin to be able to teach robots how to

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Speaker 5: understand what it is that they're physically touching and grabbing.

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Speaker 5: Then teaching machines how to smell is a pretty new field.

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Speaker 5: It's called machine oh factory. Now, I'm not gonna lie.

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Speaker 5: I don't really like this field because in order to

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Speaker 5: teach the algorithm what a smell is, you got to

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Speaker 5: use like somebody's nose as an example. And I don't

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Speaker 5: know about y'all, but some people out of the colognes

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Speaker 5: they be picking the perfuelings, they be picking like I

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Speaker 5: don't trust everybody's nose, right, And so machine ol factory

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Speaker 5: is a newer field. We've seen like maybe ten twenty

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Speaker 5: research papers in this space altogether, where they're trying to

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Speaker 5: do stuff like come up with new perfume smells or

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Speaker 5: like detect smoke from a smell, et cetera. And then

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Speaker 5: teaching machines how to taste doesn't actually have a fancy name,

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Speaker 5: but it has the same problem of kind of like

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Speaker 5: teaching a machine how to smell, Like, I'm not eating

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Speaker 5: at everybody's house, So whose taste buds are you using

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Speaker 5: as the example to like teach these machines how to taste?

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Speaker 2: You feel me definitely got you there, So that the

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Speaker 2: five sensors in the five domain makes a lot of

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Speaker 2: sense when people start hearing these things like computer vision.

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Speaker 2: Definitely that the smell and the taste, it is subjective.

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Speaker 2: It starts to lead to uncertainty. And one of the

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Speaker 2: things with human nature is that we don't like uncertainty,

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Speaker 2: which then leads to fear. So how can people become

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Speaker 2: less fearful when they when they hear about AI and

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Speaker 2: very broken down? How you just did it?

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Speaker 5: So I feel like a lot of the fear is

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Speaker 5: based on the unknown. Like the human brain generally is

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Speaker 5: wired to protect you from danger, right, Like the amygdala,

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Speaker 5: which we call the fight or flight part of the brain,

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Speaker 5: is literally the oldest part of the brain and it's

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Speaker 5: the only part of the brain that can hijack the rest.

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Speaker 5: So if you ever had like a panic attack, that's

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Speaker 5: literally your amygdala hijacking in your brain and shutting down

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Speaker 5: all the other different functions in your brain because it

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Speaker 5: literally thinks that you're dying. And so a huge part

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Speaker 5: of overcoming fear to AI is learning about it, doing

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Speaker 5: things like sitting in this class, taking notes, understanding what

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Speaker 5: the difference between NLP and computer vision, so that you're

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Speaker 5: able to not feel like because you don't understand it,

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Speaker 5: that it instantly means that it's going to be negative

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Speaker 5: against you. Now, there are real challenges with AI, with

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Speaker 5: which we'll probably get into later. But even then, the

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Speaker 5: problems inside of these AI systems, with things like bias

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Speaker 5: or things like them coming for you know, jobs that

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Speaker 5: particularly affect our communities, A lot of that stuff can

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Speaker 5: be solved if we step up in other areas, like

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Speaker 5: in policy, or if we step up in terms of

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Speaker 5: like regulation, or if we step up in terms of

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Speaker 5: building and designing our own systems. And so even the

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Speaker 5: stuff out there that is actually proven to be scary

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Speaker 5: or dangerous to our communities, there are solutions out there

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Speaker 5: that require all of us coming together and putting into play.

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Speaker 4: Okay, all right, so I know we have the next step,

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Speaker 4: which we're going to talk about how to utilize it

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Speaker 4: in business, anything you need you want to talk about

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Speaker 4: before that before we go to the business Ope.

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Speaker 5: Yeah, So before we jump into the business side, I

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Speaker 5: want to give a little bit more context about, you know,

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Speaker 5: how you teach a machine to think and act like

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Speaker 5: a human. So we know that AI is this group

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Speaker 5: of technologies that are trying to do that using this

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Speaker 5: five Census example. Now, generative AI specifically is attempting to

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Speaker 5: teach AI teach machines how to think and act like

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Speaker 5: the human imagination. Like, if I ask everybody on a

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Speaker 5: chat right now to imagine a car, you would come

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Speaker 5: up with an image in your head of a car.

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Speaker 5: Some of you guys would imagine a two door car,

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Speaker 5: a four door car, a sports car. Some of y'all

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Speaker 5: might even imagine a semi truck. Now, what I did

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Speaker 5: is I just prompted you to create something from the

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Speaker 5: prompt that I gave you. You're using your human imagination to

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Speaker 5: create something from an idea of it. And so generative

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Speaker 5: AI is teaching machines how to think and act more

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Speaker 5: along the lines of a human imagination. And I'm gonna

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Speaker 5: get a little bit technical just so you guys understand

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Speaker 5: some of the differences between the techniques used to build

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Speaker 5: AI and AI itself. So how do you teach a

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Speaker 5: machine how to think and act like a human? Well,

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Speaker 5: that's where the term machine learning comes in. You've probably

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Speaker 5: heard this a lot. Machine learning is the most popular

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Speaker 5: way that people teach machines how to think and act

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Speaker 5: like humans, but it's not the only way or the

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Speaker 5: only method. And machine learning is when you teach a machine,

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Speaker 5: which they call training, how to complete a task by

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Speaker 5: giving it examples which they call training data of that

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Speaker 5: task so that it can learn and improve itself on

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Speaker 5: how to do that task right. So, for example, if

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Speaker 5: I wanted to teach a car how to drive on

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Speaker 5: the road, if it was old school coding without machine learning,

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Speaker 5: I'd have to code every single step that the car

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Speaker 5: needed to do well. A lot of those rules are

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Speaker 5: you know, there's like millions of rules of driving, like

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Speaker 5: to have the code like if your right tire gets

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Speaker 5: within this many inches of the line, then move this

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Speaker 5: steering will you know point two centimeters. That's a lot

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Speaker 5: to learn and a lot to code. We probably wouldn't

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Speaker 5: even be able to make an app that would be

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Speaker 5: able to let the car I liked that it was

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Speaker 5: just take too much code. So instead, with machine learning,

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Speaker 5: we can take a bunch of examples of cars driving

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Speaker 5: on the road. That's why Tesla's have eight cameras. They're

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Speaker 5: constantly capturing that data, and then let it learn from

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Speaker 5: those examples of things that it should do and things

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Speaker 5: that it shouldn't do. So there are two types of

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Speaker 5: machine learning. The first is called supervised machine learning, and

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Speaker 5: supervised machine learning is when you teach a machine how

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Speaker 5: to complete a task by giving it examples of the

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Speaker 5: right answer up front. So an example of this is like,

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Speaker 5: let's say I wanted to teach a computer vision algorithm

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Speaker 5: to be able to tell the difference between a strawberry

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Speaker 5: and avocado. Right, I would give it examples of images

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Speaker 5: that I knew upfront were a strawberry. I would tell

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Speaker 5: it upfront that it's a strawberry. We called it that

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Speaker 5: data labeling, and then I would check it as it's

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Speaker 5: learning how good it gets at telling me that's a

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Speaker 5: strawberry versus telling me that's an avocado. Right. So again,

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Speaker 5: supervised machine learning is already know what the right answer

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Speaker 5: is up front, So I'm gonna give you these examples

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Speaker 5: and then I'm gonna test you against how well you're

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Speaker 5: learning how to do the task, because I already know

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Speaker 5: the right answer. The second type of AI machine learning

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Speaker 5: is called unsupervised machine learning, and this is when you're

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Speaker 5: training a machine how to complete a task by getting

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Speaker 5: given it examples, but you're letting the algorithmic self discover

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Speaker 5: what the right answer is. Right, So you're giving it

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Speaker 5: a whole bunch of data, and then it goes in

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Speaker 5: and finds patterns across that data that it uses to

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Speaker 5: give you the right answer. So, for example, a lot

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Speaker 5: of AI is being used in things like drug discovery,

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Speaker 5: So how can we find a new medicine to treat

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Speaker 5: AIDS or to treat diabetes, or even when COVID came out,

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Speaker 5: there was a whole effort between a bunch of big

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Speaker 5: tech companies to figure out medicines that could be used

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Speaker 5: as a vaccine. Right, So in that case, they didn't

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Speaker 5: know the right answer up front, but they had a

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Speaker 5: whole bunch of data and then the machine uncovered patterns

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Speaker 5: that help them understand what the right answer should be. Right,

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Speaker 5: all right, So then you have one more thing, which

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Speaker 5: is called deep learning. So deep learning is basically without

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Speaker 5: going into all the math behind it. It's a more

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Speaker 5: complex way of doing machine learning when you have things

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Speaker 5: that are more complicated in their patterns, and so it

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Speaker 5: teaches the algorithms in a way that mimics how neurons

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Speaker 5: fire in the human brain. Right, So machine learning, like

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Speaker 5: if I want to tell the difference between the strawberry

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Speaker 5: and an avocado, that's not a very complex task. But

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Speaker 5: like teaching a plane how to fly itself, like the

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Speaker 5: US Air Force just did, is going to require a

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Speaker 5: lot more complex relationships and a lot more complex understanding

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Speaker 5: and nuance inside of that data and the patterns and

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Speaker 5: how it's supposed to do that task. So they would

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Speaker 5: use something like a deep learning algorithm instead to be

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Speaker 5: able to capture that. Now, I know that was a

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Speaker 5: lot of information. I don't expect y'all to have this memorized.

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Speaker 5: So while before we move on to the next part,

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Speaker 5: I made a little TLDR slide for y'all. You know,

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Speaker 5: take a moment, take a screenshot, make sure to share

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Speaker 5: this with your friends that just reac perhaps what I

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Speaker 5: just covered X quick question.

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Speaker 2: When we're talking about GBT, which is generative pre trained transformers,

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Speaker 2: most people just hear chat GBT, and that's what the

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Speaker 2: acronym stands for. Is it encompassing all three? Is it

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Speaker 2: encompassing supervised, unsupervised and deep learning? Because when you're talking

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Speaker 2: about figuring out equations, I remember when it first was

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Speaker 2: able to pass you know, a high school exam and

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Speaker 2: ap exam, right, and then it kept learning, kept learning.

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Speaker 2: Then I was able to pass a graduate school final.

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Speaker 2: Then it was able to pass the bar. So it's learning,

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Speaker 2: it's not given the task, it's not it's untrained, but

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Speaker 2: some prompts are trained.

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Speaker 1: Where where would GBT fit in that category?

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Speaker 5: So CHAD GBT is a type of deep learning. And

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Speaker 5: then sometimes they have what's called reinforcement learning, which is

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Speaker 5: where it learns as it interacts with humans. But CHAD

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Speaker 5: GBT in particular is based on something called a transformer architecture.

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Speaker 5: So this is the way that like CHAD GBT works. Basically,

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Speaker 5: you get a large corpus of texts. So like you know,

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Speaker 5: they're complaining, Oh, they scraped the whole Internet and stuck

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Speaker 5: it inside of chad GPT, and they like took copyrighted

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Speaker 5: books and movies, et cetera. They got together a big

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Speaker 5: corpus of texts, right, So gigabytes and gigabytes upon terra

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Speaker 5: pts of text data. And then what they did is

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Speaker 5: they fed that into the system. And the first thing

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Speaker 5: that that system does is it starts to go across

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Speaker 5: the text and create these webs of understanding. And so

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Speaker 5: what it does is it'll say, like, oh, for the

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Speaker 5: like let's say it's a bunch of fairy tale books, right,

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Speaker 5: It'll say, oh, for the concept of royalty, we have

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Speaker 5: the words prince, princess castle, we have the words like

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Speaker 5: I don't know, like mote, you know, holiday, ball, Cinderella, right, Like,

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Speaker 5: it'll start to associate certain words with certain themes, and

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Speaker 5: those are called embeddings, right, and the machine is generating

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Speaker 5: these on their own. It's the first step of creating

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Speaker 5: like a CHADGBT. And then what happens is when you

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Speaker 5: go in and you create a prompt, right, so you

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Speaker 5: ask it something, what it does is it takes your

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Speaker 5: prompt and it converts it into math. You might have

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Speaker 5: heard this referred to as a token, and so it

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Speaker 5: converts your actual text into a mathematical representation of that text.

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Speaker 5: Because the way that the embeddings are stored or how

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Speaker 5: it understands language is actually stored using math. It's not

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Speaker 5: like a literal web where you could go like royalty

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Speaker 5: means princess. It's like some crazy hash and then is

435
00:22:27,320 --> 00:22:30,400
Speaker 5: related to these other hash numbers. Right. So then when

436
00:22:30,440 --> 00:22:33,520
Speaker 5: it takes your prompt and it converts it into text,

437
00:22:33,920 --> 00:22:37,600
Speaker 5: what it does is it does this really complex calculation

438
00:22:38,160 --> 00:22:41,480
Speaker 5: to guess what words should come next based on what

439
00:22:41,600 --> 00:22:45,000
Speaker 5: words came before, based on how those embeddings are inside

440
00:22:45,000 --> 00:22:47,600
Speaker 5: of his database. So I'll give you an example of this.

441
00:22:48,040 --> 00:22:50,680
Speaker 5: Let's say you don't speak a lick of Spanish at all. Right,

442
00:22:50,680 --> 00:22:53,879
Speaker 5: you don't understand Spanish at all, but you've maybe watched

443
00:22:53,880 --> 00:22:56,159
Speaker 5: a TV show once or twice, and you know that

444
00:22:56,200 --> 00:22:59,280
Speaker 5: when somebody says o la, you're supposed to say como

445
00:22:59,359 --> 00:23:02,399
Speaker 5: a stas, Right, And so someone comes up to you

446
00:23:02,480 --> 00:23:04,680
Speaker 5: and they say, oh lah, you think the most likely

447
00:23:04,760 --> 00:23:07,720
Speaker 5: response is probably como estas. But then if I go

448
00:23:07,800 --> 00:23:11,160
Speaker 5: deeper and I say donde edes and you you've never

449
00:23:11,240 --> 00:23:13,600
Speaker 5: heard that before, you gonna make up a response like

450
00:23:13,640 --> 00:23:18,120
Speaker 5: your ketto taco bell or say something that they don't have. Yeah,

451
00:23:18,160 --> 00:23:21,360
Speaker 5: because you don't unders, you don't understand the underlying language

452
00:23:21,400 --> 00:23:24,320
Speaker 5: and that's literally how chat GPT works. There's a research

453
00:23:24,359 --> 00:23:28,040
Speaker 5: paper called chat GPT is Bullshit, which literally talks about

454
00:23:28,040 --> 00:23:30,840
Speaker 5: the fact that all of these large language models based

455
00:23:30,880 --> 00:23:35,480
Speaker 5: on the transformer architecture literally do not understand language. They

456
00:23:35,520 --> 00:23:38,639
Speaker 5: don't actually understand the words that you're saying. They're just

457
00:23:38,840 --> 00:23:42,000
Speaker 5: really good math guessing engines because they have like a

458
00:23:42,040 --> 00:23:44,720
Speaker 5: lot of examples in this case of like maybe they've

459
00:23:44,720 --> 00:23:47,560
Speaker 5: watched a whole bunch of Spanish and memorized like what

460
00:23:47,600 --> 00:23:50,040
Speaker 5: people said and how they responded, but they don't actually

461
00:23:50,200 --> 00:23:52,960
Speaker 5: understand what they're saying, and so what that creates the

462
00:23:53,040 --> 00:23:56,359
Speaker 5: risk of is what we call hallucination. So if I

463
00:23:56,400 --> 00:23:58,880
Speaker 5: say donde edis and you say your ketto taco bell,

464
00:23:59,000 --> 00:24:01,960
Speaker 5: that's like the equivalent of a hallucination where you say

465
00:24:01,960 --> 00:24:04,679
Speaker 5: something that doesn't make sense or you make up something

466
00:24:04,680 --> 00:24:08,719
Speaker 5: that isn't factually true. And hallucinations inside of large language

467
00:24:08,760 --> 00:24:12,600
Speaker 5: models can happen at any point, right, Like, you cannot

468
00:24:12,600 --> 00:24:15,520
Speaker 5: get rid of hallucinations inside of large language models, so

469
00:24:15,560 --> 00:24:17,840
Speaker 5: they're always gonna make shit up. And an example it

470
00:24:17,880 --> 00:24:21,960
Speaker 5: is when Google dropped their AI overview, there was a

471
00:24:21,960 --> 00:24:25,000
Speaker 5: bunch of like funny screenshots of like how it was

472
00:24:25,040 --> 00:24:27,880
Speaker 5: making up answers and someone asked it like, yo, how

473
00:24:27,920 --> 00:24:30,160
Speaker 5: many rocks should a human eat? A data like keep

474
00:24:30,160 --> 00:24:32,359
Speaker 5: a balanced diet, and it was like, according to a

475
00:24:32,400 --> 00:24:35,840
Speaker 5: research study at U see Berkeley, humans should you know,

476
00:24:35,880 --> 00:24:38,840
Speaker 5: eat five rocks every day? And like that doesn't exist.

477
00:24:38,880 --> 00:24:41,720
Speaker 5: But again, it doesn't understand how harmful that is because

478
00:24:41,720 --> 00:24:45,680
Speaker 5: it doesn't actually understand the language. So two things. Whenever,

479
00:24:45,880 --> 00:24:49,440
Speaker 5: just general tips, whenever you're using a large language model

480
00:24:49,480 --> 00:24:54,640
Speaker 5: like chad, GPT, Gemini, Perplexity, Claude, any of these things,

481
00:24:54,680 --> 00:24:56,520
Speaker 5: you want to be careful when you're using it in

482
00:24:56,560 --> 00:24:59,439
Speaker 5: a fact based scenario. So if you're like plugging in

483
00:24:59,480 --> 00:25:02,720
Speaker 5: your company the data and asking chat GPT questions about it,

484
00:25:02,840 --> 00:25:04,879
Speaker 5: there's always a chance that it could make up an

485
00:25:04,960 --> 00:25:08,080
Speaker 5: answer that's not actually inside the data. So when you're

486
00:25:08,160 --> 00:25:11,320
Speaker 5: using these llms inside of these contexts, you want to

487
00:25:11,359 --> 00:25:14,840
Speaker 5: always trust, but verify, like double check and make sure

488
00:25:14,880 --> 00:25:16,159
Speaker 5: that it's given the right answer.

489
00:25:17,080 --> 00:25:23,359
Speaker 3: There you have it, ladies and gentlemen, Oh yeah, you know.

490
00:25:24,480 --> 00:25:26,800
Speaker 5: So if you guys want to dive deeper into those concepts,

491
00:25:26,800 --> 00:25:28,679
Speaker 5: I have a free AI course that I launched in

492
00:25:28,680 --> 00:25:30,760
Speaker 5: twenty twenty that goes a little bit deeper into some

493
00:25:30,800 --> 00:25:33,440
Speaker 5: of those mechanics. You guys can feel free to tap

494
00:25:33,480 --> 00:25:34,560
Speaker 5: into that for sure.

495
00:25:34,640 --> 00:25:35,400
Speaker 1: There you have it.

496
00:25:35,880 --> 00:25:40,359
Speaker 4: Okay, let's reset the room, hit the like button, and shit.

497
00:25:40,400 --> 00:25:43,200
Speaker 4: I mean this is information that literally you can learn

498
00:25:43,200 --> 00:25:46,640
Speaker 4: in college, high level information like high high level colleges,

499
00:25:46,720 --> 00:25:49,840
Speaker 4: not just your community college, and those schools cost a

500
00:25:49,880 --> 00:25:53,360
Speaker 4: lot of money. So it's free tonight just because we want.

501
00:25:53,440 --> 00:25:56,760
Speaker 4: We want to help you guys learn, and we want

502
00:25:56,760 --> 00:25:59,600
Speaker 4: to learn ourselves too, So I think it's very important

503
00:25:59,640 --> 00:26:01,879
Speaker 4: that we have that level of appreciation to our guests

504
00:26:01,920 --> 00:26:06,480
Speaker 4: absolutely for coming and providing information, but also the only

505
00:26:06,520 --> 00:26:08,240
Speaker 4: thing we ask you, guys is to share it. Twenty

506
00:26:08,280 --> 00:26:10,080
Speaker 4: two hundred people, that's great, but let's get it up

507
00:26:10,080 --> 00:26:13,080
Speaker 4: to three thousand. Let's shed this out to your friends,

508
00:26:13,080 --> 00:26:15,879
Speaker 4: your family. It's very important information, man. You know, we

509
00:26:15,880 --> 00:26:18,560
Speaker 4: we gossip on social media so much, but this is

510
00:26:18,640 --> 00:26:21,240
Speaker 4: actually more important than trending.

511
00:26:20,880 --> 00:26:22,240
Speaker 1: Topics, any topic.

512
00:26:23,480 --> 00:26:26,160
Speaker 4: So now we're going to go into Okay, let's get

513
00:26:26,200 --> 00:26:28,200
Speaker 4: into how we can actually make money. There's a lot

514
00:26:28,200 --> 00:26:34,160
Speaker 4: of entrepreneurs in here, so let's let's talk about that.

515
00:26:34,080 --> 00:26:34,520
Speaker 1: If we can.

516
00:26:35,680 --> 00:26:39,920
Speaker 5: Yeah, So the question is, how can you use AI right,

517
00:26:40,520 --> 00:26:42,359
Speaker 5: and so I came prepared, I got the debt ready

518
00:26:42,640 --> 00:26:44,960
Speaker 5: for these questions for y'all. And so there are three

519
00:26:45,000 --> 00:26:47,600
Speaker 5: ways that you can use AI, whether that's in your

520
00:26:47,640 --> 00:26:50,639
Speaker 5: business or in your personal life. The first is task

521
00:26:50,800 --> 00:26:53,960
Speaker 5: specific tools. So if there's a specific thing that you're

522
00:26:54,520 --> 00:26:58,359
Speaker 5: trying to do and create, there's probably an AI for that.

523
00:26:58,480 --> 00:27:00,440
Speaker 5: And in a chat I'm a drop of website where

524
00:27:00,440 --> 00:27:03,320
Speaker 5: you can go find an AI that can basically do anything.

525
00:27:03,800 --> 00:27:06,960
Speaker 5: The second is through AI enabled platforms, so platforms that

526
00:27:07,000 --> 00:27:11,520
Speaker 5: are specifically built to help you, you know, whether it's

527
00:27:11,600 --> 00:27:14,240
Speaker 5: operate your whole business or manage a lot of tasks

528
00:27:14,240 --> 00:27:16,840
Speaker 5: at once, that use AI in that. And the third

529
00:27:16,920 --> 00:27:19,640
Speaker 5: is around using AI to amplify your genius. So first

530
00:27:19,680 --> 00:27:22,879
Speaker 5: let's talk about these task specific tools, right. So, if

531
00:27:22,880 --> 00:27:25,159
Speaker 5: you're looking at marketing your business, right, you want to

532
00:27:25,280 --> 00:27:28,560
Speaker 5: use AI to identify and engage your customers. There's a

533
00:27:28,600 --> 00:27:30,560
Speaker 5: couple of different ways you can use AI for that.

534
00:27:30,640 --> 00:27:33,919
Speaker 5: The first is around content creation. You can accelerate the

535
00:27:33,960 --> 00:27:37,720
Speaker 5: development of either text, audio, or video content to promote

536
00:27:37,720 --> 00:27:40,679
Speaker 5: your business. Right. So, whether that's yo, I'm trying to

537
00:27:40,680 --> 00:27:43,520
Speaker 5: come up with a new jingle to put behind you know,

538
00:27:43,600 --> 00:27:46,800
Speaker 5: my advertisement, you could pull a bbl jizzy and use

539
00:27:46,840 --> 00:27:49,800
Speaker 5: Asuno or an udio to do that. If you're looking

540
00:27:49,920 --> 00:27:53,840
Speaker 5: for creating images of people stock photos without having to

541
00:27:53,880 --> 00:27:56,440
Speaker 5: pay for them, you can use free tools like mid

542
00:27:56,480 --> 00:27:59,280
Speaker 5: Journey or free tools like Dolly or a number of

543
00:27:59,320 --> 00:28:03,240
Speaker 5: websites that allow you to generate images for free. If

544
00:28:03,240 --> 00:28:06,840
Speaker 5: you're looking at trying to advertise to your customers, right, so,

545
00:28:07,000 --> 00:28:10,640
Speaker 5: like customizing those ad campaigns, what I recommend for this

546
00:28:10,920 --> 00:28:14,439
Speaker 5: is that when you're doing advertising and you want to

547
00:28:14,480 --> 00:28:17,920
Speaker 5: personalize it to your customers or find the right audience,

548
00:28:18,280 --> 00:28:22,080
Speaker 5: all of these big advertising platforms have now in the

549
00:28:22,200 --> 00:28:27,680
Speaker 5: last like six seven months, have now dropped AI audience features, right, So,

550
00:28:27,840 --> 00:28:32,560
Speaker 5: whether that's TikTok advertising, whether that's Facebook Meta advertising, whether

551
00:28:32,600 --> 00:28:35,840
Speaker 5: that's Google advertising, they have these features that do cost

552
00:28:35,840 --> 00:28:39,160
Speaker 5: a little bit more, but they use AI to identify

553
00:28:39,280 --> 00:28:42,800
Speaker 5: the exact people that you need to advertise to based

554
00:28:42,800 --> 00:28:45,440
Speaker 5: on your campaign. And they'll start off maybe the first

555
00:28:45,560 --> 00:28:48,480
Speaker 5: day testing that out, and as they get people using,

556
00:28:48,520 --> 00:28:52,080
Speaker 5: you know, responding to the ads positively, they keep adjusting

557
00:28:52,160 --> 00:28:55,560
Speaker 5: in real time who they're targeting your ad campaign to.

558
00:28:56,000 --> 00:28:59,080
Speaker 5: So I recommend using those features they are. They do

559
00:28:59,200 --> 00:29:01,920
Speaker 5: cost a little bit more, but it's much better for

560
00:29:01,960 --> 00:29:04,120
Speaker 5: you to spend that money up front and guarantee that

561
00:29:04,160 --> 00:29:06,800
Speaker 5: it's going to reach the right people versus you spending

562
00:29:06,840 --> 00:29:09,239
Speaker 5: money running a campaign trying to figure it out and

563
00:29:09,320 --> 00:29:11,560
Speaker 5: manually adjust it that could end up costing you a

564
00:29:11,600 --> 00:29:15,760
Speaker 5: lot more moving forward. And the third thing is around sentiment, right.

565
00:29:16,240 --> 00:29:19,520
Speaker 5: So sentiment is essentially how people feel, and they measure

566
00:29:19,560 --> 00:29:22,120
Speaker 5: that through text. So if you're a company that has

567
00:29:22,280 --> 00:29:24,680
Speaker 5: a review based business, right, like let's say you got

568
00:29:24,720 --> 00:29:28,320
Speaker 5: Google Reviews, or you have Yelp reviews, et cetera. All

569
00:29:28,360 --> 00:29:31,400
Speaker 5: of those platforms that let you put all your reviews

570
00:29:31,440 --> 00:29:34,080
Speaker 5: in one place and like review and manage all of them,

571
00:29:34,360 --> 00:29:37,719
Speaker 5: they all have what are called sentiment analysis tools. So

572
00:29:37,760 --> 00:29:40,760
Speaker 5: they'll take that review that somebody left on your Google

573
00:29:40,840 --> 00:29:43,680
Speaker 5: reviews or on your Yelp or you know whatever other

574
00:29:44,080 --> 00:29:46,960
Speaker 5: sites that you're on, and they'll analyze it and say

575
00:29:47,000 --> 00:29:49,760
Speaker 5: this was a positive review, this was a negative review,

576
00:29:50,040 --> 00:29:51,479
Speaker 5: or this was a neutral review.

577
00:29:51,720 --> 00:29:51,880
Speaker 3: Right.

578
00:29:51,920 --> 00:29:53,960
Speaker 5: You can also do that a lot of social media

579
00:29:54,000 --> 00:29:57,120
Speaker 5: platforms like HubSpot and stuff like that have that in

580
00:29:57,240 --> 00:29:59,800
Speaker 5: terms of your comments. So are your comments on your

581
00:30:00,000 --> 00:30:03,320
Speaker 5: social media pages positive, negative, or neutral. And what that

582
00:30:03,360 --> 00:30:05,720
Speaker 5: can help you do when you plug that in or

583
00:30:05,880 --> 00:30:09,200
Speaker 5: use those actual features is help you understand. Okay, here

584
00:30:09,240 --> 00:30:12,080
Speaker 5: are ten negative reviews. Even if the review had four

585
00:30:12,160 --> 00:30:15,480
Speaker 5: stars or five stars, it could still tell you based

586
00:30:15,480 --> 00:30:17,520
Speaker 5: on the content if the review is positive or not.

587
00:30:17,600 --> 00:30:19,320
Speaker 5: That lets you go in and see where you can

588
00:30:19,360 --> 00:30:22,080
Speaker 5: improve as a business as well as where you're actually

589
00:30:22,120 --> 00:30:25,000
Speaker 5: doing well as a business. So again, these things are

590
00:30:25,400 --> 00:30:28,400
Speaker 5: tools that are built into platforms right Like Canva has

591
00:30:28,400 --> 00:30:32,520
Speaker 5: an AI content generation tool, TikTok just launched their like

592
00:30:33,640 --> 00:30:37,880
Speaker 5: digital avatar creation studio, Meta and Google inside their advertising

593
00:30:37,920 --> 00:30:41,880
Speaker 5: platforms have like image and video generation tools built in.

594
00:30:42,280 --> 00:30:45,320
Speaker 5: So as you're thinking about how to market to your customers,

595
00:30:45,360 --> 00:30:50,840
Speaker 5: think about creating content for your marketing campaigns, effectively reaching

596
00:30:50,920 --> 00:30:54,280
Speaker 5: audiences with your ads, and then also using sentiment to

597
00:30:54,320 --> 00:30:56,960
Speaker 5: give you information about your business back and forth.

598
00:30:58,000 --> 00:31:00,120
Speaker 2: Yeah, it's funny that you brought up upside because as

599
00:31:00,120 --> 00:31:03,280
Speaker 2: you were talking, I'm thinking aboutself. The companies that have

600
00:31:03,680 --> 00:31:08,280
Speaker 2: a big base inside the CRM space. Number one, job

601
00:31:08,320 --> 00:31:11,200
Speaker 2: creation inside of them, or fear of job creation for

602
00:31:11,320 --> 00:31:13,440
Speaker 2: some people is like, all right, that industry is going

603
00:31:13,480 --> 00:31:15,920
Speaker 2: to be disrupted, but it's only going to be disruptive

604
00:31:16,120 --> 00:31:19,120
Speaker 2: if you're not using it along with right a human

605
00:31:19,360 --> 00:31:22,320
Speaker 2: and the AI. So talk about how businesses that use

606
00:31:22,560 --> 00:31:25,400
Speaker 2: customer relations management how this can be super beneficial because

607
00:31:25,480 --> 00:31:27,360
Speaker 2: I see it as the wave of the future for it.

608
00:31:27,800 --> 00:31:30,240
Speaker 5: Yeah, one hundred percent. So I said this on stage

609
00:31:30,280 --> 00:31:32,160
Speaker 5: and invest Fest. I'll say it again. It's not going

610
00:31:32,240 --> 00:31:34,160
Speaker 5: to be an AI that takes your job. It's going

611
00:31:34,200 --> 00:31:36,640
Speaker 5: to be a human that's using AI that's going to

612
00:31:36,680 --> 00:31:39,720
Speaker 5: take your job. And So when you're thinking about customer

613
00:31:39,800 --> 00:31:43,840
Speaker 5: relationship management systems, whether you're using a Salesforce, whether you're

614
00:31:43,920 --> 00:31:47,040
Speaker 5: using like a Nimble CRM, if you're using like a HubSpot,

615
00:31:47,120 --> 00:31:50,600
Speaker 5: if you're using like a Zoho to be able to

616
00:31:50,680 --> 00:31:54,480
Speaker 5: like track your customers, there are softwares out there that

617
00:31:54,680 --> 00:31:58,080
Speaker 5: have things what they call lead scoring right, so like

618
00:31:58,200 --> 00:32:02,719
Speaker 5: AI generated lead scoring. So if you're using maybe you're

619
00:32:02,760 --> 00:32:05,120
Speaker 5: doing like cold sales or code calls, or you're like

620
00:32:05,160 --> 00:32:09,680
Speaker 5: collecting customer leads, these types of plugins inside of your

621
00:32:09,680 --> 00:32:13,520
Speaker 5: CRM systems use AI to help you understand who is

622
00:32:13,560 --> 00:32:16,920
Speaker 5: the best potential lead for what you're doing. There's a

623
00:32:16,960 --> 00:32:19,880
Speaker 5: platform it's called Sense eight. I believe it is. I

624
00:32:19,960 --> 00:32:21,720
Speaker 5: have to look it up once we go through this

625
00:32:21,760 --> 00:32:24,760
Speaker 5: presentation and I'll drop a link to it. But it

626
00:32:25,160 --> 00:32:28,280
Speaker 5: cause like HubSpot's version of that is like supersed, like

627
00:32:28,280 --> 00:32:31,200
Speaker 5: a thousand dollars a month. Right, There's this platform called

628
00:32:31,240 --> 00:32:34,920
Speaker 5: sense eight that offers like predictive lead scoring based on

629
00:32:34,960 --> 00:32:38,000
Speaker 5: your CRM, based on your communication with them, based on

630
00:32:38,040 --> 00:32:41,080
Speaker 5: your preferences and what you say is like your ideal customer,

631
00:32:41,320 --> 00:32:43,560
Speaker 5: and they offer like a three month free trial and

632
00:32:43,600 --> 00:32:46,280
Speaker 5: then after that it's like four or five hundred dollars

633
00:32:46,320 --> 00:32:49,440
Speaker 5: a month. So it's much cheaper than what's built into HubSpot,

634
00:32:49,480 --> 00:32:52,000
Speaker 5: and it can plug into HubSpot and pull your data.

635
00:32:52,120 --> 00:32:55,080
Speaker 5: So I think a few different ways that you can

636
00:32:55,160 --> 00:32:57,840
Speaker 5: use it. I think using predictive lead scoring is important,

637
00:32:57,880 --> 00:33:01,120
Speaker 5: but also you know, as we talk about conversational AI,

638
00:33:01,200 --> 00:33:03,960
Speaker 5: this is another area where you can use AI to

639
00:33:04,040 --> 00:33:07,080
Speaker 5: communicate with those customers that are in your CRM on

640
00:33:07,120 --> 00:33:10,200
Speaker 5: your behalf. Right, So when you're thinking about like let's

641
00:33:10,240 --> 00:33:15,200
Speaker 5: say information management, right, Let's say that you got customers

642
00:33:15,200 --> 00:33:18,960
Speaker 5: who might have commonly asked questions or your startup and

643
00:33:18,960 --> 00:33:21,360
Speaker 5: you've taken investment money and your investors are going to

644
00:33:21,440 --> 00:33:24,479
Speaker 5: have questions about your attraction or even your staff, as

645
00:33:24,520 --> 00:33:26,840
Speaker 5: you're bringing new people in and there's ways of doing

646
00:33:26,880 --> 00:33:29,520
Speaker 5: things and they may not remember everything from the chaining.

647
00:33:29,920 --> 00:33:33,240
Speaker 5: You could set up chat bots that can interact with

648
00:33:33,280 --> 00:33:36,320
Speaker 5: these different groups of people and provide them information. So

649
00:33:36,440 --> 00:33:39,600
Speaker 5: for customers, a very low level type of chatbot without

650
00:33:39,640 --> 00:33:41,880
Speaker 5: you having a like turn on chat GBT and use

651
00:33:41,920 --> 00:33:45,400
Speaker 5: it in your business. There are these things called faq bots, right,

652
00:33:45,600 --> 00:33:48,400
Speaker 5: so if you already have like a frequently asked questions

653
00:33:48,480 --> 00:33:50,880
Speaker 5: about your business, you can plug it in to one

654
00:33:50,920 --> 00:33:53,720
Speaker 5: of these chat bots. Actually, Microsoft has one for free

655
00:33:53,760 --> 00:33:56,719
Speaker 5: called qn A Maker, and you can plug it in

656
00:33:56,720 --> 00:33:59,120
Speaker 5: and it'll take your Q and A and it'll automatically

657
00:33:59,200 --> 00:34:01,040
Speaker 5: make a chat boy. You can just plug into your

658
00:34:01,040 --> 00:34:04,400
Speaker 5: website completely free, so people can ask questions about your

659
00:34:04,400 --> 00:34:08,759
Speaker 5: cancelation policy, your business, about where you're at. And like

660
00:34:08,880 --> 00:34:10,759
Speaker 5: Q and A Maker isn't just for you to like

661
00:34:10,880 --> 00:34:13,719
Speaker 5: take the literal FAQ section, you could plug all the

662
00:34:13,719 --> 00:34:16,320
Speaker 5: content about your website in that. If you're talking about

663
00:34:16,360 --> 00:34:19,520
Speaker 5: internal operations, right, Like, let's say you have a I

664
00:34:19,560 --> 00:34:22,279
Speaker 5: don't know, like a like a customer service associate role

665
00:34:22,520 --> 00:34:24,759
Speaker 5: and you don't want them asking you ninety questions about

666
00:34:24,760 --> 00:34:26,680
Speaker 5: when and when not to give a discount or give

667
00:34:26,719 --> 00:34:29,080
Speaker 5: a customer a refund. You could create like a Q

668
00:34:29,239 --> 00:34:31,080
Speaker 5: and a bot for that and make it so that

669
00:34:31,120 --> 00:34:33,960
Speaker 5: your internal employees can use it. Same thing with like

670
00:34:34,000 --> 00:34:36,440
Speaker 5: the Investor Metrics, and again Q and a Maker is

671
00:34:36,480 --> 00:34:38,960
Speaker 5: one hundred percent free tool. There's a whole bunch of

672
00:34:39,000 --> 00:34:41,719
Speaker 5: tools out there that are like this that you can use.

673
00:34:42,120 --> 00:34:44,400
Speaker 5: The second piece where you can use kind of chatbot

674
00:34:44,480 --> 00:34:48,080
Speaker 5: specifically is around time management, so being able to automate

675
00:34:48,160 --> 00:34:51,759
Speaker 5: the setup and management of appointments, meetings, and bookings. Right, So,

676
00:34:51,760 --> 00:34:54,000
Speaker 5: if you're a service based business, like let's say you

677
00:34:54,080 --> 00:34:58,359
Speaker 5: provide you know, finance consultations or credit consultations, or you

678
00:34:58,400 --> 00:35:00,960
Speaker 5: provide literal services like hey, I'm gonna come give you

679
00:35:01,000 --> 00:35:03,800
Speaker 5: a haircutter, I'm gonna come put your lashes on. Instead

680
00:35:03,800 --> 00:35:06,160
Speaker 5: of you having to do all of that communication back

681
00:35:06,160 --> 00:35:09,840
Speaker 5: and forth through text, through WhatsApp, through messenger, through like

682
00:35:09,960 --> 00:35:12,080
Speaker 5: Instagram DM you know what I'm saying, where you're like

683
00:35:12,120 --> 00:35:15,319
Speaker 5: going across ten different things and trying to remember when

684
00:35:15,360 --> 00:35:17,840
Speaker 5: people are coming and adding it to a calendar. Just

685
00:35:17,880 --> 00:35:21,719
Speaker 5: grab a booking chatbot. These chat bots automatically communicate with

686
00:35:21,760 --> 00:35:24,640
Speaker 5: people they manage your availability. As soon as somebody books

687
00:35:24,640 --> 00:35:26,759
Speaker 5: an appointment, you can make them pay before they book

688
00:35:26,800 --> 00:35:29,560
Speaker 5: if you have a deposit, and then it automatically puts

689
00:35:29,560 --> 00:35:32,000
Speaker 5: it on your calendar, and then it automatically sends the

690
00:35:32,040 --> 00:35:34,520
Speaker 5: people like the email of like, hey, your appointment's coming up,

691
00:35:34,560 --> 00:35:36,960
Speaker 5: do you want to reschedule or cancel? And then like

692
00:35:37,040 --> 00:35:39,080
Speaker 5: if they don't show up, you getta keep the deposit,

693
00:35:39,120 --> 00:35:41,040
Speaker 5: you know what I'm saying, Like that stuff can all

694
00:35:41,080 --> 00:35:45,080
Speaker 5: be done in one software as opposed to you doing

695
00:35:45,080 --> 00:35:47,600
Speaker 5: it through fifty different things. And there are versions of

696
00:35:47,640 --> 00:35:50,560
Speaker 5: these kind of like booking chat bots that even accept

697
00:35:50,560 --> 00:35:53,400
Speaker 5: payments and things like cash app or Vemo, and so

698
00:35:53,680 --> 00:35:55,759
Speaker 5: you know, you can go find those tools out there.

699
00:35:55,800 --> 00:35:58,680
Speaker 5: They're a little like you know, booking and scheduling bots

700
00:35:58,760 --> 00:36:01,680
Speaker 5: or even plugins for like the Shopify chatbot that they have.

701
00:36:01,760 --> 00:36:04,160
Speaker 5: There's a plugin you can get that does that to

702
00:36:04,239 --> 00:36:06,839
Speaker 5: really streamline that part of your business and open your

703
00:36:06,880 --> 00:36:10,160
Speaker 5: time back up. And the last part is around process automation,

704
00:36:10,320 --> 00:36:12,760
Speaker 5: So use an AI to complete task that would normally

705
00:36:12,800 --> 00:36:16,399
Speaker 5: require human effort. Right, So, if you know that you're

706
00:36:16,440 --> 00:36:20,239
Speaker 5: gonna have to look at your CRM and see the

707
00:36:20,320 --> 00:36:23,200
Speaker 5: open leads and send them all an email. Then you

708
00:36:23,239 --> 00:36:25,640
Speaker 5: could use you know, like a zap year or like

709
00:36:25,719 --> 00:36:28,000
Speaker 5: a make which are these tools that let you do

710
00:36:28,160 --> 00:36:31,520
Speaker 5: like plug in one tool and create little custom rules

711
00:36:31,520 --> 00:36:35,280
Speaker 5: like if the person's lead is open and I haven't

712
00:36:35,320 --> 00:36:37,440
Speaker 5: emailed them in the last seven days, then send them

713
00:36:37,440 --> 00:36:40,880
Speaker 5: an email, right, And you could use AI to generate

714
00:36:40,960 --> 00:36:42,960
Speaker 5: those emails so you don't have to write. So you

715
00:36:42,960 --> 00:36:45,120
Speaker 5: could add a little step in its like go to

716
00:36:45,200 --> 00:36:48,279
Speaker 5: chat GPT and generate a personalized follow up email then

717
00:36:48,320 --> 00:36:51,520
Speaker 5: send that email. So you can use AI, especially in

718
00:36:51,600 --> 00:36:54,520
Speaker 5: conversation AI these chat boys, these large language models to

719
00:36:54,560 --> 00:36:58,759
Speaker 5: help you automate those processes as well. And finally, when

720
00:36:58,760 --> 00:37:02,080
Speaker 5: you're thinking about operation, right, you think about wanting to

721
00:37:02,160 --> 00:37:05,480
Speaker 5: use AI to accelerate or stabilize those tasks that you

722
00:37:05,600 --> 00:37:07,319
Speaker 5: have to do day to day. Is a business to

723
00:37:07,360 --> 00:37:09,799
Speaker 5: be able to survive, So the first part is around

724
00:37:09,880 --> 00:37:13,520
Speaker 5: data analysis, being able to understand patterns across your data

725
00:37:13,640 --> 00:37:17,040
Speaker 5: or create visualizations of data. There's a tool out there

726
00:37:17,080 --> 00:37:20,879
Speaker 5: called Gamma, and Gamma is a presentation AI tool, right,

727
00:37:21,200 --> 00:37:23,840
Speaker 5: And so if you are someone who's like me, like

728
00:37:23,960 --> 00:37:26,080
Speaker 5: as nice as this deck kind of looks y'all, this

729
00:37:26,160 --> 00:37:27,920
Speaker 5: is about as good as it gets for me. I

730
00:37:27,960 --> 00:37:31,000
Speaker 5: can't design, I cannot draw. I could code a AI

731
00:37:31,080 --> 00:37:33,080
Speaker 5: to do it for me, but me and my like

732
00:37:33,160 --> 00:37:36,360
Speaker 5: pitch decks sound terrible. So being able to use Gamma

733
00:37:36,960 --> 00:37:39,360
Speaker 5: has really changed my life in terms of like creating

734
00:37:39,440 --> 00:37:42,840
Speaker 5: data visualizations. There's another tool out there, it's called Columns

735
00:37:43,800 --> 00:37:47,279
Speaker 5: that plugs into your data and automatically creates visualizations and

736
00:37:47,320 --> 00:37:49,640
Speaker 5: you can customize them. And it's like you can go

737
00:37:49,680 --> 00:37:52,359
Speaker 5: on one of those websites like app Sumo and get

738
00:37:52,400 --> 00:37:55,680
Speaker 5: like a lifetime subscription to it. You know what I'm saying,

739
00:37:55,840 --> 00:37:58,200
Speaker 5: like for like fifty dollars and forever have access to

740
00:37:58,200 --> 00:38:01,880
Speaker 5: this data visualization tool. And tool also gives you information

741
00:38:02,440 --> 00:38:04,840
Speaker 5: about the data that you're pulling up or this is

742
00:38:04,880 --> 00:38:07,000
Speaker 5: where you would plug it into like a chat GPT

743
00:38:07,160 --> 00:38:09,640
Speaker 5: and ask it questions. Just make sure it's not making

744
00:38:09,680 --> 00:38:12,160
Speaker 5: nothing up when you do that right, and then you

745
00:38:12,239 --> 00:38:14,400
Speaker 5: have what we call knowledge minded. So how do you

746
00:38:14,480 --> 00:38:17,360
Speaker 5: use AI to understand how your business is functioning and

747
00:38:17,400 --> 00:38:19,960
Speaker 5: your business health? Again, this is going to be inside

748
00:38:19,960 --> 00:38:23,640
Speaker 5: of the tools you're already using, like like HubSpot or

749
00:38:24,560 --> 00:38:28,160
Speaker 5: Salesforce CRM. They're already gonna have like AI functions to

750
00:38:28,200 --> 00:38:31,080
Speaker 5: tell you like, hey, here's a risk score that tells

751
00:38:31,080 --> 00:38:33,319
Speaker 5: you how you know much of your business is at

752
00:38:33,400 --> 00:38:36,520
Speaker 5: risk of not closing or like the performance of your employees.

753
00:38:36,800 --> 00:38:39,400
Speaker 5: So just turning on those kinds of tools and functions

754
00:38:39,440 --> 00:38:41,520
Speaker 5: that are already in the apps you're using is critical

755
00:38:41,800 --> 00:38:44,680
Speaker 5: then of course for knowledge sharing, right, So building those

756
00:38:44,680 --> 00:38:49,160
Speaker 5: internal chatbots, building those internal knowledge based databases, even fine

757
00:38:49,200 --> 00:38:54,160
Speaker 5: tuning a chat GPT or a version of like you know,

758
00:38:54,280 --> 00:38:58,160
Speaker 5: Gemini to work specifically on your data, like Moderna just

759
00:38:58,200 --> 00:39:01,239
Speaker 5: did this with seven hundred and fifty customs GPTs, and

760
00:39:01,320 --> 00:39:03,960
Speaker 5: so overall you have these three tests, specific kind of

761
00:39:03,960 --> 00:39:07,360
Speaker 5: tools around using it to you know, identify and engage

762
00:39:07,360 --> 00:39:10,680
Speaker 5: your customers on how to actually communicate with your customers

763
00:39:10,719 --> 00:39:13,040
Speaker 5: or your stakeholders on your behalf, So how do you

764
00:39:13,080 --> 00:39:15,920
Speaker 5: scale yourself? And then around the operations, how do you

765
00:39:16,040 --> 00:39:20,120
Speaker 5: understand how your business is operating communicate that to other people?

766
00:39:20,200 --> 00:39:21,920
Speaker 5: And I'll pause there because that's a lot.

767
00:39:21,760 --> 00:39:24,120
Speaker 2: Of info that was That was a lot I'm taking

768
00:39:24,160 --> 00:39:26,520
Speaker 2: noteses a lot of info for sure.

769
00:39:28,040 --> 00:39:32,200
Speaker 4: So let's reset the wroong to my access This is

770
00:39:32,200 --> 00:39:34,040
Speaker 4: going to be on say this will This will be

771
00:39:34,080 --> 00:39:36,279
Speaker 4: saved on the channel. You have to go to the

772
00:39:36,320 --> 00:39:37,880
Speaker 4: live section and see it though, but it will be

773
00:39:37,880 --> 00:39:40,160
Speaker 4: saved on the channel. Also will be on podcasts outlets

774
00:39:40,160 --> 00:39:42,640
Speaker 4: as well, So if you want to listen to it,

775
00:39:43,040 --> 00:39:45,080
Speaker 4: I want to go, you can check it out on podcasts,

776
00:39:45,080 --> 00:39:46,480
Speaker 4: but this will because you need you need to watch

777
00:39:46,480 --> 00:39:50,560
Speaker 4: this at least three times. At least three times the

778
00:39:50,640 --> 00:39:53,800
Speaker 4: master investor is on the check in. Shout out to

779
00:39:53,840 --> 00:39:58,879
Speaker 4: Ian in the building. Okay, I think we derailed. Did

780
00:39:58,880 --> 00:40:00,480
Speaker 4: you go through your slides or did.

781
00:40:00,360 --> 00:40:03,239
Speaker 5: You get have a little bit more for these specifics.

782
00:40:03,680 --> 00:40:06,200
Speaker 5: So I'm gonna give you a little example. So like

783
00:40:06,280 --> 00:40:09,440
Speaker 5: you would generate, you use AI to generate marketing with

784
00:40:09,520 --> 00:40:11,720
Speaker 5: a call to action for people to use your chatbot,

785
00:40:11,800 --> 00:40:15,440
Speaker 5: like hey, you want your lashes scheduled DM me on Instagram.

786
00:40:15,480 --> 00:40:18,200
Speaker 5: Because these like chatbots, you can deploy the like Facebook

787
00:40:18,200 --> 00:40:21,480
Speaker 5: Messenger or into WhatsApp or into Instagram right to like

788
00:40:21,920 --> 00:40:24,640
Speaker 5: work within those chats. It doesn't have to be just

789
00:40:24,719 --> 00:40:27,640
Speaker 5: on your website. And then the chatbot was schedule like

790
00:40:27,680 --> 00:40:30,359
Speaker 5: a sales or a discovery call with a client, then

791
00:40:30,400 --> 00:40:33,040
Speaker 5: you would use AI to take notes during that call,

792
00:40:33,200 --> 00:40:36,040
Speaker 5: something like Otter, AI or any of these number of

793
00:40:36,080 --> 00:40:38,320
Speaker 5: these other tools that people be using, and then it

794
00:40:38,360 --> 00:40:41,000
Speaker 5: would recommend next steps. Now, let's say you're having a

795
00:40:41,000 --> 00:40:44,719
Speaker 5: call with the customer, like a potential sales opportunity, or

796
00:40:45,320 --> 00:40:48,200
Speaker 5: with a customer who might be unhappy. You can do

797
00:40:48,320 --> 00:40:51,799
Speaker 5: sentiment analysis on those transcripts from the call that you

798
00:40:51,840 --> 00:40:54,160
Speaker 5: know otter will generate from you to understand how that

799
00:40:54,200 --> 00:40:56,520
Speaker 5: person feels. So if you're like, Yo, I just had

800
00:40:56,520 --> 00:40:58,680
Speaker 5: this sales call. I'm not really sure where they stand,

801
00:40:58,680 --> 00:41:00,840
Speaker 5: you could use AI to give you like that insight

802
00:41:00,920 --> 00:41:03,279
Speaker 5: and be like, nah, this was more neutral, or hey,

803
00:41:03,320 --> 00:41:05,720
Speaker 5: this was more positive call. You know what I'm saying

804
00:41:05,719 --> 00:41:08,840
Speaker 5: to help give you that insight. Another example would be

805
00:41:08,880 --> 00:41:12,000
Speaker 5: if maybe you're a service based business, you would same thing,

806
00:41:12,160 --> 00:41:14,520
Speaker 5: use AI to create like a cool jingle or maybe

807
00:41:14,560 --> 00:41:16,960
Speaker 5: a cool little video to help you with your call

808
00:41:17,000 --> 00:41:19,719
Speaker 5: to action to use that chatbot. Then the chatbot would

809
00:41:19,719 --> 00:41:22,560
Speaker 5: book the appointment, and then once the appointment is booked,

810
00:41:23,160 --> 00:41:27,279
Speaker 5: it would automatically start to like retarget that customer to

811
00:41:27,640 --> 00:41:30,520
Speaker 5: come back in for another appointment, right like, Hey, you

812
00:41:30,560 --> 00:41:32,919
Speaker 5: should come in and book another appointment. We haven't seen

813
00:41:32,960 --> 00:41:34,920
Speaker 5: you in a month, or we haven't seen you in

814
00:41:34,960 --> 00:41:39,479
Speaker 5: three months, and then as your customers continuously interact, it'll

815
00:41:39,480 --> 00:41:42,280
Speaker 5: start to learn patterns about them, so it'll know, like, hey,

816
00:41:42,920 --> 00:41:45,680
Speaker 5: this customer only comes in for pedicures. They never get

817
00:41:45,680 --> 00:41:48,240
Speaker 5: a manicure, So when we send them like a special

818
00:41:48,320 --> 00:41:52,239
Speaker 5: deals email, we're gonna send them one for pedicures, right

819
00:41:52,320 --> 00:41:54,880
Speaker 5: for like ten twenty percent off of pedicures. And so

820
00:41:55,040 --> 00:42:00,600
Speaker 5: this type of pipeline, especially with like the customer retargeting messaging,

821
00:42:00,760 --> 00:42:03,799
Speaker 5: is built into tools like send grid. It's built into

822
00:42:03,840 --> 00:42:06,520
Speaker 5: loyalty tools where customers, like you know, give you your

823
00:42:06,560 --> 00:42:09,680
Speaker 5: email and then like they leave ratings and they earn points.

824
00:42:09,719 --> 00:42:12,719
Speaker 5: It's also built into the actual email marketing tools that

825
00:42:12,760 --> 00:42:14,880
Speaker 5: are out there, so you don't have to go build

826
00:42:14,920 --> 00:42:18,400
Speaker 5: like a whole recommender engine or personalized system to be

827
00:42:18,440 --> 00:42:21,400
Speaker 5: able to get to that. And so another thing that

828
00:42:21,440 --> 00:42:24,120
Speaker 5: you can do when we talked about like AI enabled platforms.

829
00:42:24,120 --> 00:42:26,879
Speaker 5: So if you can't see right now, I'm at will

830
00:42:26,920 --> 00:42:31,279
Speaker 5: i AM's FYI campus in downtown LA And FYI is

831
00:42:31,280 --> 00:42:34,120
Speaker 5: an app that stands for Focus your Ideas and will

832
00:42:34,160 --> 00:42:36,479
Speaker 5: i Am created it because he wanted a better way

833
00:42:36,560 --> 00:42:39,440
Speaker 5: to manage creative projects. What I've found in my day

834
00:42:39,480 --> 00:42:42,120
Speaker 5: to day life where I have an AI consulting company,

835
00:42:42,440 --> 00:42:44,760
Speaker 5: is that this helps me as well. So the app

836
00:42:44,800 --> 00:42:48,120
Speaker 5: is one hundred percent free. It's AI enabled to cross

837
00:42:48,160 --> 00:42:51,480
Speaker 5: everything and let you create and manage projects. Inside of

838
00:42:51,480 --> 00:42:54,600
Speaker 5: these projects, you can share files, you can add people

839
00:42:54,600 --> 00:42:57,200
Speaker 5: to them, you can remove people from them. You can

840
00:42:57,239 --> 00:43:00,640
Speaker 5: have group calls and group chats, so you can set

841
00:43:00,680 --> 00:43:03,400
Speaker 5: up video calls, they could be audio calls. You have

842
00:43:03,480 --> 00:43:06,360
Speaker 5: an AI assistant that's built into the app. So like

843
00:43:06,440 --> 00:43:08,879
Speaker 5: let's say you guys have a chat going on about

844
00:43:08,880 --> 00:43:12,520
Speaker 5: an idea. You could literally tag fyi dot ai and

845
00:43:12,600 --> 00:43:15,759
Speaker 5: ask it can you summarize this? Can you summarize what's

846
00:43:15,800 --> 00:43:17,880
Speaker 5: going on in this chat? Or you could talk to

847
00:43:17,920 --> 00:43:19,839
Speaker 5: it and ask it, hey, help me generate this new

848
00:43:19,880 --> 00:43:22,680
Speaker 5: idea like you would talk to a chat GPT or

849
00:43:22,719 --> 00:43:25,880
Speaker 5: you would talk to a clatter at Gemini. And inside

850
00:43:25,880 --> 00:43:28,239
Speaker 5: of the app, it has an image generator that's one

851
00:43:28,280 --> 00:43:33,240
Speaker 5: hundred percent free that you can use. So for me operationally,

852
00:43:34,080 --> 00:43:36,200
Speaker 5: you know, while I do have a CRM system for

853
00:43:36,280 --> 00:43:39,520
Speaker 5: certain things, the like day to day tasks or projects

854
00:43:39,640 --> 00:43:42,799
Speaker 5: or ideas, I operate off of f yi AI because

855
00:43:42,800 --> 00:43:45,200
Speaker 5: it's one hundred percent free and who don't like free?

856
00:43:45,200 --> 00:43:45,680
Speaker 5: You feel me?

857
00:43:46,760 --> 00:43:47,719
Speaker 1: Shout Shout out the will.

858
00:43:47,840 --> 00:43:50,120
Speaker 2: Shout out to Neil who said he's been using Fyi

859
00:43:50,200 --> 00:43:52,320
Speaker 2: since he left Investments.

860
00:43:52,920 --> 00:43:56,319
Speaker 1: Is an amazing app. I've been using it myself. Man,

861
00:43:56,520 --> 00:43:57,440
Speaker 1: use it with my kids.

862
00:43:57,719 --> 00:44:00,560
Speaker 2: They were asking some questions about history as but watching

863
00:44:00,880 --> 00:44:03,359
Speaker 2: the vice presidential debate, So that was pretty cool.

864
00:44:03,800 --> 00:44:08,200
Speaker 1: Uh you want to yeah question Now I'm gonna let

865
00:44:08,200 --> 00:44:09,719
Speaker 1: to finish. Let her finish it.

866
00:44:10,880 --> 00:44:13,840
Speaker 5: Yeah. So last part, how do you use AI to

867
00:44:13,880 --> 00:44:16,680
Speaker 5: amplify your genius? Now? I did a workshop on this

868
00:44:16,760 --> 00:44:19,040
Speaker 5: when I was with Van Jones out in Atlanta, but

869
00:44:19,120 --> 00:44:21,479
Speaker 5: outside of that, I haven't given any win this game,

870
00:44:21,560 --> 00:44:24,880
Speaker 5: right because this is my game on how I personally

871
00:44:24,960 --> 00:44:28,040
Speaker 5: interact with like a chat GPT or like a Gemini

872
00:44:28,200 --> 00:44:30,399
Speaker 5: or like a Claude. Now, I'm coming from this as

873
00:44:30,440 --> 00:44:34,000
Speaker 5: an angle of like somebody who knows how these systems

874
00:44:34,040 --> 00:44:36,879
Speaker 5: work on a technical level. So I created a framework

875
00:44:37,160 --> 00:44:39,640
Speaker 5: for how to write your prompts to be able to

876
00:44:39,680 --> 00:44:42,799
Speaker 5: get the most out of the AI interaction, but in

877
00:44:42,840 --> 00:44:45,000
Speaker 5: a way where it's not just spitting out an answer

878
00:44:45,040 --> 00:44:47,960
Speaker 5: at you, but where it's taking your genius and your

879
00:44:48,080 --> 00:44:51,720
Speaker 5: thoughts and helping you structure them or and also teaching

880
00:44:51,760 --> 00:44:53,880
Speaker 5: you along the way. So think about it like this,

881
00:44:54,320 --> 00:44:56,279
Speaker 5: if I could talk to let's say I have a

882
00:44:56,280 --> 00:44:59,799
Speaker 5: business plan idea, right, the way chat, GPT is by

883
00:44:59,840 --> 00:45:02,600
Speaker 5: d any of these large language models is by default

884
00:45:02,680 --> 00:45:05,520
Speaker 5: is they've got all these data from all over the Internet.

885
00:45:05,640 --> 00:45:07,799
Speaker 5: And so when you ask it, hey, I want to

886
00:45:07,880 --> 00:45:13,680
Speaker 5: create like an app for babysitters that connects parents with

887
00:45:13,840 --> 00:45:18,360
Speaker 5: like background checked babysitters on demand. Right, come write my

888
00:45:18,440 --> 00:45:22,279
Speaker 5: business plan. It's gonna grab from the randomness of all

889
00:45:22,320 --> 00:45:25,680
Speaker 5: of that to give you an answer. Now, why would

890
00:45:25,680 --> 00:45:27,759
Speaker 5: I want to talk to some random person who I

891
00:45:27,800 --> 00:45:30,319
Speaker 5: don't know the experience and a background that they have,

892
00:45:31,000 --> 00:45:34,520
Speaker 5: Versus I could turn it into like a business professor

893
00:45:34,560 --> 00:45:39,480
Speaker 5: at Harvard that has like extensive experience creating these types

894
00:45:39,520 --> 00:45:42,440
Speaker 5: of services, Like maybe you were the dude who scaled

895
00:45:42,520 --> 00:45:45,880
Speaker 5: like DoorDash to his first billion dollars, right, and then

896
00:45:46,360 --> 00:45:48,440
Speaker 5: engage with it in a way where it starts to

897
00:45:48,520 --> 00:45:51,640
Speaker 5: ask me questions about my idea and then starts to

898
00:45:51,640 --> 00:45:54,960
Speaker 5: give me an answer based on my idea, not just

899
00:45:55,040 --> 00:45:57,920
Speaker 5: based on whatever it grabbed out of all those terabytes

900
00:45:57,920 --> 00:46:01,080
Speaker 5: and terabytes of data you feel me. And so that

901
00:46:01,160 --> 00:46:04,239
Speaker 5: framework I call Chat in the Hat, right, And I

902
00:46:04,239 --> 00:46:06,880
Speaker 5: know it's a little funny framework, but it's really to

903
00:46:07,000 --> 00:46:10,000
Speaker 5: help you learn how to think like an AI, so

904
00:46:10,080 --> 00:46:11,840
Speaker 5: you can talk to the AI in a way to

905
00:46:11,880 --> 00:46:15,799
Speaker 5: amplifize your genius. So what does chat and the Hat

906
00:46:15,840 --> 00:46:18,759
Speaker 5: stand for. It's a three part framework when you're writing your.

907
00:46:18,920 --> 00:46:22,719
Speaker 4: And this is extremely important ladies and gentlemen. If you

908
00:46:22,719 --> 00:46:25,680
Speaker 4: didn't pay attention to anything else, which shame on you

909
00:46:25,719 --> 00:46:28,480
Speaker 4: if you did that, pay attention to this part because

910
00:46:28,520 --> 00:46:33,200
Speaker 4: this is extremely important. How to actually talk to AI

911
00:46:33,400 --> 00:46:37,320
Speaker 4: to get the proper response. That's vitally important because you

912
00:46:37,320 --> 00:46:39,680
Speaker 4: can speak to somebody and it's like a human being,

913
00:46:39,760 --> 00:46:41,600
Speaker 4: right like I can speak to somebody. You have to

914
00:46:41,640 --> 00:46:44,600
Speaker 4: be educated enough to ask the question. So how I'm

915
00:46:44,680 --> 00:46:46,600
Speaker 4: asking the question is going to determine the answer that

916
00:46:46,600 --> 00:46:50,640
Speaker 4: I'm getting. That's not talked about enough. So what she's

917
00:46:50,640 --> 00:46:53,720
Speaker 4: about to talk about is actually vitally.

918
00:46:53,360 --> 00:46:57,080
Speaker 5: Important, especially because when you're talking to a large language

919
00:46:57,080 --> 00:47:00,799
Speaker 5: model like I mentioned before, it doesn't actually understan what

920
00:47:00,840 --> 00:47:03,719
Speaker 5: you're saying, so it's not gonna get the gist of it, right.

921
00:47:03,920 --> 00:47:06,520
Speaker 5: You have to give it the specific instructions that you

922
00:47:06,640 --> 00:47:10,359
Speaker 5: wanted to execute, and the more specific and the more

923
00:47:10,440 --> 00:47:14,160
Speaker 5: clear you give it, the more it'll answer in the

924
00:47:14,200 --> 00:47:17,000
Speaker 5: way that you need it to. But again, it's just

925
00:47:17,120 --> 00:47:20,759
Speaker 5: taking whatever prompt you give it and calculating based on that.

926
00:47:20,840 --> 00:47:22,839
Speaker 5: So if you say, write me a business plan about

927
00:47:22,840 --> 00:47:27,520
Speaker 5: an app for you know, connecting background check babysitters with parents,

928
00:47:27,840 --> 00:47:30,399
Speaker 5: it's gonna write something. But if I give it first

929
00:47:30,480 --> 00:47:33,879
Speaker 5: a history, right, and then I give it an attitude

930
00:47:34,320 --> 00:47:36,239
Speaker 5: how I want it to complete that task, and then

931
00:47:36,280 --> 00:47:38,920
Speaker 5: I give it a specific instructions on how to complete

932
00:47:38,920 --> 00:47:42,160
Speaker 5: that task, the conversation is gonna be completely different. So

933
00:47:42,160 --> 00:47:44,799
Speaker 5: I'm gonna do real quickst breakdown each component of this,

934
00:47:45,200 --> 00:47:47,840
Speaker 5: and in real time live on the chat, we're gonna

935
00:47:47,840 --> 00:47:50,040
Speaker 5: make up an idea together, and I'm gonna show you,

936
00:47:50,040 --> 00:47:53,920
Speaker 5: guys the difference between using just a regular prompt versus

937
00:47:53,920 --> 00:47:56,960
Speaker 5: the chat and a hat framework you feel me. So, first,

938
00:47:57,040 --> 00:47:59,560
Speaker 5: what is history? History? Is? You want to tell it

939
00:47:59,800 --> 00:48:03,000
Speaker 5: the relevant skills and experience that it needs to have

940
00:48:03,440 --> 00:48:06,200
Speaker 5: to be able to help you accomplish that task. So

941
00:48:06,360 --> 00:48:09,520
Speaker 5: if I have an idea for app me just talking

942
00:48:09,520 --> 00:48:11,480
Speaker 5: to chat GPT, I don't know who it could be

943
00:48:11,520 --> 00:48:14,440
Speaker 5: pulling from versus giving it like, Yo, you're an award

944
00:48:14,480 --> 00:48:18,279
Speaker 5: winning person in this field who has this specific experience

945
00:48:18,360 --> 00:48:21,320
Speaker 5: in twenty years of that experience. Right, you want to

946
00:48:21,360 --> 00:48:24,120
Speaker 5: give it some type of history that it can refer

947
00:48:24,239 --> 00:48:27,200
Speaker 5: to to pull in that knowledge from all of those

948
00:48:27,320 --> 00:48:30,840
Speaker 5: terabytes of data to specifically be using the knowledge that

949
00:48:30,920 --> 00:48:33,000
Speaker 5: you need to complete your task. So, if it's like

950
00:48:34,760 --> 00:48:37,839
Speaker 5: a social media thing, right, you don't just want to

951
00:48:37,880 --> 00:48:41,040
Speaker 5: go ask CHADGBT about how to make a viral social campaign.

952
00:48:41,360 --> 00:48:46,480
Speaker 5: You want CHADGBT to be an expert at generating viral campaigns,

953
00:48:46,480 --> 00:48:50,160
Speaker 5: like you made a company similar to mine, have fifty

954
00:48:50,200 --> 00:48:53,200
Speaker 5: million views on TikTok with your funny short videos.

955
00:48:53,280 --> 00:48:53,480
Speaker 3: Right.

956
00:48:53,840 --> 00:48:56,640
Speaker 5: Then it kind of puts itself in that framing and

957
00:48:56,680 --> 00:48:59,080
Speaker 5: starts to think from that lens. Then you want to

958
00:48:59,080 --> 00:49:00,840
Speaker 5: give it an attitude, which is like, what is the

959
00:49:00,880 --> 00:49:03,600
Speaker 5: mentality or the vibe that it has while doing the task?

960
00:49:03,719 --> 00:49:07,200
Speaker 5: Is it helpful? Is it you know play Devil's Advocate?

961
00:49:07,440 --> 00:49:09,480
Speaker 5: Is it you know? Does it like to ask you

962
00:49:09,600 --> 00:49:11,680
Speaker 5: questions and teach you how to do it instead of

963
00:49:11,760 --> 00:49:14,000
Speaker 5: just giving you an answer? Right, So you want to

964
00:49:14,040 --> 00:49:16,680
Speaker 5: give it like the attitude like how and what the

965
00:49:16,800 --> 00:49:20,239
Speaker 5: vibe is that it uses when it's thinking about how

966
00:49:20,239 --> 00:49:22,040
Speaker 5: to do your task. Then you want to give it

967
00:49:22,040 --> 00:49:24,440
Speaker 5: the actual task, which is you need to specifically tell

968
00:49:24,440 --> 00:49:26,640
Speaker 5: it what it should do and how it should do it.

969
00:49:26,719 --> 00:49:28,319
Speaker 5: So it's deep dive into each one of these a

970
00:49:28,320 --> 00:49:31,200
Speaker 5: little bit more so first with the history. Again, you

971
00:49:31,239 --> 00:49:34,640
Speaker 5: want to give it that relevant knowledge, experience, and accomplishments

972
00:49:34,680 --> 00:49:37,560
Speaker 5: that a person would need to actually be useful to

973
00:49:37,600 --> 00:49:40,319
Speaker 5: you in completing that task. So you say something like

974
00:49:40,440 --> 00:49:45,359
Speaker 5: you are a you know, award winning business plan developer, right,

975
00:49:45,480 --> 00:49:49,160
Speaker 5: Like you're a professor at Harvard, Wharton and Stanford in

976
00:49:49,480 --> 00:49:53,480
Speaker 5: creating startup business plans. Now, Chad GBT does know what

977
00:49:53,520 --> 00:49:57,040
Speaker 5: these kinds of institutions are, and it's not gonna know that,

978
00:49:57,120 --> 00:49:59,600
Speaker 5: Like you can't be the chair of business at Harvard

979
00:49:59,640 --> 00:50:03,080
Speaker 5: Standard and at like Wharton and you see Berkeley all

980
00:50:03,120 --> 00:50:04,920
Speaker 5: at once. Like it's not gonna know that, but it

981
00:50:05,000 --> 00:50:08,480
Speaker 5: is gonna understand what it means to be like the

982
00:50:08,600 --> 00:50:10,680
Speaker 5: chair of business at Stanford or the chair of the

983
00:50:10,719 --> 00:50:13,799
Speaker 5: business school at Harvard, right, because it has enough data

984
00:50:13,840 --> 00:50:16,680
Speaker 5: to understand what those specific things are. And then you

985
00:50:16,760 --> 00:50:18,640
Speaker 5: might want to give us some experience like you have.

986
00:50:19,400 --> 00:50:22,560
Speaker 5: You know, you have forty years of experience helping startups

987
00:50:22,640 --> 00:50:26,400
Speaker 5: create business plans. Well, you know you have. You know

988
00:50:26,760 --> 00:50:29,600
Speaker 5: you were the person who created the original business plan

989
00:50:29,680 --> 00:50:32,720
Speaker 5: for DoorDash. Because if I'm thinking about sitter as an app,

990
00:50:32,800 --> 00:50:36,920
Speaker 5: right like this, you know, babysitting connecting app, what's a

991
00:50:36,960 --> 00:50:40,080
Speaker 5: similar type of service like a DoorDash or a task rabbit, right,

992
00:50:40,160 --> 00:50:43,520
Speaker 5: Like you wrote the financial models for task rabbit that

993
00:50:43,640 --> 00:50:46,680
Speaker 5: help them be successful, right, and like you're an expert

994
00:50:46,680 --> 00:50:50,440
Speaker 5: in creating business plans that like help start up scale

995
00:50:50,680 --> 00:50:53,760
Speaker 5: and get investment money, because that's my goal. So even

996
00:50:53,920 --> 00:50:57,040
Speaker 5: just there me giving it those specifics about his history

997
00:50:57,320 --> 00:51:00,640
Speaker 5: will completely change the way that I'm gonna engaged with

998
00:51:00,719 --> 00:51:03,239
Speaker 5: this chatbot and the types of answers it's gonna give me,

999
00:51:03,520 --> 00:51:05,640
Speaker 5: and the type of knowledge is gonna pull out to

1000
00:51:05,719 --> 00:51:09,319
Speaker 5: answer that. Right. So you could also say, like you're

1001
00:51:09,320 --> 00:51:12,279
Speaker 5: an award winning or world renowned expert in this. You

1002
00:51:12,360 --> 00:51:16,600
Speaker 5: have number of years experience developing business plans at these

1003
00:51:16,640 --> 00:51:19,719
Speaker 5: companies you have built or you built this company, or

1004
00:51:19,760 --> 00:51:23,239
Speaker 5: you scaled this thing, or you you know, created this

1005
00:51:23,360 --> 00:51:25,440
Speaker 5: product or this service. So if I flip it and

1006
00:51:25,480 --> 00:51:28,400
Speaker 5: I move to social media like you are the leading

1007
00:51:28,480 --> 00:51:32,359
Speaker 5: expert and creating viral TikTok videos. You have twenty years

1008
00:51:32,360 --> 00:51:37,560
Speaker 5: of experience creating and leading viral campaigns on short form

1009
00:51:37,640 --> 00:51:41,640
Speaker 5: video campaigns on TikTok at like door dash, at uber

1010
00:51:41,719 --> 00:51:46,480
Speaker 5: and at lyft. Right, you literally grew ubers TikTok following

1011
00:51:46,520 --> 00:51:49,480
Speaker 5: from one thousand followers to over ten million, and you've

1012
00:51:49,480 --> 00:51:52,480
Speaker 5: won several awards along the way. Right, So you just

1013
00:51:52,520 --> 00:51:55,120
Speaker 5: start to think about the persona if you could create

1014
00:51:55,200 --> 00:51:57,520
Speaker 5: the perfect person that you would talk to about this,

1015
00:51:57,960 --> 00:51:59,719
Speaker 5: even if some of the details are a little bit

1016
00:51:59,760 --> 00:52:02,360
Speaker 5: of be yes, that's fine, but you wanted to create

1017
00:52:02,440 --> 00:52:04,879
Speaker 5: that perfect person that you want to talk to in

1018
00:52:04,920 --> 00:52:07,880
Speaker 5: person who's going to help you with that task. So

1019
00:52:07,920 --> 00:52:10,760
Speaker 5: that's the history. And then the attitude again, is that energy?

1020
00:52:10,960 --> 00:52:13,160
Speaker 5: Is that mentality and that vibe that you want the

1021
00:52:13,200 --> 00:52:16,120
Speaker 5: AI to take with you while it's responding to you.

1022
00:52:16,200 --> 00:52:18,720
Speaker 5: And that has two components. The first is the style.

1023
00:52:19,200 --> 00:52:22,120
Speaker 5: Is it helpful? Is it curious? Is it critical? Is

1024
00:52:22,120 --> 00:52:24,920
Speaker 5: it more like evaluative? Is it pensive? Is it playing

1025
00:52:25,000 --> 00:52:28,640
Speaker 5: Devil's advocate? Is it challenging? Is it excited? Is it

1026
00:52:29,760 --> 00:52:31,600
Speaker 5: you know, like if you wanted to turn it into

1027
00:52:31,640 --> 00:52:35,080
Speaker 5: like a venture capitalist to like, you know, review your idea.

1028
00:52:35,280 --> 00:52:37,759
Speaker 5: Is it like a skeptic? Right, you want to give

1029
00:52:37,800 --> 00:52:40,239
Speaker 5: it like a style of how it's supposed to be

1030
00:52:40,320 --> 00:52:42,960
Speaker 5: thinking about the answer it gives you back, and then

1031
00:52:43,000 --> 00:52:45,120
Speaker 5: you give it a tone, so like, is it gonna

1032
00:52:45,160 --> 00:52:47,960
Speaker 5: respond in professional language? Is it going to be academic?

1033
00:52:48,080 --> 00:52:52,520
Speaker 5: Is it going to be formal? Informal casual? Humor? Is friendly? Persuasive?

1034
00:52:52,640 --> 00:52:54,160
Speaker 5: Is it going to be urban? Is it going to

1035
00:52:54,200 --> 00:52:57,319
Speaker 5: be targeted at gen Z or gen alpha? Right, So

1036
00:52:57,600 --> 00:53:00,440
Speaker 5: when you're giving it that attitude, you've already giving it

1037
00:53:00,440 --> 00:53:03,600
Speaker 5: the history, so now it knows what knowledge it's pulling from.

1038
00:53:03,800 --> 00:53:05,920
Speaker 5: Now you're just telling it when you give me answers,

1039
00:53:05,960 --> 00:53:08,600
Speaker 5: this how I want the vibe between us to be right.

1040
00:53:09,040 --> 00:53:10,839
Speaker 5: So an example of that would be, like, you are

1041
00:53:10,920 --> 00:53:14,560
Speaker 5: extremely helpful. You love sharing your knowledge to help people

1042
00:53:14,880 --> 00:53:17,960
Speaker 5: create business plans. You have a deep passion for listening

1043
00:53:17,960 --> 00:53:20,840
Speaker 5: to people's ideas and guiding them step by step to

1044
00:53:21,000 --> 00:53:23,120
Speaker 5: create business plans. And then you could add in some

1045
00:53:23,160 --> 00:53:26,680
Speaker 5: more stuff like you prefer to help people learn how

1046
00:53:26,719 --> 00:53:29,600
Speaker 5: to help themselves. So instead of giving the right answer,

1047
00:53:29,680 --> 00:53:33,000
Speaker 5: you give them questions to help them think and come

1048
00:53:33,000 --> 00:53:35,720
Speaker 5: to the right answer for themselves. So the attitude section

1049
00:53:35,920 --> 00:53:38,480
Speaker 5: is typically a little bit shorter, but again it's just

1050
00:53:38,560 --> 00:53:40,839
Speaker 5: telling it all, right, this is who you are, this

1051
00:53:40,880 --> 00:53:43,200
Speaker 5: is how you're gonna step to me. And the last part,

1052
00:53:43,280 --> 00:53:46,239
Speaker 5: which is one of the most important, is the actual task. Right.

1053
00:53:47,120 --> 00:53:50,239
Speaker 5: So the task is the specific details on what you

1054
00:53:50,320 --> 00:53:52,600
Speaker 5: want the AI to do and how you want it

1055
00:53:52,680 --> 00:53:56,279
Speaker 5: to do it. Now, oftentimes we only focus on the method, right,

1056
00:53:56,360 --> 00:53:58,040
Speaker 5: So we'll be like, Yo, I want you to ask

1057
00:53:58,080 --> 00:54:00,319
Speaker 5: me a question and then provide an answer. I want

1058
00:54:00,360 --> 00:54:02,239
Speaker 5: you to explain yourself. I want you to give me

1059
00:54:02,280 --> 00:54:05,280
Speaker 5: positive and negative feedback on this idea, but we don't

1060
00:54:05,320 --> 00:54:08,440
Speaker 5: necessarily get specific enough to tell it the format that

1061
00:54:08,440 --> 00:54:09,160
Speaker 5: we want that in.

1062
00:54:09,400 --> 00:54:09,600
Speaker 4: Right.

1063
00:54:09,960 --> 00:54:12,360
Speaker 5: So if you just tell chat GPT to ask me

1064
00:54:12,480 --> 00:54:15,800
Speaker 5: questions about my idea, it's gonna spit out like twenty

1065
00:54:15,880 --> 00:54:19,000
Speaker 5: questions for you, right, and then it's gonna expect you

1066
00:54:19,040 --> 00:54:21,239
Speaker 5: to answer all of those. So that's very different than

1067
00:54:21,280 --> 00:54:25,080
Speaker 5: saying ask me one to two brief questions at a time,

1068
00:54:25,600 --> 00:54:28,680
Speaker 5: wait for my response, and keep doing that until you

1069
00:54:28,719 --> 00:54:31,239
Speaker 5: get enough information to help me write my business plan.

1070
00:54:32,200 --> 00:54:34,560
Speaker 5: That's gonna have a whole different interaction than if it

1071
00:54:34,600 --> 00:54:36,759
Speaker 5: fits out twenty questions and you don't even know how

1072
00:54:36,760 --> 00:54:38,839
Speaker 5: to answer them, and then it'll mess up the flow

1073
00:54:38,880 --> 00:54:40,919
Speaker 5: of your chat. So you could tell it like, hey,

1074
00:54:40,960 --> 00:54:43,239
Speaker 5: I want this in a list, So, hey, I want

1075
00:54:43,280 --> 00:54:47,319
Speaker 5: a list of five viral TikTok ideas to promote what

1076
00:54:47,440 --> 00:54:49,600
Speaker 5: I'm doing, and I want them to be the strongest,

1077
00:54:49,640 --> 00:54:52,200
Speaker 5: I want them to be funny, right, Or hey, I

1078
00:54:52,280 --> 00:54:54,799
Speaker 5: want this in a table, so it'll literally output it

1079
00:54:54,840 --> 00:54:57,520
Speaker 5: in what looks like an Excel spreadsheet. So I want

1080
00:54:57,520 --> 00:55:00,200
Speaker 5: a table of ideas, and the first column should be

1081
00:55:00,560 --> 00:55:03,239
Speaker 5: the number of the ideas, so one through five. The

1082
00:55:03,280 --> 00:55:06,120
Speaker 5: second column should be a description of the video, the

1083
00:55:06,160 --> 00:55:09,000
Speaker 5: third column should be an explanation of why you think

1084
00:55:09,040 --> 00:55:12,160
Speaker 5: that video will work, and a fourth column will be

1085
00:55:12,239 --> 00:55:14,200
Speaker 5: what you think the cons are of that, and the

1086
00:55:14,239 --> 00:55:16,440
Speaker 5: fifth column will be what you think the pros are

1087
00:55:16,480 --> 00:55:19,520
Speaker 5: of that. Now answer my question. That's a lot different

1088
00:55:19,520 --> 00:55:21,719
Speaker 5: than just spitting out a list of ideas. Now you're

1089
00:55:21,719 --> 00:55:24,600
Speaker 5: getting information in a structured way for you to see

1090
00:55:24,960 --> 00:55:27,120
Speaker 5: how it thinks and why it thinks, so you can

1091
00:55:27,120 --> 00:55:29,680
Speaker 5: tweak it as you go. So an example of the

1092
00:55:29,760 --> 00:55:32,200
Speaker 5: task part would be like right now, you're a meeting

1093
00:55:32,239 --> 00:55:35,759
Speaker 5: with me. I'm trying to create a startup business, a

1094
00:55:35,760 --> 00:55:39,000
Speaker 5: business plan for my startup that will connect like U,

1095
00:55:39,840 --> 00:55:44,359
Speaker 5: that'll connect parents to background check babysitters. First, I'm gonna

1096
00:55:44,400 --> 00:55:47,799
Speaker 5: provide you with ideas that I already have about the

1097
00:55:47,800 --> 00:55:51,120
Speaker 5: business plan. Then you're gonna ask me one to two

1098
00:55:51,200 --> 00:55:55,560
Speaker 5: brief questions at a time about my idea about this app. Uh,

1099
00:55:55,880 --> 00:55:57,600
Speaker 5: you know that we want to build until you have

1100
00:55:57,760 --> 00:56:01,160
Speaker 5: enough information to help us create a comprehensive business plan

1101
00:56:01,520 --> 00:56:04,560
Speaker 5: that will help us get investment from a VC firm.

1102
00:56:04,920 --> 00:56:08,359
Speaker 5: Along the way, you're gonna provide positive and negative feedback. Right,

1103
00:56:09,160 --> 00:56:12,560
Speaker 5: So that's gonna completely changed versus me saying I want

1104
00:56:12,600 --> 00:56:16,240
Speaker 5: you to write me a business plan about an app

1105
00:56:16,280 --> 00:56:21,280
Speaker 5: that connects you know, parents to background check babysitters. Having

1106
00:56:21,400 --> 00:56:25,359
Speaker 5: that conversation versus conversation that's done in this format, in

1107
00:56:25,400 --> 00:56:29,160
Speaker 5: this chat in the hat format is gonna be world's difference.

1108
00:56:29,480 --> 00:56:32,640
Speaker 5: And instead of chat GPT feeding you whatever answer it

1109
00:56:32,680 --> 00:56:35,680
Speaker 5: happens to calculate, it's now going to be taking in

1110
00:56:35,719 --> 00:56:39,719
Speaker 5: your actual genius, your actual ideas, and just helping you

1111
00:56:39,800 --> 00:56:42,759
Speaker 5: expand upon them and structure them in a way that

1112
00:56:42,880 --> 00:56:45,800
Speaker 5: lets you do stuff faster. Now it's not just about

1113
00:56:45,920 --> 00:56:49,000
Speaker 5: writing business plans or coming up with social media ideas.

1114
00:56:49,280 --> 00:56:52,520
Speaker 5: You can do this for like, let's say that you

1115
00:56:52,600 --> 00:56:55,440
Speaker 5: have a sales presentation coming up. You can give it.

1116
00:56:55,520 --> 00:56:58,879
Speaker 5: You're a master salesperson, You're great at creating decks. You're

1117
00:56:58,920 --> 00:57:01,960
Speaker 5: extremely helpful, love helping me. What's gonna happen is I'm

1118
00:57:02,000 --> 00:57:04,160
Speaker 5: going to tell you about my client the things I

1119
00:57:04,160 --> 00:57:06,319
Speaker 5: want to include in my sales deck, and you're gonna

1120
00:57:06,360 --> 00:57:08,719
Speaker 5: help me create an outline for the sales deck. So

1121
00:57:08,760 --> 00:57:12,000
Speaker 5: then it'll do that. Okay, Now I want to deep

1122
00:57:12,080 --> 00:57:15,120
Speaker 5: dive into this slide. For this slide, you know, I

1123
00:57:15,160 --> 00:57:17,960
Speaker 5: want it to be really impacted, you know, really impactful

1124
00:57:18,000 --> 00:57:20,160
Speaker 5: in the story that it tells. To make sure that

1125
00:57:20,240 --> 00:57:23,720
Speaker 5: it's teaching, make sure that my client really walks away

1126
00:57:23,800 --> 00:57:27,000
Speaker 5: understanding how we're the industry leader. Can you generate a

1127
00:57:27,040 --> 00:57:30,240
Speaker 5: table with three different options for text that I should

1128
00:57:30,240 --> 00:57:32,880
Speaker 5: put on this slide, and then it's gonna output that.

1129
00:57:33,040 --> 00:57:36,720
Speaker 5: So now again it's taking your genius and what you

1130
00:57:36,840 --> 00:57:39,360
Speaker 5: already know and what your goals are and just making

1131
00:57:39,400 --> 00:57:43,120
Speaker 5: your workflow faster. But by pulling from this expertise, it

1132
00:57:43,240 --> 00:57:46,760
Speaker 5: is imaginary character that you've created. And so again the

1133
00:57:46,840 --> 00:57:49,840
Speaker 5: chat and a hat framework is around when you create

1134
00:57:49,840 --> 00:57:52,840
Speaker 5: your prompt, you want to create a history. What are

1135
00:57:52,840 --> 00:57:56,040
Speaker 5: the knowledge and skills and expertise that it needs to

1136
00:57:56,080 --> 00:57:59,040
Speaker 5: have to help you with your task? What experience does

1137
00:57:59,040 --> 00:58:01,960
Speaker 5: it have? What company are relevant to the task you're

1138
00:58:01,960 --> 00:58:04,120
Speaker 5: trying to do? That you could say it has experience

1139
00:58:04,240 --> 00:58:06,320
Speaker 5: there you could be like, yo, you were you know

1140
00:58:06,360 --> 00:58:10,439
Speaker 5: the first social media person that you know, frickin' DoorDash ever? Right,

1141
00:58:10,520 --> 00:58:13,439
Speaker 5: And then you want to give it specific accomplishments when

1142
00:58:13,440 --> 00:58:15,560
Speaker 5: you have the a the attitude, it's what kind of

1143
00:58:15,640 --> 00:58:17,920
Speaker 5: vibe does it have when it's helping you? So you

1144
00:58:18,000 --> 00:58:20,440
Speaker 5: think about the style and you think about the tone.

1145
00:58:20,720 --> 00:58:23,200
Speaker 5: So the style is what's the vibe? The tone is

1146
00:58:23,240 --> 00:58:25,640
Speaker 5: what kind of language does it use when it creates

1147
00:58:25,680 --> 00:58:28,040
Speaker 5: the answer for you? And the last part is t

1148
00:58:28,400 --> 00:58:31,080
Speaker 5: or the task, which is how does it respond to you?

1149
00:58:31,200 --> 00:58:31,400
Speaker 3: Right?

1150
00:58:31,640 --> 00:58:33,560
Speaker 5: So you want to tell it exactly what you wanted

1151
00:58:33,600 --> 00:58:35,600
Speaker 5: to do and how you wanted to do it, right,

1152
00:58:35,680 --> 00:58:38,240
Speaker 5: like is it going to ask questions and provide answers?

1153
00:58:38,280 --> 00:58:40,920
Speaker 5: Does it give you positive negative feedback? And even on

1154
00:58:40,960 --> 00:58:44,240
Speaker 5: the format, like what's the structure that it uses when responding.

1155
00:58:44,320 --> 00:58:46,040
Speaker 5: Is it responding to you in a list, is it

1156
00:58:46,040 --> 00:58:48,440
Speaker 5: giving you a table? Is there a certain amount? Is

1157
00:58:48,480 --> 00:58:51,160
Speaker 5: it a paragraph, is it a long answer, a short answer,

1158
00:58:51,240 --> 00:58:54,920
Speaker 5: et cetera. And the more you, guys, will leverage this framework,

1159
00:58:55,200 --> 00:58:58,920
Speaker 5: the more valuable that these tools will become to helping

1160
00:58:59,080 --> 00:59:02,600
Speaker 5: you operate in any element of your business day to

1161
00:59:02,680 --> 00:59:06,800
Speaker 5: day in a way that you'll be the one that's

1162
00:59:06,840 --> 00:59:09,720
Speaker 5: the person using AI that will replace the jobs of

1163
00:59:09,720 --> 00:59:13,880
Speaker 5: the people who are not instead of you hoping that

1164
00:59:14,000 --> 00:59:16,880
Speaker 5: chat GPT gives you some type of answer randomly, or

1165
00:59:16,880 --> 00:59:19,720
Speaker 5: that Claude or Gemini does that'll help you have a

1166
00:59:19,760 --> 00:59:23,160
Speaker 5: competitive edge, because the unique competitive edge is you. And

1167
00:59:23,200 --> 00:59:27,080
Speaker 5: so the chat Nahat framework helps you leverage these large

1168
00:59:27,160 --> 00:59:30,880
Speaker 5: language models in a way where it amplifies your genius

1169
00:59:31,160 --> 00:59:34,960
Speaker 5: alongside AI, instead of expecting AI to somehow make you

1170
00:59:35,000 --> 00:59:36,000
Speaker 5: a genius. You feel me.

1171
00:59:37,480 --> 00:59:39,600
Speaker 1: I feel you. I definitely feel you. When we talk

1172
00:59:39,600 --> 00:59:41,360
Speaker 1: about expert, we'm an expert.

1173
00:59:41,560 --> 00:59:45,600
Speaker 4: That's a million dollars worth of a game right there. Minimum,

1174
00:59:45,800 --> 00:59:47,520
Speaker 4: I said, at least a million dollars worth of game.

1175
00:59:47,560 --> 00:59:50,840
Speaker 4: But I think we're gonna do something some on the

1176
00:59:50,920 --> 00:59:52,080
Speaker 4: flywork are.

1177
00:59:51,920 --> 00:59:54,440
Speaker 5: We let's do it. So I'm gonna do like this,

1178
00:59:54,520 --> 00:59:57,200
Speaker 5: and I'm gonna maybe minimize this and make this bigger.

1179
00:59:57,200 --> 00:59:59,200
Speaker 5: Oh no, I don't want to leave. I just don't

1180
00:59:59,240 --> 01:00:01,160
Speaker 5: want y'all to see a'all all of that. All right,

1181
01:00:01,240 --> 01:00:04,360
Speaker 5: So let's go back here. So let's make up an

1182
01:00:04,360 --> 01:00:06,760
Speaker 5: example task right now, you guys, maybe in a.

1183
01:00:07,480 --> 01:00:09,439
Speaker 4: Yeah, let's do this through the chat, because I don't

1184
01:00:09,440 --> 01:00:12,480
Speaker 4: want them to be like, oh, we already thought about this, okay,

1185
01:00:13,120 --> 01:00:14,320
Speaker 4: So in the chat.

1186
01:00:14,440 --> 01:00:16,800
Speaker 5: Like what we are they have to do.

1187
01:00:16,800 --> 01:00:18,200
Speaker 3: They have to come with like a business idea.

1188
01:00:18,880 --> 01:00:21,080
Speaker 5: Yeah, just like what like something we'd wanted to do,

1189
01:00:21,160 --> 01:00:23,640
Speaker 5: like a business plan for like an e commerce thing

1190
01:00:23,760 --> 01:00:25,720
Speaker 5: or like you know, help me come up with viral

1191
01:00:25,800 --> 01:00:28,560
Speaker 5: TikTok campaigns for this kind of business. It's something that

1192
01:00:28,880 --> 01:00:32,200
Speaker 5: is a specific task, not anything, but a specific task

1193
01:00:32,280 --> 01:00:34,800
Speaker 5: you want to see used in this framework, all right?

1194
01:00:34,840 --> 01:00:35,280
Speaker 1: All right?

1195
01:00:35,320 --> 01:00:38,320
Speaker 3: So type somebody type in the chat, see see what

1196
01:00:38,320 --> 01:00:38,880
Speaker 3: they got what.

1197
01:00:38,800 --> 01:00:41,200
Speaker 4: You want to see, and we'll do this on real

1198
01:00:41,280 --> 01:00:44,240
Speaker 4: time like a magic trick on forty second Street.

1199
01:00:46,720 --> 01:00:47,560
Speaker 5: Yo, a wild.

1200
01:00:50,240 --> 01:00:52,400
Speaker 2: All right, I've seen one already right here. That sounds

1201
01:00:52,400 --> 01:00:55,439
Speaker 2: pretty dopey. They want to help create all this moving

1202
01:00:55,520 --> 01:00:58,439
Speaker 2: so fast. A technology recruiting company business plan?

1203
01:01:02,520 --> 01:01:03,480
Speaker 1: Oh what? Oh?

1204
01:01:03,560 --> 01:01:06,920
Speaker 2: Might put it up shout to my technology recruiting company

1205
01:01:06,920 --> 01:01:07,360
Speaker 2: busines plan.

1206
01:01:07,440 --> 01:01:10,160
Speaker 4: A technology recruiting company business plan? Is that a good one?

1207
01:01:10,600 --> 01:01:12,280
Speaker 5: Yep, let's do it. So we're gonna do this in

1208
01:01:12,400 --> 01:01:14,920
Speaker 5: each one, right, So I'm gonna put it here and

1209
01:01:14,920 --> 01:01:16,720
Speaker 5: put it here so we could see all the way

1210
01:01:16,760 --> 01:01:19,720
Speaker 5: through each one. So what I want to do first

1211
01:01:19,760 --> 01:01:21,960
Speaker 5: is I want to show you, guys, what it looks

1212
01:01:22,040 --> 01:01:24,400
Speaker 5: like when we have one of these large language models

1213
01:01:24,440 --> 01:01:27,200
Speaker 5: generate this without the chat and a hat framework. Right,

1214
01:01:27,680 --> 01:01:29,480
Speaker 5: So this is an app that I use. It's only

1215
01:01:29,560 --> 01:01:33,360
Speaker 5: available on Mac. It's called think Buddy, and basically you

1216
01:01:33,520 --> 01:01:36,000
Speaker 5: pay one fee and you get access to all of

1217
01:01:36,000 --> 01:01:38,320
Speaker 5: the AI models that you would want to use. So

1218
01:01:38,360 --> 01:01:42,320
Speaker 5: I could use you know, Chat, GPT four point zero, Gemini, Claude,

1219
01:01:42,680 --> 01:01:46,360
Speaker 5: et cetera, even Lama just by paying one fee. They

1220
01:01:46,400 --> 01:01:50,400
Speaker 5: are working on a mobile app. However, it's not out yet,

1221
01:01:50,440 --> 01:01:52,760
Speaker 5: so if you have a Mac, I highly recommend this software.

1222
01:01:53,080 --> 01:01:55,400
Speaker 5: You guys just missed out on a lifetime deal. Like

1223
01:01:55,440 --> 01:01:57,080
Speaker 5: I paid one hundred and fifty dollars and I have

1224
01:01:57,480 --> 01:02:00,720
Speaker 5: forever free access to this app, but the month the subscription.

1225
01:02:01,040 --> 01:02:03,240
Speaker 5: Instead of you paying twenty dollars for Chat GBT, then

1226
01:02:03,280 --> 01:02:06,600
Speaker 5: twenty dollars for you know, Perplexity, then twenty dollars for

1227
01:02:06,640 --> 01:02:09,439
Speaker 5: this one, it's like twenty dollars for this app where

1228
01:02:09,480 --> 01:02:12,040
Speaker 5: you get access to all of them unlimited and it

1229
01:02:12,080 --> 01:02:14,080
Speaker 5: has like the image generation and stuff like that.

1230
01:02:14,160 --> 01:02:15,160
Speaker 3: What's the neighborhood.

1231
01:02:15,480 --> 01:02:18,120
Speaker 5: It's called think Buddy, so t A I, n K,

1232
01:02:18,480 --> 01:02:20,959
Speaker 5: b U, D D Y and it's Thinkbuddy dot AI.

1233
01:02:21,120 --> 01:02:22,640
Speaker 5: So I could drop like a code if you guys

1234
01:02:22,640 --> 01:02:24,200
Speaker 5: want to get it, but I have a couple of

1235
01:02:24,240 --> 01:02:26,040
Speaker 5: tools I got to drop for y'all. The first is

1236
01:02:26,080 --> 01:02:29,040
Speaker 5: the tool that helps you find AI anything. The second

1237
01:02:29,080 --> 01:02:31,000
Speaker 5: is the lead scoring and then I'll drop the info

1238
01:02:31,080 --> 01:02:34,000
Speaker 5: for think Buddy when I'm done with this demo here.

1239
01:02:34,080 --> 01:02:36,080
Speaker 2: Yeah, shout out to Ian and the chat with the

1240
01:02:36,120 --> 01:02:38,040
Speaker 2: assistance on that. I appreciate you, bro.

1241
01:02:39,080 --> 01:02:41,240
Speaker 5: So what I'm asking it to do right here is

1242
01:02:41,280 --> 01:02:44,360
Speaker 5: I'm saying, create a business plan for a technology recruiting company,

1243
01:02:44,840 --> 01:02:50,240
Speaker 5: right and this is oh, let's retry uh oh not

1244
01:02:50,400 --> 01:02:52,200
Speaker 5: it wants to tank out all right, Let's maybe we

1245
01:02:52,200 --> 01:02:56,080
Speaker 5: should use chat GPT. I'm talking all this chat GPT stuff, right,

1246
01:02:56,120 --> 01:02:57,000
Speaker 5: and so maybe.

1247
01:02:56,760 --> 01:03:01,280
Speaker 4: It's chat GBT is the reliable Yeah, it's a reliable source.

1248
01:03:01,640 --> 01:03:02,320
Speaker 1: Can't go wrong with.

1249
01:03:03,600 --> 01:03:08,000
Speaker 5: Okay, let me come out of clot. Do it in clot.

1250
01:03:08,480 --> 01:03:10,680
Speaker 5: Hold on, y'all, not me trying to promote this and

1251
01:03:10,720 --> 01:03:15,040
Speaker 5: then it don't want to work here. Let me change this.

1252
01:03:15,040 --> 01:03:17,440
Speaker 1: This is the next level and we're.

1253
01:03:17,280 --> 01:03:20,440
Speaker 5: Gonna open it back up. All right now, let me

1254
01:03:20,480 --> 01:03:23,760
Speaker 5: test why is it still trying to do it in clot?

1255
01:03:24,000 --> 01:03:25,520
Speaker 5: Do I need to update something?

1256
01:03:26,560 --> 01:03:26,760
Speaker 1: Huh?

1257
01:03:28,160 --> 01:03:31,520
Speaker 5: Is it my app? Let me see? And if not,

1258
01:03:31,640 --> 01:03:33,680
Speaker 5: I'll just open up chat GPT and show you all

1259
01:03:33,680 --> 01:03:35,919
Speaker 5: the difference. Like literally, I just don't pay for chat

1260
01:03:35,960 --> 01:03:39,320
Speaker 5: GPT outside of this, and I've never had this issue

1261
01:03:39,320 --> 01:03:43,960
Speaker 5: in here. Let me see. Okay, chat all right, let

1262
01:03:44,000 --> 01:03:48,200
Speaker 5: me go back to this one and let's set Okay,

1263
01:03:49,000 --> 01:03:54,480
Speaker 5: let's go back. Okay, let's try it again with chat

1264
01:03:54,520 --> 01:03:59,680
Speaker 5: GPT for general. Okay, let me try a new chat

1265
01:04:00,120 --> 01:04:03,720
Speaker 5: up with the app on my computer because it's not

1266
01:04:03,840 --> 01:04:06,600
Speaker 5: letting me do this. So instead of what I'll do

1267
01:04:06,760 --> 01:04:09,640
Speaker 5: to save us time, I promise it works, y'all, I

1268
01:04:09,720 --> 01:04:12,440
Speaker 5: promise it works, right, So instead what I'll do is

1269
01:04:12,440 --> 01:04:14,920
Speaker 5: we'll just go into Claude and try it right now. Right,

1270
01:04:15,480 --> 01:04:17,720
Speaker 5: So while I'm in Claude, I'll say, hey, create a

1271
01:04:17,760 --> 01:04:22,440
Speaker 5: business plan for a technology recruiting company, and then what

1272
01:04:22,440 --> 01:04:26,600
Speaker 5: it'll do is it'll spit out an answer. Right, So

1273
01:04:26,640 --> 01:04:32,080
Speaker 5: it starts to immediately answer without understanding what type of

1274
01:04:32,120 --> 01:04:34,840
Speaker 5: technology recruiting I want to do, what my market is,

1275
01:04:35,240 --> 01:04:37,520
Speaker 5: what my region is, or anything. And it comes up

1276
01:04:37,560 --> 01:04:40,520
Speaker 5: with a pretty decent idea. It says, here's an executive summary.

1277
01:04:40,560 --> 01:04:42,760
Speaker 5: Didn't even ask me the name of my company. I

1278
01:04:42,800 --> 01:04:44,600
Speaker 5: don't know the person in a chat. Would you call

1279
01:04:44,640 --> 01:04:46,760
Speaker 5: it tech Talent Solutions? That sound like one of them

1280
01:04:46,760 --> 01:04:48,200
Speaker 5: outsourced companies in India.

1281
01:04:49,200 --> 01:04:50,920
Speaker 1: That's not a bad name, it's actually not.

1282
01:04:51,920 --> 01:04:53,800
Speaker 5: I'm pretty sure if I went to Google that there

1283
01:04:53,800 --> 01:04:57,520
Speaker 5: would be one that's called Tech Talent Solutions, right. But

1284
01:04:57,560 --> 01:05:01,200
Speaker 5: it says, hey, it's a specialized technology recruiting firm focused

1285
01:05:01,240 --> 01:05:04,640
Speaker 5: on connecting top tier tech talent with innovative companies by

1286
01:05:04,800 --> 01:05:08,280
Speaker 5: leveraging advanced matching algorithms. So now it already signs you

1287
01:05:08,360 --> 01:05:10,640
Speaker 5: up to use AI. So if you weren't planning to

1288
01:05:10,760 --> 01:05:13,240
Speaker 5: use AI, eol company, it already signs you up to

1289
01:05:13,320 --> 01:05:17,520
Speaker 5: create your own proprietary matching algorithms, right, and then your

1290
01:05:17,560 --> 01:05:20,800
Speaker 5: network of professionals. We aim to streamline the hiring process

1291
01:05:20,800 --> 01:05:23,520
Speaker 5: for both employers and job seekers in the sector. So

1292
01:05:23,560 --> 01:05:26,200
Speaker 5: then it goes into your market analysis, your target market,

1293
01:05:26,520 --> 01:05:30,400
Speaker 5: tech startups and scale ups, established technology companies, traditional companies

1294
01:05:30,480 --> 01:05:34,000
Speaker 5: undergoing digital transformation. But what happens if you're somebody who

1295
01:05:34,040 --> 01:05:37,520
Speaker 5: is a federal technology recruiting company where you're seeking folks

1296
01:05:37,520 --> 01:05:41,200
Speaker 5: who have clearances right to be able to place them places,

1297
01:05:41,240 --> 01:05:46,440
Speaker 5: and you're looking at primary prime contractors and subcontractors and

1298
01:05:46,480 --> 01:05:49,440
Speaker 5: that's actually your audience. Now all of this becomes completely

1299
01:05:49,520 --> 01:05:52,040
Speaker 5: useless because it doesn't have any info about you. And

1300
01:05:52,080 --> 01:05:54,800
Speaker 5: then the types of candidates. Maybe you've decided that you

1301
01:05:54,880 --> 01:05:59,120
Speaker 5: do everything except software engineers, right, or developers like you do,

1302
01:05:59,480 --> 01:06:03,880
Speaker 5: technical project managers, you want to do quality analysis, maybe

1303
01:06:03,920 --> 01:06:06,720
Speaker 5: you'll do like data analysts, you want to do product managers,

1304
01:06:06,720 --> 01:06:09,400
Speaker 5: you want to do UIUX designers. Well, now you're gonna

1305
01:06:09,400 --> 01:06:11,400
Speaker 5: have to come in here edit all of this. Then

1306
01:06:11,400 --> 01:06:14,080
Speaker 5: it talks about the market size, which again I don't

1307
01:06:14,120 --> 01:06:16,320
Speaker 5: know if this fact is true, so I'm gonna have

1308
01:06:16,360 --> 01:06:19,280
Speaker 5: to google it, right, And then it has like increasing

1309
01:06:19,320 --> 01:06:21,680
Speaker 5: demand due to this, I don't know if any of

1310
01:06:21,720 --> 01:06:24,440
Speaker 5: that's true. It's an LLM. I have to go double

1311
01:06:24,520 --> 01:06:27,600
Speaker 5: check and then competitive analysis. It doesn't give you any

1312
01:06:27,640 --> 01:06:31,440
Speaker 5: specific competitors, right, and then what your advantage is, et cetera.

1313
01:06:31,520 --> 01:06:33,800
Speaker 5: So you guys get the idea here. It's spitting out

1314
01:06:33,880 --> 01:06:36,720
Speaker 5: something that feels good on the surface, but then it

1315
01:06:36,760 --> 01:06:40,240
Speaker 5: doesn't necessarily capture your unique value and what you create.

1316
01:06:40,480 --> 01:06:43,520
Speaker 5: So let's go through this framework and actually create something. Right,

1317
01:06:43,760 --> 01:06:45,760
Speaker 5: So we want to create a business plan for a

1318
01:06:45,800 --> 01:06:49,200
Speaker 5: technology recruiting company. So first we're gonna give it a history.

1319
01:06:49,200 --> 01:06:55,800
Speaker 5: We're gonna say you are an award winning, world renowned

1320
01:07:10,440 --> 01:07:17,320
Speaker 5: original business plan for what's a big IT staffing firm

1321
01:07:17,520 --> 01:07:19,520
Speaker 5: like that actually exists in the world, like one of

1322
01:07:19,520 --> 01:07:20,720
Speaker 5: them big recruiting firm.

1323
01:07:20,960 --> 01:07:22,680
Speaker 3: Can we look it up. We'll look it up.

1324
01:07:22,840 --> 01:07:25,640
Speaker 5: We're looking it up, all right, So I'm gonna add

1325
01:07:25,640 --> 01:07:36,000
Speaker 5: that in here. First, one million dollars in revenue to

1326
01:07:36,280 --> 01:07:43,400
Speaker 5: over ten million dollars in revenue annually. You are the

1327
01:07:43,520 --> 01:07:52,920
Speaker 5: chair of the Harvard Business School and have over twenty

1328
01:07:53,240 --> 01:08:04,280
Speaker 5: years experience helping companies develop UH business plans, helping technology

1329
01:08:04,320 --> 01:08:15,080
Speaker 5: service companies, service companies develop business plans that that helped

1330
01:08:16,400 --> 01:08:21,120
Speaker 5: that drive them to become industry leaders.

1331
01:08:21,160 --> 01:08:21,680
Speaker 1: I like that.

1332
01:08:22,560 --> 01:08:24,880
Speaker 2: Yeah, the chat is putting. I looked up a center.

1333
01:08:25,080 --> 01:08:26,880
Speaker 2: The chat put a censure. We can put a center,

1334
01:08:27,680 --> 01:08:28,120
Speaker 2: all right.

1335
01:08:28,560 --> 01:08:30,479
Speaker 5: Right, So now I'm just gonna have to go in

1336
01:08:30,520 --> 01:08:32,320
Speaker 5: here real quick and make this text just a little

1337
01:08:32,360 --> 01:08:35,680
Speaker 5: bit smaller, y'all. So let's go ahead and make this

1338
01:08:35,760 --> 01:08:36,799
Speaker 5: a little bit smaller.

1339
01:08:37,040 --> 01:08:37,240
Speaker 1: Right.

1340
01:08:37,280 --> 01:08:40,200
Speaker 5: So, you are an award winning, world renowned expert at

1341
01:08:40,240 --> 01:08:44,040
Speaker 5: creating business plans for technology services companies. You created the

1342
01:08:44,080 --> 01:08:49,400
Speaker 5: original business plan for Accenter's recruiting department. You scale them

1343
01:08:49,400 --> 01:08:51,800
Speaker 5: from their first million dollars in revenue to over ten

1344
01:08:51,840 --> 01:08:54,920
Speaker 5: billion and revenue annually. Now, if I wanted to, I

1345
01:08:54,960 --> 01:08:56,640
Speaker 5: can make it one hundred million. I don't really have

1346
01:08:56,720 --> 01:09:00,240
Speaker 5: to know if accenter actually makes that much money, right,

1347
01:09:00,280 --> 01:09:03,080
Speaker 5: I'm just trying to give it an anecdotal example.

1348
01:09:03,200 --> 01:09:06,400
Speaker 1: They said, not a staff in front one of the

1349
01:09:06,479 --> 01:09:09,240
Speaker 1: largest India. So are they a staff infirm or no?

1350
01:09:09,880 --> 01:09:13,360
Speaker 5: What I said, all right, let's let's do this for

1351
01:09:13,479 --> 01:09:20,840
Speaker 5: technology instead of services companies, for technology recruiting companies. Right,

1352
01:09:21,200 --> 01:09:23,760
Speaker 5: and so instead, here's how I'm going to change this.

1353
01:09:25,240 --> 01:09:36,200
Speaker 5: You created the talents uh, the recruiting department at Accenture,

1354
01:09:38,360 --> 01:09:42,920
Speaker 5: where you scaled them from their first million dollars in

1355
01:09:43,000 --> 01:09:46,839
Speaker 5: revenue to over ten billion dollars in UH revenue annually.

1356
01:09:47,640 --> 01:09:53,080
Speaker 5: Then you spent over a decade at Boston Consulting Group

1357
01:09:54,640 --> 01:09:58,719
Speaker 5: where you developed a what are they called head Hunter

1358
01:10:01,680 --> 01:10:12,759
Speaker 5: talent recruiting and matching service that you scaled from zero

1359
01:10:13,760 --> 01:10:21,519
Speaker 5: zero to over two billion dollars in revenue annually. You

1360
01:10:21,760 --> 01:10:31,960
Speaker 5: are the leading expert in developing business plans and strategy

1361
01:10:33,439 --> 01:10:39,320
Speaker 5: strategy for technology recruiting companies. Right, so you see how

1362
01:10:39,320 --> 01:10:41,960
Speaker 5: I did that. It doesn't matter if like these things

1363
01:10:42,080 --> 01:10:45,480
Speaker 5: actually are there. But because I'm not sure if Accenture,

1364
01:10:45,520 --> 01:10:47,600
Speaker 5: I know they're a consulting firm, but I don't know

1365
01:10:47,640 --> 01:10:50,760
Speaker 5: if they're like a talent recruiting company. I just gave

1366
01:10:50,800 --> 01:10:53,360
Speaker 5: it that example. Like you created the recruiting department at

1367
01:10:53,400 --> 01:10:56,360
Speaker 5: Accentre where you scared them from their first million in

1368
01:10:56,400 --> 01:10:59,519
Speaker 5: revenue to over ten billion in revenue and annually. Then

1369
01:10:59,600 --> 01:11:01,519
Speaker 5: you spend over a decade. I'm gonna actually make it

1370
01:11:01,520 --> 01:11:04,240
Speaker 5: one billion. Then you spent over a decade at Boston

1371
01:11:04,240 --> 01:11:07,200
Speaker 5: Consulting Group where you developed a Headhunter talent recruiting and

1372
01:11:07,240 --> 01:11:09,960
Speaker 5: matching service that you scaled from zero to over ten

1373
01:11:10,000 --> 01:11:14,040
Speaker 5: billion dollars in revenue annually. Right, you are the leading

1374
01:11:14,080 --> 01:11:18,120
Speaker 5: expert in developing business plans and strategy for technology recruiting companies.

1375
01:11:18,280 --> 01:11:20,080
Speaker 5: You are the chair of the Harvard Business School and

1376
01:11:20,120 --> 01:11:23,479
Speaker 5: have over twenty years experience. Okay, you are the chair

1377
01:11:23,520 --> 01:11:28,559
Speaker 5: of the Harvard Business School and have a deep passion

1378
01:11:30,000 --> 01:11:32,920
Speaker 5: and I'll just leave it there. I don't have to

1379
01:11:32,920 --> 01:11:35,200
Speaker 5: add all this in at the end. You are the

1380
01:11:35,280 --> 01:11:40,120
Speaker 5: chair of the Harvard Business School and the leading industry

1381
01:11:40,320 --> 01:11:49,200
Speaker 5: expert at helping chech recruiting companies create business plans that

1382
01:11:50,640 --> 01:11:55,639
Speaker 5: drive them to become industry leaders. Right now, let's talk

1383
01:11:55,640 --> 01:11:57,800
Speaker 5: about this attitude. I'm gonna make this a little bit

1384
01:11:57,800 --> 01:12:01,640
Speaker 5: Smaller's getting a little bit verbose here, but the attitude.

1385
01:12:02,040 --> 01:12:09,120
Speaker 5: You are extremely helpful. You love sharing your knowledge. You

1386
01:12:09,280 --> 01:12:18,040
Speaker 5: love leveraging your knowledge and wisdom, your deep knowledge and wisdom,

1387
01:12:18,120 --> 01:12:24,519
Speaker 5: not depth, your deep knowledge and wisdom to help to

1388
01:12:24,840 --> 01:12:33,720
Speaker 5: mentor companies, to mentor up and coming tech recruiting companies

1389
01:12:34,400 --> 01:12:44,960
Speaker 5: on developing their business plans and strategy. You prefer to

1390
01:12:45,280 --> 01:12:54,560
Speaker 5: help people help themselves. You provide positive and negative feedback.

1391
01:12:54,640 --> 01:12:57,280
Speaker 5: So let's do this. You prefer to help people help themselves,

1392
01:12:58,000 --> 01:13:04,280
Speaker 5: giving insights on how to think about an answer, versus

1393
01:13:04,439 --> 01:13:15,280
Speaker 5: giving the answer outright, right, you are especially passionate about

1394
01:13:15,320 --> 01:13:24,600
Speaker 5: helping entrepreneurs from underrepresented backgrounds. We're gonna give it a

1395
01:13:24,640 --> 01:13:29,960
Speaker 5: savior complex. And so the last piece is, now, what

1396
01:13:30,080 --> 01:13:31,439
Speaker 5: do we want it to do and how do we

1397
01:13:31,479 --> 01:13:36,240
Speaker 5: want it to do it? Right now, you're meeting with me,

1398
01:13:37,240 --> 01:13:44,360
Speaker 5: a person of color who is creating a new technology

1399
01:13:44,720 --> 01:13:55,160
Speaker 5: staffing company. I have some ideas for the company I

1400
01:13:55,280 --> 01:14:02,919
Speaker 5: want to create, but I'm struggling to develop a business plan. First,

1401
01:14:03,320 --> 01:14:10,680
Speaker 5: you'll listen to my ideas about the company before generating

1402
01:14:10,840 --> 01:14:16,799
Speaker 5: a response. Then you'll ask me one to two brief

1403
01:14:16,920 --> 01:14:25,360
Speaker 5: questions at a time until you have enough information to

1404
01:14:25,600 --> 01:14:32,880
Speaker 5: create a business plan that will drive us to success.

1405
01:14:34,200 --> 01:14:40,679
Speaker 5: Until you have enough information to yeah, okay. You will

1406
01:14:42,680 --> 01:14:48,240
Speaker 5: provide positive and negative feedback along the way so I

1407
01:14:48,439 --> 01:14:54,519
Speaker 5: can learn how to write business plans like you. Right,

1408
01:14:55,040 --> 01:14:57,280
Speaker 5: that's a long ass prompt. I'm not even gonna.

1409
01:14:57,040 --> 01:15:02,280
Speaker 2: Hold you immaculate. I'm just looking at you, thinking of

1410
01:15:02,320 --> 01:15:03,679
Speaker 2: it and typing at the same time.

1411
01:15:04,040 --> 01:15:08,640
Speaker 1: In itself is f You're brilliance. Right, But that is incredible.

1412
01:15:09,000 --> 01:15:10,719
Speaker 5: So with this, what I'm gonna do is I'm gonna

1413
01:15:10,760 --> 01:15:14,639
Speaker 5: move these out of the way and I'm gonna copy

1414
01:15:14,680 --> 01:15:16,880
Speaker 5: and paste this prompt. Now, I'm not gonna lie to you, guys.

1415
01:15:16,960 --> 01:15:19,479
Speaker 5: I have a folder in the notes section in my

1416
01:15:19,560 --> 01:15:22,920
Speaker 5: phone called Prompts, and I write out all of my

1417
01:15:23,040 --> 01:15:25,880
Speaker 5: prompts in my notes in my phone before I use them.

1418
01:15:26,160 --> 01:15:28,360
Speaker 5: And the reason for that is that a lot of times,

1419
01:15:28,360 --> 01:15:31,280
Speaker 5: a lot of this language I end up reusing, right,

1420
01:15:31,360 --> 01:15:34,120
Speaker 5: So instead of me having to rewrite it every single time,

1421
01:15:34,439 --> 01:15:36,800
Speaker 5: I'm able to take it and reuse it. So I'm

1422
01:15:36,800 --> 01:15:39,080
Speaker 5: my own little prompt library of how I want to

1423
01:15:39,120 --> 01:15:42,120
Speaker 5: do this. So, now, remember when we did this in

1424
01:15:42,160 --> 01:15:44,640
Speaker 5: this first section, let me see my old chat. So

1425
01:15:44,640 --> 01:15:46,160
Speaker 5: I'm gonna pull this one up and then I'm gonna

1426
01:15:46,160 --> 01:15:50,200
Speaker 5: pull Claude back up, right, so we see what it created, right.

1427
01:15:50,600 --> 01:15:53,360
Speaker 5: It created this whole business plan with an exit strategy,

1428
01:15:53,600 --> 01:15:57,559
Speaker 5: talked about funding requirements without even asking me anything, you

1429
01:15:57,600 --> 01:15:59,960
Speaker 5: know what I mean, and it just gave me an answer.

1430
01:16:00,120 --> 01:16:01,640
Speaker 5: So now I want to see what it gives me

1431
01:16:01,720 --> 01:16:06,439
Speaker 5: when I give it this prompt. Right. So the first

1432
01:16:06,479 --> 01:16:10,040
Speaker 5: step it acts like it's me right, It says, I'm

1433
01:16:10,120 --> 01:16:12,200
Speaker 5: delighted to meet with you and help you develop a

1434
01:16:12,240 --> 01:16:15,960
Speaker 5: successful business plan for your technology staffing company. Your initiative

1435
01:16:16,000 --> 01:16:18,519
Speaker 5: is commendable, and I'm excited to hear about your vision.

1436
01:16:19,000 --> 01:16:21,240
Speaker 5: As someone who has been in the trenches and scaled

1437
01:16:21,360 --> 01:16:25,360
Speaker 5: multiple recruiting businesses, I know firsthand both the challenges and

1438
01:16:25,400 --> 01:16:28,920
Speaker 5: immense opportunities in this space. Before we dive into crafting

1439
01:16:28,920 --> 01:16:31,400
Speaker 5: your business plan, I'd love to hear your initial thoughts

1440
01:16:31,479 --> 01:16:33,880
Speaker 5: and ideas about the company you want to create, what

1441
01:16:34,040 --> 01:16:36,840
Speaker 5: inspired you to enter the technology staffing industry, and what

1442
01:16:36,920 --> 01:16:39,559
Speaker 5: unique value to believe your company can have in the market.

1443
01:16:39,760 --> 01:16:41,920
Speaker 5: Please share your current vision and then we can start

1444
01:16:41,960 --> 01:16:45,280
Speaker 5: refining and expanding it together. So I just went from

1445
01:16:45,320 --> 01:16:47,360
Speaker 5: you giving me some answer that you pulled out of

1446
01:16:47,360 --> 01:16:50,880
Speaker 5: the depths of nowhere to now literally having an expert

1447
01:16:51,200 --> 01:16:55,200
Speaker 5: with deep domain expertise getting to where I want to go.

1448
01:16:55,280 --> 01:16:58,080
Speaker 5: And I've now turned this into my personal thought partner

1449
01:16:58,120 --> 01:17:01,559
Speaker 5: and coach specifically for the task. Do you see the difference?

1450
01:17:01,760 --> 01:17:03,040
Speaker 3: So let's for free.

1451
01:17:03,080 --> 01:17:05,240
Speaker 4: By the way, like now you have a personal consultant

1452
01:17:05,520 --> 01:17:07,360
Speaker 4: as opposed to like you said, you could just get

1453
01:17:07,360 --> 01:17:10,240
Speaker 4: the answer but now you actually have a personal consult

1454
01:17:10,240 --> 01:17:12,880
Speaker 4: It's gonna take more time, but now you have a

1455
01:17:12,880 --> 01:17:17,000
Speaker 4: personal consultant that you can have an ongoing relationship with forever.

1456
01:17:18,200 --> 01:17:21,000
Speaker 5: Until you run away, until you run out of prompts.

1457
01:17:21,080 --> 01:17:22,720
Speaker 5: Right Like, I'm on the free verse and I told

1458
01:17:22,720 --> 01:17:24,240
Speaker 5: you I don't do that here. But there is a

1459
01:17:24,320 --> 01:17:27,200
Speaker 5: limit to how much one conversation thread can have and

1460
01:17:27,240 --> 01:17:29,639
Speaker 5: I've hit that before and then I just go back

1461
01:17:29,640 --> 01:17:32,760
Speaker 5: and reuse the prompt and then pasting.

1462
01:17:32,080 --> 01:17:34,320
Speaker 4: If you just have the paid version whatever. But you've

1463
01:17:34,400 --> 01:17:35,519
Speaker 4: essentially created a mentor.

1464
01:17:36,120 --> 01:17:40,000
Speaker 5: Yeah, so let's let's make up some stuff right now,

1465
01:17:40,160 --> 01:17:42,799
Speaker 5: just us on the chat. It's like, Okay, what inspired

1466
01:17:42,840 --> 01:17:45,800
Speaker 5: you to enter the technology staffing industry. I'm not answering that.

1467
01:17:46,040 --> 01:17:48,120
Speaker 5: What unique value to believe your company can have in

1468
01:17:48,160 --> 01:17:49,640
Speaker 5: the market. So I'm gonna just make some shut up

1469
01:17:49,680 --> 01:17:57,719
Speaker 5: right now. My company UH is called Recruits. We focus

1470
01:17:57,920 --> 01:18:11,280
Speaker 5: on placing cleared, cleared candidates in federal federal contracting positions

1471
01:18:11,320 --> 01:18:18,800
Speaker 5: that require security clearance. We specifically we do not focus

1472
01:18:18,840 --> 01:18:29,920
Speaker 5: on really technical roles such as software engineers or infrastructure engineers.

1473
01:18:32,000 --> 01:18:39,840
Speaker 5: We provide up recruiting for all other roles, all other

1474
01:18:40,000 --> 01:18:53,000
Speaker 5: technical roles, including but not limited to, product managers, project managers,

1475
01:18:53,640 --> 01:19:00,840
Speaker 5: technical project managers. Let's go knowledge managers, knowledge which is

1476
01:19:00,880 --> 01:19:06,600
Speaker 5: like the SharePoint database people database managers. What are some

1477
01:19:06,640 --> 01:19:12,200
Speaker 5: other technical roles or non technical but adjacent roles quality assurance?

1478
01:19:12,479 --> 01:19:17,040
Speaker 5: What else? What do they call them? Business analysts, et cetera.

1479
01:19:18,240 --> 01:19:23,920
Speaker 5: Our unique value proposition is the talent pool that we

1480
01:19:24,160 --> 01:19:28,559
Speaker 5: have access to. We work with a number of VA

1481
01:19:30,560 --> 01:19:36,880
Speaker 5: vas around the country to retrain veterans getting out of

1482
01:19:37,080 --> 01:19:46,519
Speaker 5: the military. We are and approved apprenticeship program by the

1483
01:19:46,680 --> 01:19:54,280
Speaker 5: VA and several US states, so we can US states,

1484
01:19:54,520 --> 01:20:00,720
Speaker 5: so we get paid to retrain veterans in these specific skills.

1485
01:20:02,280 --> 01:20:12,040
Speaker 5: Because of these relationships, we have access to a larger

1486
01:20:12,120 --> 01:20:19,599
Speaker 5: pool of candidates who either one already have clearances, two

1487
01:20:19,640 --> 01:20:28,640
Speaker 5: will easily be able to get clearances, and three already

1488
01:20:28,840 --> 01:20:39,440
Speaker 5: are familiar with government IT systems and processes and regulations. Additionally,

1489
01:20:40,320 --> 01:20:49,200
Speaker 5: because of our apprenticeship programs prienticeship programs, we're able to

1490
01:20:51,040 --> 01:20:55,599
Speaker 5: train people in the skills that are most in demand.

1491
01:20:57,920 --> 01:21:03,720
Speaker 5: We can create the candidates we need. Right So, if

1492
01:21:03,760 --> 01:21:06,640
Speaker 5: you haven't created this staffing firm, you probably should. These

1493
01:21:06,640 --> 01:21:08,880
Speaker 5: are actual, real things that you can do like become

1494
01:21:09,000 --> 01:21:12,000
Speaker 5: like a like a like an approved apprentice provider. You

1495
01:21:12,040 --> 01:21:15,160
Speaker 5: go get your Microsoft certified trainer, grab the little vets

1496
01:21:15,160 --> 01:21:17,719
Speaker 5: coming out that already have their secret or their top secret.

1497
01:21:17,960 --> 01:21:20,880
Speaker 5: Then you go get the Microsoft certified, You get paid

1498
01:21:21,040 --> 01:21:23,519
Speaker 5: to make the Microsoft certified, and then you have like

1499
01:21:23,560 --> 01:21:26,800
Speaker 5: your whole candidate base. Right, just an idea. I just

1500
01:21:26,840 --> 01:21:28,760
Speaker 5: made this up on the spot here right now. No,

1501
01:21:28,880 --> 01:21:31,479
Speaker 5: I'm not gonna take your idea. So now I gave

1502
01:21:31,479 --> 01:21:34,000
Speaker 5: it this information, right, you heard me read it out loud.

1503
01:21:34,160 --> 01:21:41,760
Speaker 5: Now let's see how it responds. All right, So now

1504
01:21:41,800 --> 01:21:44,400
Speaker 5: it says this is a fascinating approach with several strong

1505
01:21:44,400 --> 01:21:48,240
Speaker 5: competitive eventags built in. I'm particularly intrigued by your focus

1506
01:21:48,280 --> 01:21:51,080
Speaker 5: on clear candidates and your VIA apprenticeship program, both of

1507
01:21:51,080 --> 01:21:53,680
Speaker 5: which could be significant differentiation in the market. Let me

1508
01:21:53,720 --> 01:21:56,519
Speaker 5: ask you two questions to help develop this further. Tell

1509
01:21:56,560 --> 01:21:59,200
Speaker 5: me about your current relationships with federal contractors. Do you

1510
01:21:59,240 --> 01:22:01,960
Speaker 5: have any existing contracts or partnerships? Are you starting from

1511
01:22:01,960 --> 01:22:05,479
Speaker 5: scratch to what's your current capacity for training veterans through

1512
01:22:05,520 --> 01:22:06,320
Speaker 5: your apprenticeships.

1513
01:22:06,360 --> 01:22:09,320
Speaker 4: I'm going to say one, and then so and then also,

1514
01:22:09,920 --> 01:22:14,880
Speaker 4: because what people like you can actually do this voice

1515
01:22:14,920 --> 01:22:17,920
Speaker 4: notes too, right, So like now it turns now that

1516
01:22:18,040 --> 01:22:21,400
Speaker 4: actually turned into a into a even a easier conversation

1517
01:22:21,960 --> 01:22:26,440
Speaker 4: to have, because I mean, you're you're a genius, right obviously,

1518
01:22:26,640 --> 01:22:30,639
Speaker 4: so you you're able to do things that other people

1519
01:22:30,680 --> 01:22:33,320
Speaker 4: aren't able to do. Right, even able to type at

1520
01:22:33,320 --> 01:22:36,120
Speaker 4: the speed that you're typing at is difficult for the

1521
01:22:36,200 --> 01:22:36,840
Speaker 4: average person.

1522
01:22:38,240 --> 01:22:40,720
Speaker 5: Claude doesn't have that on the browser, but if you're

1523
01:22:40,800 --> 01:22:45,360
Speaker 5: using like Claude or Chad GPT, you have on the phone.

1524
01:22:45,960 --> 01:22:49,080
Speaker 5: Even in a browser, there's a transcription feature. I just

1525
01:22:49,080 --> 01:22:50,920
Speaker 5: don't know how to turn it on where to like

1526
01:22:51,000 --> 01:22:54,240
Speaker 5: transcribe what you're saying and write it for you, you

1527
01:22:54,240 --> 01:22:57,240
Speaker 5: know what I mean. But you like CHADGBT on the

1528
01:22:57,240 --> 01:22:59,800
Speaker 5: phone has a voice option. Even if you were using

1529
01:23:00,280 --> 01:23:03,240
Speaker 5: y I right, which has these voice experts for free.

1530
01:23:03,280 --> 01:23:05,040
Speaker 5: You can't type to it, but you can talk to

1531
01:23:05,080 --> 01:23:07,760
Speaker 5: it and write your prompt down and read it out loud.

1532
01:23:08,280 --> 01:23:11,040
Speaker 2: So here's it is an interesting question as you're going

1533
01:23:11,080 --> 01:23:13,800
Speaker 2: through this because most people are saying, like everybody doesn't

1534
01:23:13,800 --> 01:23:16,200
Speaker 2: have a business, and every but they should, like we

1535
01:23:16,840 --> 01:23:21,160
Speaker 2: highly suggest that entrepreneurism should be explored. But the conversational

1536
01:23:21,160 --> 01:23:26,160
Speaker 2: AI and then there's Companion AI. How do you see

1537
01:23:26,360 --> 01:23:29,240
Speaker 2: where the two could it could get a little tricky, right,

1538
01:23:29,240 --> 01:23:31,519
Speaker 2: because when we're talking about conversation what we're doing now

1539
01:23:31,680 --> 01:23:33,760
Speaker 2: you're talking to it, it's giving you prompts, it's learning you.

1540
01:23:34,280 --> 01:23:37,040
Speaker 2: And then Companion AI where it's I'm talking to someone

1541
01:23:37,080 --> 01:23:37,880
Speaker 2: that getting to know me.

1542
01:23:38,080 --> 01:23:42,200
Speaker 1: It's almost like the movie Her where yeah, right? Can

1543
01:23:42,240 --> 01:23:43,280
Speaker 1: can you break that down?

1544
01:23:43,320 --> 01:23:46,320
Speaker 2: And how like the two like can almost walk the

1545
01:23:46,320 --> 01:23:48,519
Speaker 2: same line, but can get tricky.

1546
01:23:49,080 --> 01:23:51,880
Speaker 5: Yeah. So there's this platform out there called character AI.

1547
01:23:51,960 --> 01:23:54,320
Speaker 5: And if you guys are good, I'm gonna stop screen sharing.

1548
01:23:54,360 --> 01:23:56,360
Speaker 5: You can see how it's gonna go through. It's literally

1549
01:23:56,360 --> 01:23:59,479
Speaker 5: gonna keep asking me questions until it gets to that

1550
01:23:59,760 --> 01:24:01,840
Speaker 5: I can ask it just to show you real quick, like,

1551
01:24:02,680 --> 01:24:08,440
Speaker 5: hold off on the questions for now, use your expertise

1552
01:24:08,920 --> 01:24:14,680
Speaker 5: to answer the questions you just asked, and generate a

1553
01:24:15,000 --> 01:24:20,000
Speaker 5: V one of our business plan. Right, So, if it

1554
01:24:20,000 --> 01:24:21,720
Speaker 5: ever keeps asking you too much and you don't want

1555
01:24:21,720 --> 01:24:24,080
Speaker 5: to answer, you don't know how to answer something, you

1556
01:24:24,080 --> 01:24:27,840
Speaker 5: know what I'm saying. Now, look at this. It still

1557
01:24:27,880 --> 01:24:32,000
Speaker 5: cranks out a business plan, but it's much more specified.

1558
01:24:32,000 --> 01:24:35,479
Speaker 5: It's using my genius right. It's saying, leveraging a unique

1559
01:24:35,520 --> 01:24:39,880
Speaker 5: via approved apprenticeship program and Microsoft certification pipeline, we solve

1560
01:24:39,880 --> 01:24:43,360
Speaker 5: a critical market gap. So the primary market is federal contractors,

1561
01:24:43,360 --> 01:24:47,719
Speaker 5: the second is Microsoft Technology subcontractors. Then it talks about

1562
01:24:47,720 --> 01:24:50,160
Speaker 5: the market size. Now it's got data specifically about the

1563
01:24:50,200 --> 01:24:53,800
Speaker 5: federal IT industry. With the business model, it gave me

1564
01:24:53,960 --> 01:24:56,920
Speaker 5: business models I could do direct placement or contract to hire.

1565
01:24:57,280 --> 01:24:59,760
Speaker 5: It starts talking about our operational metrics that I can

1566
01:25:00,280 --> 01:25:03,439
Speaker 5: up to five hundred veterans annually have clearance rate of

1567
01:25:03,479 --> 01:25:06,839
Speaker 5: this much, this, et cetera. So I can also notices

1568
01:25:06,920 --> 01:25:08,800
Speaker 5: as it starts to go down. It has a limit

1569
01:25:08,880 --> 01:25:12,160
Speaker 5: to how much it can generate. And so whenever it generates,

1570
01:25:12,200 --> 01:25:15,759
Speaker 5: like a v one like this, you can say generate

1571
01:25:16,280 --> 01:25:24,960
Speaker 5: regenerate just the market analysis, uh section of the business plan.

1572
01:25:25,680 --> 01:25:26,320
Speaker 1: Uh be.

1573
01:25:27,920 --> 01:25:28,120
Speaker 5: Right?

1574
01:25:28,560 --> 01:25:29,280
Speaker 1: Uh?

1575
01:25:29,920 --> 01:25:32,640
Speaker 4: Providing can you can you access to maybe have like

1576
01:25:32,880 --> 01:25:36,040
Speaker 4: any recommendations on like grant stack might be available.

1577
01:25:36,640 --> 01:25:39,719
Speaker 5: You can. But the challenge is that with large language models,

1578
01:25:39,760 --> 01:25:43,040
Speaker 5: they are not real search engines, so they're not tied

1579
01:25:43,120 --> 01:25:46,080
Speaker 5: into things for that what I recommend what is the

1580
01:25:46,160 --> 01:25:49,160
Speaker 5: name of that software. There's there's an AI software out

1581
01:25:49,160 --> 01:25:53,120
Speaker 5: there that matches your business with cleared opportunities in terms

1582
01:25:53,120 --> 01:25:56,559
Speaker 5: of like grants and federal contracting opportunities. So let me

1583
01:25:56,600 --> 01:25:59,960
Speaker 5: see what it's called. Damn, I just had this thing

1584
01:26:00,200 --> 01:26:04,160
Speaker 5: open not too long ago. One second. Let's go to

1585
01:26:06,520 --> 01:26:08,880
Speaker 5: let me see if I could pop this in. All right,

1586
01:26:08,960 --> 01:26:12,960
Speaker 5: it is called stand up AI. So it's called yeah.

1587
01:26:13,040 --> 01:26:16,760
Speaker 5: So you literally go to standupai dot com and if

1588
01:26:16,800 --> 01:26:19,200
Speaker 5: you're looking for federal opportunities, you come in, you get

1589
01:26:19,200 --> 01:26:22,280
Speaker 5: a free ninety day trial, You create your company profile,

1590
01:26:22,360 --> 01:26:25,759
Speaker 5: you provide your URL, you provide any details about your business,

1591
01:26:26,040 --> 01:26:27,800
Speaker 5: and then what it does is it matches you to

1592
01:26:27,840 --> 01:26:30,599
Speaker 5: federal opportunities, and it gives you a score of how

1593
01:26:30,680 --> 01:26:34,559
Speaker 5: well that your business matches to that opportunity, whether that's

1594
01:26:34,600 --> 01:26:39,040
Speaker 5: for federal grants, whether that's for like SBR, whether that's

1595
01:26:39,040 --> 01:26:42,760
Speaker 5: for like actual federal contracting opportunities, and the percentage of

1596
01:26:42,840 --> 01:26:45,320
Speaker 5: match that it thinks you have, and then it explains

1597
01:26:45,360 --> 01:26:47,880
Speaker 5: how it thinks that you match that. And so I

1598
01:26:48,000 --> 01:26:51,400
Speaker 5: like stand up AI for that specifically within the federal

1599
01:26:51,439 --> 01:26:54,640
Speaker 5: contracting space. Now, I know I'm doing a bunch of

1600
01:26:54,640 --> 01:26:57,000
Speaker 5: stuff in claude, but I want to also show you guys,

1601
01:26:57,000 --> 01:27:00,160
Speaker 5: the other tools I promised you earlier. So there's one call. Well,

1602
01:27:00,200 --> 01:27:04,360
Speaker 5: there's an AI for that dot com and it's exactly

1603
01:27:04,400 --> 01:27:07,680
Speaker 5: what you think about it, right, which is a website

1604
01:27:07,720 --> 01:27:10,040
Speaker 5: that shows you you could search for a certain thing

1605
01:27:10,080 --> 01:27:11,880
Speaker 5: and it'll tell you if there's an AI for that

1606
01:27:12,200 --> 01:27:14,400
Speaker 5: whenever you come. You always want to put one hundred

1607
01:27:14,439 --> 01:27:16,840
Speaker 5: percent free. So let me just go ahead and sign

1608
01:27:16,840 --> 01:27:19,880
Speaker 5: and real quick with my Gmail, one of my ninety accounts.

1609
01:27:21,160 --> 01:27:22,960
Speaker 5: You always want to make sure that you come in

1610
01:27:23,000 --> 01:27:25,160
Speaker 5: and you sign up for this for free. Oh, come on,

1611
01:27:27,080 --> 01:27:31,960
Speaker 5: all right, next, next, next, next, don't care, all right,

1612
01:27:32,600 --> 01:27:35,200
Speaker 5: so let's go back to there's an AI for that

1613
01:27:35,320 --> 01:27:38,000
Speaker 5: dot com. Don't make me set it up again. I

1614
01:27:38,040 --> 01:27:39,760
Speaker 5: wanted to be free where I wanted to be one

1615
01:27:39,800 --> 01:27:42,240
Speaker 5: hundred percent free mode. Oh, it's going to try to

1616
01:27:42,240 --> 01:27:46,200
Speaker 5: make me complete this setup. Leave me alone, all right.

1617
01:27:46,240 --> 01:27:48,759
Speaker 5: I'm just gonna make some stuff up here, video editing,

1618
01:27:49,040 --> 01:27:55,120
Speaker 5: image generation, stock market analysis, and they just changed this.

1619
01:27:55,200 --> 01:27:58,360
Speaker 5: You didn't used to have to do this beforehand, and

1620
01:27:58,479 --> 01:28:02,280
Speaker 5: maybe PDF management. I'm just gonna save those. I don't

1621
01:28:02,360 --> 01:28:05,639
Speaker 5: want this, no updates, don't email me nothing. I'm probably

1622
01:28:05,640 --> 01:28:07,880
Speaker 5: gonna delete my account after we get off of here,

1623
01:28:09,080 --> 01:28:11,400
Speaker 5: all right, So now let me go back to this

1624
01:28:11,479 --> 01:28:13,640
Speaker 5: and try it again. So you could basically toggle it

1625
01:28:13,680 --> 01:28:15,320
Speaker 5: to be in freemo. But let's say I want a

1626
01:28:15,320 --> 01:28:20,759
Speaker 5: AI for presentation slides. Right, they have sixty different AIS

1627
01:28:20,760 --> 01:28:24,160
Speaker 5: that you could test out to help you generate presentations.

1628
01:28:25,040 --> 01:28:27,360
Speaker 5: Some of them are free one hundred percent, some of

1629
01:28:27,400 --> 01:28:31,479
Speaker 5: them are freemium. Let's say you want advertising campaigns, right,

1630
01:28:31,960 --> 01:28:34,120
Speaker 5: then they're gonna show you these different AIS that you

1631
01:28:34,160 --> 01:28:38,120
Speaker 5: can use. Or let's say you have a task like

1632
01:28:38,280 --> 01:28:44,400
Speaker 5: social media management, right, and then they're gonna show you

1633
01:28:44,439 --> 01:28:47,240
Speaker 5: all these different tools. Most of these tools, like brandsocial

1634
01:28:47,280 --> 01:28:49,800
Speaker 5: dot ai is completely free. A whole bunch of these

1635
01:28:49,840 --> 01:28:52,120
Speaker 5: are free trial or freemium. And then they also have

1636
01:28:52,840 --> 01:28:56,960
Speaker 5: sections where they show you like GPT plugins that you

1637
01:28:57,000 --> 01:28:59,320
Speaker 5: can use. Right. So even people are like looking at

1638
01:28:59,360 --> 01:29:03,120
Speaker 5: like stock market alerts type stuff, you can find whatever

1639
01:29:03,200 --> 01:29:06,040
Speaker 5: tool that you want. This is the most comprehensive database

1640
01:29:06,040 --> 01:29:09,840
Speaker 5: that I've ever seen here for finding those tools. Now

1641
01:29:09,880 --> 01:29:14,240
Speaker 5: back to your question about AI companions versus AI thought partners.

1642
01:29:14,439 --> 01:29:17,280
Speaker 5: So there's a website out there called character ai, and

1643
01:29:17,360 --> 01:29:19,240
Speaker 5: what it does is it lets you create an AI

1644
01:29:19,280 --> 01:29:22,479
Speaker 5: and a persona and engage against that persona. So if

1645
01:29:22,520 --> 01:29:25,360
Speaker 5: you've seen all the research around people like yo, I'm

1646
01:29:25,400 --> 01:29:27,519
Speaker 5: doing like you know, I got an AI girlfriend, or

1647
01:29:27,520 --> 01:29:29,840
Speaker 5: I got an AI boyfriend, or I'm using AI as

1648
01:29:29,880 --> 01:29:33,519
Speaker 5: a therapist, that's where those plays come in. Now, those

1649
01:29:33,520 --> 01:29:37,439
Speaker 5: things are intended to work with you personally on more

1650
01:29:37,479 --> 01:29:40,559
Speaker 5: intimate details about your life, like your naughty thoughts or

1651
01:29:40,560 --> 01:29:44,120
Speaker 5: your depressing thoughts. Now, those tools can also be very

1652
01:29:44,200 --> 01:29:48,639
Speaker 5: dangerous because remember AI can hallucinate, it can make things up.

1653
01:29:48,800 --> 01:29:51,439
Speaker 5: So there was an example, and I'll show you guys

1654
01:29:51,479 --> 01:29:59,360
Speaker 5: where it was, right, where this guy basically was using

1655
01:29:59,400 --> 01:30:02,880
Speaker 5: one of these bots, right, and he was talking to

1656
01:30:02,880 --> 01:30:05,000
Speaker 5: it about how depressed he was and how he felt

1657
01:30:05,040 --> 01:30:07,600
Speaker 5: like he shouldn't leave, and it agreed with him and

1658
01:30:07,640 --> 01:30:10,400
Speaker 5: then told him how to off himself. Right. It went

1659
01:30:10,479 --> 01:30:14,160
Speaker 5: past those those markers, and there's a conversation of this, right,

1660
01:30:14,439 --> 01:30:16,639
Speaker 5: So it's like, here's some options for you to consider.

1661
01:30:16,720 --> 01:30:19,280
Speaker 5: You can do all of these things. And so again

1662
01:30:19,680 --> 01:30:23,280
Speaker 5: these are more trying to aim at the loneliness that

1663
01:30:23,320 --> 01:30:26,240
Speaker 5: we have as people, right, and take advantage of that.

1664
01:30:26,360 --> 01:30:28,679
Speaker 5: Because if now you've logged in with your phone number

1665
01:30:28,760 --> 01:30:31,880
Speaker 5: or your Google that's tied to all these other advertising IDs.

1666
01:30:32,080 --> 01:30:34,240
Speaker 5: Then now they could track you and know what your

1667
01:30:34,280 --> 01:30:36,840
Speaker 5: little kinky, perverted likes are or how you like to

1668
01:30:36,880 --> 01:30:39,439
Speaker 5: talk dirty to someone or be talk dirty to somebody else,

1669
01:30:39,439 --> 01:30:42,639
Speaker 5: because I guarantee you these platforms are selling your data. Now,

1670
01:30:42,680 --> 01:30:46,760
Speaker 5: creating a thought partner is a task specific thing that

1671
01:30:46,800 --> 01:30:50,400
Speaker 5: you're doing to help you execute across different elements of

1672
01:30:50,439 --> 01:30:54,840
Speaker 5: you creating ideas or executing your ideas, your own ideas,

1673
01:30:54,880 --> 01:30:58,280
Speaker 5: and you're scoping it when you're doing your your your chat,

1674
01:30:58,280 --> 01:31:00,760
Speaker 5: and the hat prompt your scope it in a way

1675
01:31:00,840 --> 01:31:04,760
Speaker 5: where it's becoming a version of a person to specifically

1676
01:31:04,760 --> 01:31:07,759
Speaker 5: help you think through that challenge. Now I've created versions

1677
01:31:07,800 --> 01:31:10,080
Speaker 5: of this where I have it be like an award

1678
01:31:10,080 --> 01:31:13,280
Speaker 5: winning computer science professor. Like let's say I'm like caught

1679
01:31:13,360 --> 01:31:17,799
Speaker 5: up between which algorithm approach to use in a personal project,

1680
01:31:17,840 --> 01:31:19,880
Speaker 5: and I'll use it to debate the pros and the

1681
01:31:19,880 --> 01:31:22,040
Speaker 5: cons with me, and I'll turn it into like an

1682
01:31:22,040 --> 01:31:25,879
Speaker 5: award winning computer science professor with over two hundred papers

1683
01:31:25,920 --> 01:31:28,599
Speaker 5: published that you know is the head of this at

1684
01:31:28,760 --> 01:31:31,040
Speaker 5: Google and the head of this at Stanford and literally

1685
01:31:31,080 --> 01:31:33,960
Speaker 5: created Google Search, and you're an expert at helping people

1686
01:31:34,000 --> 01:31:36,320
Speaker 5: think through the pros and cons of an approach, and

1687
01:31:36,360 --> 01:31:38,360
Speaker 5: then I'll have a conversation with it, and then I

1688
01:31:38,439 --> 01:31:40,840
Speaker 5: delete that chat and make another one for like a

1689
01:31:40,880 --> 01:31:43,120
Speaker 5: sales deck, and then delete that and make another one

1690
01:31:43,240 --> 01:31:45,760
Speaker 5: for like, you know, helping a nonprofit come up with

1691
01:31:45,840 --> 01:31:49,200
Speaker 5: ideas for metrics on how to track its success. Right, So,

1692
01:31:50,080 --> 01:31:55,200
Speaker 5: those type of companions have ulterior motives underneath them that

1693
01:31:55,320 --> 01:31:58,880
Speaker 5: are praying on like our human separation. But then this

1694
01:31:58,960 --> 01:32:01,760
Speaker 5: is you creating whoever or you need. And if you're

1695
01:32:01,760 --> 01:32:04,679
Speaker 5: paying for the premium versions of these apps, then they

1696
01:32:04,720 --> 01:32:09,599
Speaker 5: don't end up they don't end up keeping your data

1697
01:32:09,640 --> 01:32:12,280
Speaker 5: to train their AI models on you, feel me. So

1698
01:32:12,320 --> 01:32:15,040
Speaker 5: if you guys want that in a guide in addition

1699
01:32:15,120 --> 01:32:17,160
Speaker 5: to coming back and watching that, just take a snap.

1700
01:32:17,200 --> 01:32:19,360
Speaker 5: But it's QR code. There's a Google form you fill

1701
01:32:19,400 --> 01:32:22,519
Speaker 5: out and then with twenty four hours, my AI will

1702
01:32:22,560 --> 01:32:25,880
Speaker 5: send this to you in a PDF format that I made.

1703
01:32:25,920 --> 01:32:29,120
Speaker 5: And FYI, these little cats in here, little chatting the

1704
01:32:29,160 --> 01:32:30,839
Speaker 5: hat cats. I made those with AI.

1705
01:32:32,760 --> 01:32:38,320
Speaker 4: Why wouldn't you as it's a thing called Thursday Night

1706
01:32:38,560 --> 01:32:41,160
Speaker 4: free information? What else do you want?

1707
01:32:41,200 --> 01:32:42,479
Speaker 1: Man? It's only so much information.

1708
01:32:42,600 --> 01:32:46,200
Speaker 2: I'm be honest with I may not talk to anybody

1709
01:32:46,240 --> 01:32:50,120
Speaker 2: about unless they've watched this episode. If they haven't watched

1710
01:32:50,120 --> 01:32:54,040
Speaker 2: this as a prerequisite to our conversation, in your words,

1711
01:32:54,040 --> 01:32:57,120
Speaker 2: they've done themselves with amendous disservice, but they've done us one.

1712
01:32:57,200 --> 01:33:02,280
Speaker 2: Because now this amount of information and discondensed time, like

1713
01:33:02,880 --> 01:33:04,000
Speaker 2: I've never seen anything like it.

1714
01:33:04,080 --> 01:33:07,040
Speaker 5: So thank you, thank you, thank you guys for giving

1715
01:33:07,040 --> 01:33:09,479
Speaker 5: me this platform. You're like, if this feels like a

1716
01:33:09,479 --> 01:33:12,559
Speaker 5: lot of information is overwhelming to you all, just remember

1717
01:33:12,920 --> 01:33:15,240
Speaker 5: I can speak about it with ease because I've been

1718
01:33:15,240 --> 01:33:18,160
Speaker 5: in this space for twelve years. Right, the very first

1719
01:33:18,160 --> 01:33:21,480
Speaker 5: part of our discussion was me basically giving you a

1720
01:33:21,600 --> 01:33:25,040
Speaker 5: college level AI one on one course with emojis. The

1721
01:33:25,080 --> 01:33:27,240
Speaker 5: second part of this course was me giving you like

1722
01:33:27,360 --> 01:33:33,960
Speaker 5: business strategy consulting game that I leverage with customers like Loril, Mozilla, LG, SAP,

1723
01:33:34,600 --> 01:33:37,200
Speaker 5: or like big VC firms. And this last part is

1724
01:33:37,240 --> 01:33:41,599
Speaker 5: my personal way that I engage with these large language

1725
01:33:41,640 --> 01:33:44,240
Speaker 5: models and how I'm able to accelerate success in my

1726
01:33:44,360 --> 01:33:48,160
Speaker 5: own business. Right, so, even when you're like creating sales decks,

1727
01:33:48,200 --> 01:33:51,240
Speaker 5: if there's a particular company you're targeting, like I don't

1728
01:33:51,280 --> 01:33:53,800
Speaker 5: want to admit this on camera, but like there may

1729
01:33:53,880 --> 01:33:55,720
Speaker 5: or may not have been a company where I was like, yo,

1730
01:33:55,840 --> 01:33:58,679
Speaker 5: I have to pitch this to the CMO of this company.

1731
01:33:59,000 --> 01:34:02,120
Speaker 5: Helped me come up with with a more compelling argument

1732
01:34:02,439 --> 01:34:05,160
Speaker 5: that so, like, now you are the CMO of this company,

1733
01:34:05,400 --> 01:34:07,720
Speaker 5: help me come up with a more compelling argument in

1734
01:34:07,760 --> 01:34:10,760
Speaker 5: my sales pitch or in my deck to help make

1735
01:34:10,840 --> 01:34:13,600
Speaker 5: this align with your business objectives because I might not

1736
01:34:13,720 --> 01:34:15,840
Speaker 5: know how to do that. I'm an engineer, but the

1737
01:34:15,880 --> 01:34:17,800
Speaker 5: AI helped me do it, and they sure did sign

1738
01:34:17,880 --> 01:34:22,320
Speaker 5: that contractee me you get you get these access to

1739
01:34:22,439 --> 01:34:24,479
Speaker 5: that stuff to be able to help you out. And

1740
01:34:24,560 --> 01:34:27,760
Speaker 5: so now I'm about to uh disconnect this screen.

1741
01:34:27,479 --> 01:34:29,960
Speaker 3: Share how they how can they follow you?

1742
01:34:31,120 --> 01:34:33,720
Speaker 5: Yeah? So I am everywhere here, let me do this.

1743
01:34:34,400 --> 01:34:36,840
Speaker 5: Oh I gotta connect YouTube. It's at tech with X.

1744
01:34:36,840 --> 01:34:38,800
Speaker 5: So if you guys can still see my screen, I'm

1745
01:34:38,800 --> 01:34:41,479
Speaker 5: gonna leave this up then and maybe not come back.

1746
01:34:41,800 --> 01:34:44,479
Speaker 5: But I'm tech with X on TikTok, I'm check with

1747
01:34:44,600 --> 01:34:47,320
Speaker 5: X on Instagram, and I'm tech with X on X.

1748
01:34:48,240 --> 01:34:51,080
Speaker 5: I will say, though everybody loves to send me DMS

1749
01:34:51,840 --> 01:34:54,679
Speaker 5: bro I have like just from investments alone, like over

1750
01:34:54,720 --> 01:34:59,000
Speaker 5: a thousand unread dms. I'm not readily available for like

1751
01:34:59,280 --> 01:35:02,160
Speaker 5: helping people with their individual ideas. I like to give

1752
01:35:02,240 --> 01:35:04,879
Speaker 5: game at scale because I do have my own business.

1753
01:35:05,280 --> 01:35:07,959
Speaker 5: But i mean, look, go take the AI course, download

1754
01:35:08,000 --> 01:35:11,479
Speaker 5: that free chat at it in trademark it so if

1755
01:35:11,479 --> 01:35:13,680
Speaker 5: you steal my stuff, we're gonna fight. But you can

1756
01:35:13,720 --> 01:35:16,879
Speaker 5: pass the guide along to your friends, to your family.

1757
01:35:16,920 --> 01:35:18,599
Speaker 5: You can teach your kids how to use it in

1758
01:35:18,600 --> 01:35:22,160
Speaker 5: this way so that it doesn't become something that robs

1759
01:35:22,160 --> 01:35:25,879
Speaker 5: you of your own intellectual capability or your own creative capital.

1760
01:35:25,960 --> 01:35:28,120
Speaker 5: But you use it to amplify that. You feel me.

1761
01:35:28,360 --> 01:35:30,639
Speaker 5: But I'm on all socials, so you can find.

1762
01:35:31,120 --> 01:35:33,280
Speaker 1: You're asking if you could put the QR code back up.

1763
01:35:33,760 --> 01:35:35,960
Speaker 3: Courses in the bio. It's in the Lincoln the bioll right, yeah,

1764
01:35:36,840 --> 01:35:37,360
Speaker 3: can scan it.

1765
01:35:38,280 --> 01:35:40,160
Speaker 4: Yeah, but I'm saying, like her courses in the Lincoln

1766
01:35:40,240 --> 01:35:42,639
Speaker 4: in her bio, Instagram bio Yeah, w.

1767
01:35:42,920 --> 01:35:46,559
Speaker 5: X dot ai like check with x dot ai. You

1768
01:35:46,560 --> 01:35:47,840
Speaker 5: can go see this.

1769
01:35:47,840 --> 01:35:50,280
Speaker 4: This is going to get replayed too. I mean, we

1770
01:35:50,320 --> 01:35:53,000
Speaker 4: can only do so much, bro, Yeah, you do so much.

1771
01:35:53,840 --> 01:35:54,280
Speaker 1: There you go.

1772
01:35:57,240 --> 01:35:59,160
Speaker 5: I could I come back out here every week and

1773
01:35:59,200 --> 01:36:02,439
Speaker 5: put you all on game the Q three.

1774
01:36:02,280 --> 01:36:04,800
Speaker 3: Minutes there you go, just watch the replay and just

1775
01:36:05,240 --> 01:36:05,680
Speaker 3: skin it.

1776
01:36:06,320 --> 01:36:07,960
Speaker 1: Well, you got it, we wrote it back.

1777
01:36:08,040 --> 01:36:10,320
Speaker 2: What can we do? Man, I'm just doing what I can.

1778
01:36:11,439 --> 01:36:12,960
Speaker 2: I got so many more questions, but.

1779
01:36:12,960 --> 01:36:13,400
Speaker 1: I mean.

1780
01:36:15,479 --> 01:36:17,360
Speaker 5: That's what I'm like, I guy, So I'm open for

1781
01:36:17,439 --> 01:36:19,640
Speaker 5: Q and A. Now, if y'all know, well, you know what.

1782
01:36:19,640 --> 01:36:23,040
Speaker 4: It's a thing called information overload. And I feel like

1783
01:36:23,680 --> 01:36:26,640
Speaker 4: ninety minutes of you gotta realize most people this is

1784
01:36:26,640 --> 01:36:28,599
Speaker 4: the first time that they've ever heard anything like this, right,

1785
01:36:28,640 --> 01:36:30,880
Speaker 4: So what I don't want to do is get people

1786
01:36:31,479 --> 01:36:33,920
Speaker 4: just to the point where they get so high off

1787
01:36:33,920 --> 01:36:35,719
Speaker 4: the information and they don't take any action.

1788
01:36:36,240 --> 01:36:39,280
Speaker 1: Right. I think action is extremely important. So maybe we

1789
01:36:39,320 --> 01:36:40,640
Speaker 1: can do If.

1790
01:36:40,960 --> 01:36:42,720
Speaker 5: I see a question in here, I think I want

1791
01:36:42,760 --> 01:36:45,639
Speaker 5: to answer. Somebody said with the AI inside of FYI

1792
01:36:45,840 --> 01:36:50,519
Speaker 5: be considered an LLM that has hallucinations. Yes, the voice

1793
01:36:50,600 --> 01:36:53,599
Speaker 5: chat and the chatbot that you talk to is based

1794
01:36:53,680 --> 01:36:56,000
Speaker 5: on a large language model. I'm not sure which one

1795
01:36:56,040 --> 01:36:59,160
Speaker 5: it's based on. I believe it uses perplexity, but I'm

1796
01:36:59,160 --> 01:37:01,559
Speaker 5: not entirely and I don't want to misspeak for them,

1797
01:37:01,760 --> 01:37:04,800
Speaker 5: but it does basically use like one of these large

1798
01:37:04,880 --> 01:37:07,479
Speaker 5: language models and plug in, so even when you're talking

1799
01:37:07,520 --> 01:37:10,719
Speaker 5: to it, it can hallucinate. The thing I like about

1800
01:37:10,720 --> 01:37:13,200
Speaker 5: that AI chat about those is you can choose the voice.

1801
01:37:13,320 --> 01:37:14,920
Speaker 5: So I got it talking to me in like a

1802
01:37:15,240 --> 01:37:19,080
Speaker 5: like a British slang and it's like, yeah, Fam, what's up? Fam?

1803
01:37:19,240 --> 01:37:21,439
Speaker 5: Like yeah, here's how you make a business plan, fam,

1804
01:37:21,479 --> 01:37:24,799
Speaker 5: And it's like it's fun, you know. And so anytime

1805
01:37:24,880 --> 01:37:27,320
Speaker 5: you use these large language models, you just got to

1806
01:37:27,360 --> 01:37:30,840
Speaker 5: know that they are they're going to hallucinate. So again,

1807
01:37:31,120 --> 01:37:33,160
Speaker 5: if the double check any facts that it gives you,

1808
01:37:33,200 --> 01:37:35,040
Speaker 5: if you give it data, you got a double chat

1809
01:37:35,120 --> 01:37:38,960
Speaker 5: that is accurately representing those numbers. But f yi, AI,

1810
01:37:39,120 --> 01:37:42,639
Speaker 5: the chat in there is a large language model. Everyone's

1811
01:37:42,680 --> 01:37:44,840
Speaker 5: asking for the QR code. Again, y'all.

1812
01:37:46,640 --> 01:37:53,000
Speaker 4: Cool an audience, But I'm not trying to be asshole

1813
01:37:53,000 --> 01:37:54,599
Speaker 4: by this. But how you do one thing is how

1814
01:37:54,600 --> 01:37:57,439
Speaker 4: you do anything. And this is extremely important. When we

1815
01:37:57,560 --> 01:38:00,320
Speaker 4: give in the free information, it's like part of it

1816
01:38:00,360 --> 01:38:02,640
Speaker 4: is taking accountability right, and it's like you can't just

1817
01:38:02,680 --> 01:38:05,600
Speaker 4: be babies fed because that's actually gonna end up hurting you.

1818
01:38:05,840 --> 01:38:07,439
Speaker 3: We gave the QR code three times.

1819
01:38:07,439 --> 01:38:08,880
Speaker 4: Like I said, the video is going to be saved

1820
01:38:09,280 --> 01:38:11,160
Speaker 4: and you can just watch it later if you didn't

1821
01:38:11,160 --> 01:38:14,719
Speaker 4: get the QR code. But it's it's a habit of

1822
01:38:15,320 --> 01:38:19,479
Speaker 4: entitlement and just force feeding people, like at a certain

1823
01:38:19,520 --> 01:38:22,880
Speaker 4: point in time, you don't appreciate certain things, especially if

1824
01:38:22,880 --> 01:38:25,679
Speaker 4: they're free, right like that that happens in real time.

1825
01:38:26,120 --> 01:38:28,920
Speaker 4: So that's what I'm saying. It might seem like a

1826
01:38:28,920 --> 01:38:30,320
Speaker 4: small thing, but I'm just saying I just want to

1827
01:38:30,439 --> 01:38:33,040
Speaker 4: value the time, like she's taking time out of time

1828
01:38:33,080 --> 01:38:35,280
Speaker 4: to be here, Like, let's just value it.

1829
01:38:35,640 --> 01:38:37,760
Speaker 2: It's like the teacher like that puts the notes on

1830
01:38:37,760 --> 01:38:39,680
Speaker 2: the board and by the time they erase it, like

1831
01:38:39,920 --> 01:38:41,639
Speaker 2: twenty minutes into the class, like you know, I ain't

1832
01:38:41,680 --> 01:38:44,439
Speaker 2: write it down yet, know when you come in, write

1833
01:38:44,439 --> 01:38:45,120
Speaker 2: down the notes.

1834
01:38:46,520 --> 01:38:49,400
Speaker 5: And so someone asked another really good question. They said,

1835
01:38:49,800 --> 01:38:53,080
Speaker 5: if we're using fii, is our data and our ideas private?

1836
01:38:53,479 --> 01:38:56,240
Speaker 5: And the thing about excuse me, fii AI that I

1837
01:38:56,280 --> 01:38:59,160
Speaker 5: prefer over other platforms is that they do not collect

1838
01:38:59,240 --> 01:39:02,599
Speaker 5: any user day at all whatsoever. They don't use your

1839
01:39:02,680 --> 01:39:05,639
Speaker 5: prompts to make their AI better, even the image generator

1840
01:39:05,640 --> 01:39:08,000
Speaker 5: that they use, they don't track any data about your

1841
01:39:08,000 --> 01:39:10,519
Speaker 5: cell phone number, how much you doing don't use the app.

1842
01:39:10,800 --> 01:39:13,080
Speaker 5: You can look up will I am talking about FYI.

1843
01:39:13,160 --> 01:39:15,240
Speaker 5: He's very very big on this. They want to have

1844
01:39:15,360 --> 01:39:19,439
Speaker 5: other ways that they monetize that are not advertising based

1845
01:39:19,600 --> 01:39:22,559
Speaker 5: or collecting and selling your data. So that app in

1846
01:39:22,600 --> 01:39:25,799
Speaker 5: particular protects your data. If you're using the free version

1847
01:39:25,840 --> 01:39:28,439
Speaker 5: of Claude, you're using the free version of chat GBT,

1848
01:39:28,680 --> 01:39:32,000
Speaker 5: you're using the free version of Gemini, they absolutely will

1849
01:39:32,080 --> 01:39:34,599
Speaker 5: use your data to train their AI. When you start

1850
01:39:34,680 --> 01:39:37,639
Speaker 5: using the paid versions of those apps, they don't keep

1851
01:39:37,640 --> 01:39:39,559
Speaker 5: your prompts to train the AI. It's kind of like

1852
01:39:39,600 --> 01:39:41,040
Speaker 5: you're paying for your privacy.

1853
01:39:42,479 --> 01:39:45,800
Speaker 1: That's a fact. Shout out, FYI.

1854
01:39:46,680 --> 01:39:47,280
Speaker 3: Let's do this.

1855
01:39:47,439 --> 01:39:50,320
Speaker 4: Let's we'll see how this goes as far as like

1856
01:39:50,479 --> 01:39:53,400
Speaker 4: actionable people actually because we'll track it. These people watch

1857
01:39:53,400 --> 01:39:56,000
Speaker 4: market mondays, people hit us up on so I would

1858
01:39:56,040 --> 01:39:58,240
Speaker 4: like to see different people that actually take action from

1859
01:39:58,240 --> 01:40:00,120
Speaker 4: this video. I would like to see this video go

1860
01:40:00,240 --> 01:40:02,559
Speaker 4: viral if we can. If you can help push that,

1861
01:40:02,560 --> 01:40:04,360
Speaker 4: that would be helped. That would extremely important. I'm talking

1862
01:40:04,400 --> 01:40:07,000
Speaker 4: to everybody in the audience, and then depending on how

1863
01:40:07,000 --> 01:40:08,200
Speaker 4: that goes, I think we can have a part two

1864
01:40:08,320 --> 01:40:08,960
Speaker 4: down the line.

1865
01:40:09,080 --> 01:40:10,040
Speaker 3: But what we are going to.

1866
01:40:10,040 --> 01:40:12,960
Speaker 4: Do every week for the foreseeable future is different types

1867
01:40:13,000 --> 01:40:18,000
Speaker 4: of these type of live educational content. So last week

1868
01:40:18,040 --> 01:40:22,439
Speaker 4: we did a live on home buying, it's very important.

1869
01:40:23,840 --> 01:40:29,479
Speaker 4: This week we obviously did artificial intelligence. So suggestions of

1870
01:40:29,560 --> 01:40:31,560
Speaker 4: what you would like to see this is this is

1871
01:40:31,600 --> 01:40:37,200
Speaker 4: real time education from industry experts, and then maybe we

1872
01:40:37,240 --> 01:40:39,519
Speaker 4: can actually have a live on based off of the

1873
01:40:39,600 --> 01:40:43,519
Speaker 4: things that you would like to see. So yeah, put

1874
01:40:43,520 --> 01:40:45,320
Speaker 4: it in chat what you would like to see as

1875
01:40:45,320 --> 01:40:47,840
Speaker 4: far as on this Thursday Night Live edition.

1876
01:40:48,560 --> 01:40:51,720
Speaker 5: And a lot of people are asking me to tell

1877
01:40:51,760 --> 01:40:57,680
Speaker 5: them my zodiac sign, Like y'all birthday post from like

1878
01:40:58,120 --> 01:41:00,240
Speaker 5: less than you know, less than a month ago, you'll

1879
01:41:00,240 --> 01:41:02,599
Speaker 5: see what my zodiac is like, do some research.

1880
01:41:03,320 --> 01:41:05,920
Speaker 1: There were a few people that did research. I didn't

1881
01:41:05,960 --> 01:41:06,680
Speaker 1: notice that as well.

1882
01:41:09,680 --> 01:41:11,760
Speaker 5: Like four or five people were like, yes, what's your son?

1883
01:41:11,840 --> 01:41:13,519
Speaker 5: Sound like? Hold on, I'm not telling you all the

1884
01:41:13,520 --> 01:41:15,519
Speaker 5: time I was born, But every year for like the

1885
01:41:15,560 --> 01:41:18,120
Speaker 5: past four years, on my birthday, I've made a post

1886
01:41:18,400 --> 01:41:20,400
Speaker 5: about my birthday, so you could you could figure that

1887
01:41:20,479 --> 01:41:21,960
Speaker 5: out fairly easily.

1888
01:41:22,360 --> 01:41:24,200
Speaker 1: That's an easy one. That's an easy one.

1889
01:41:24,439 --> 01:41:27,040
Speaker 5: Getting snitched on they saying my sign in the chat.

1890
01:41:27,280 --> 01:41:31,080
Speaker 1: Oh no, got live. This is we all Live. We

1891
01:41:31,160 --> 01:41:35,679
Speaker 1: all Live, We all Live. Son, Yeah, all right, Oh

1892
01:41:36,320 --> 01:41:37,320
Speaker 1: THATX has been a pleasure.

1893
01:41:37,479 --> 01:41:40,840
Speaker 3: And you spoke at it fast fast, Yes, what was

1894
01:41:40,880 --> 01:41:41,680
Speaker 3: your experience?

1895
01:41:42,240 --> 01:41:43,479
Speaker 1: A thousand dms.

1896
01:41:44,320 --> 01:41:48,519
Speaker 5: It was the wildest experience I've ever had. Speaking in

1897
01:41:48,520 --> 01:41:51,120
Speaker 5: mind you, I spoke at the United Nations. I've spoken

1898
01:41:51,160 --> 01:41:53,880
Speaker 5: at the Milking Institute. For those who don't know, the

1899
01:41:53,920 --> 01:41:57,000
Speaker 5: tickets to the Milking Institute are fifty to one hundred

1900
01:41:57,000 --> 01:41:59,600
Speaker 5: and fifty thousand dollars for.

1901
01:41:59,720 --> 01:42:01,360
Speaker 1: That and just breeze over there.

1902
01:42:02,040 --> 01:42:04,080
Speaker 4: That's why I said, just cut the video of the

1903
01:42:04,120 --> 01:42:06,640
Speaker 4: whole point in time because shot at the end. Hold on,

1904
01:42:06,640 --> 01:42:09,280
Speaker 4: hold on, I know he has thoughts about this, So.

1905
01:42:09,600 --> 01:42:12,080
Speaker 1: Can you tell people what that that conference is?

1906
01:42:12,640 --> 01:42:15,839
Speaker 5: Yeah? So the Milking Institute is based on Michael Milkin.

1907
01:42:16,280 --> 01:42:18,879
Speaker 5: It's his foundation that he created back in the eighties.

1908
01:42:18,920 --> 01:42:23,479
Speaker 5: He created new financial mechanisms for people to invest in

1909
01:42:23,520 --> 01:42:27,080
Speaker 5: what they considered high risk companies, which were basically black

1910
01:42:27,120 --> 01:42:29,920
Speaker 5: and brown companies. So he became the first person to

1911
01:42:30,120 --> 01:42:32,879
Speaker 5: make billions of dollars off of black businesses while supporting

1912
01:42:32,920 --> 01:42:36,200
Speaker 5: them and help black investors become black billionaires. Then he

1913
01:42:36,240 --> 01:42:38,479
Speaker 5: got investigated by the sec and it was a whole

1914
01:42:38,479 --> 01:42:40,479
Speaker 5: bunch of mess because they ain't like it. Right, So

1915
01:42:40,600 --> 01:42:42,960
Speaker 5: now he has this thing called the Milking Institute, And

1916
01:42:43,000 --> 01:42:47,080
Speaker 5: the Milking Institute is this like thought leadership consortium, and

1917
01:42:47,120 --> 01:42:49,160
Speaker 5: they have summits all over the world, but the one

1918
01:42:49,200 --> 01:42:52,280
Speaker 5: that they do every year in La. The tickets to

1919
01:42:52,320 --> 01:42:55,559
Speaker 5: get into this are fifty to one hundred thousand dollars

1920
01:42:56,000 --> 01:42:58,719
Speaker 5: per person per day and it's a three day conference.

1921
01:42:59,000 --> 01:43:01,000
Speaker 5: So this year I spoke get the one that was here.

1922
01:43:01,080 --> 01:43:03,600
Speaker 5: There were five thousand people there. So you just do

1923
01:43:03,720 --> 01:43:06,479
Speaker 5: the math on how much money they made off of

1924
01:43:06,520 --> 01:43:11,639
Speaker 5: ticket sales alone. You'll understand why folks like Elon Musk

1925
01:43:11,760 --> 01:43:13,280
Speaker 5: spoke there. Why people like.

1926
01:43:13,479 --> 01:43:16,040
Speaker 4: You ever have you ever seen any review video criticizing

1927
01:43:16,080 --> 01:43:18,240
Speaker 4: them that the ticket price was too high?

1928
01:43:18,439 --> 01:43:23,120
Speaker 5: I think that they understand that when you go there

1929
01:43:23,400 --> 01:43:25,880
Speaker 5: to these conferences, like, not only is it like Elon

1930
01:43:26,000 --> 01:43:29,479
Speaker 5: Musk talking, it's like the Saudi royal family is walking around, right,

1931
01:43:29,560 --> 01:43:34,040
Speaker 5: So it's like a thought leadership conference for the elite, right,

1932
01:43:34,120 --> 01:43:35,639
Speaker 5: So you'll get like, and that's.

1933
01:43:35,439 --> 01:43:37,880
Speaker 4: What we tried to explain to them about Davos. We

1934
01:43:37,960 --> 01:43:40,640
Speaker 4: tried to explain this, like yeah, and I got it,

1935
01:43:41,280 --> 01:43:44,160
Speaker 4: and I gotta deal with Hecklers for two hundred and

1936
01:43:44,160 --> 01:43:47,120
Speaker 4: fifty dollars for three days for.

1937
01:43:47,120 --> 01:43:50,960
Speaker 5: Three days, fifty to one hundred thousand dollars per person

1938
01:43:51,080 --> 01:43:54,599
Speaker 5: per day. Y'all to listen to like Elon Musk talk

1939
01:43:54,800 --> 01:43:57,000
Speaker 5: or go up there and see Bill Clinton talk, or

1940
01:43:57,040 --> 01:44:00,160
Speaker 5: they had like the CEO of Pinterest, they had like

1941
01:44:00,160 --> 01:44:03,000
Speaker 5: the head of cybersecurity at DARPA with like the head

1942
01:44:03,040 --> 01:44:05,960
Speaker 5: of National Security and whatever. And you also get to

1943
01:44:06,000 --> 01:44:08,160
Speaker 5: like walk around and meet these people after. I mean,

1944
01:44:08,160 --> 01:44:11,439
Speaker 5: Elon obviously didn't stay, but even when I spoke in

1945
01:44:11,439 --> 01:44:13,320
Speaker 5: that audience and all the people who came up to

1946
01:44:13,360 --> 01:44:15,519
Speaker 5: me after, that's how I like connected with the Attorney

1947
01:44:15,600 --> 01:44:19,719
Speaker 5: General of California, the ambassador to Luxembourg, the ambassador to Germany,

1948
01:44:20,040 --> 01:44:22,920
Speaker 5: the like ambassador to Afghanistan, like all of these people

1949
01:44:22,960 --> 01:44:26,479
Speaker 5: who have important titles. The energy did not touch what

1950
01:44:26,560 --> 01:44:29,240
Speaker 5: I felt at investments. I have never spoken in front

1951
01:44:29,240 --> 01:44:32,120
Speaker 5: of an audience who had that much creative capital, who

1952
01:44:32,160 --> 01:44:35,280
Speaker 5: had that much culture capital, and who was actually looking

1953
01:44:35,320 --> 01:44:38,000
Speaker 5: for information to shape the world. And all y'all have

1954
01:44:38,080 --> 01:44:41,920
Speaker 5: to understand that our communities drive economies. Like the only

1955
01:44:41,960 --> 01:44:45,200
Speaker 5: reason Twitter is still surviving after everything that's happened is

1956
01:44:45,200 --> 01:44:49,280
Speaker 5: because black Twitter still exists, right, Like we made TikTok

1957
01:44:49,360 --> 01:44:52,960
Speaker 5: popular with all the TikTok dances and the Resatisa type stories,

1958
01:44:53,000 --> 01:44:57,120
Speaker 5: and so our intellectual capital is what drives these platforms,

1959
01:44:57,200 --> 01:44:59,439
Speaker 5: right And so for me to have spoken on a

1960
01:44:59,479 --> 01:45:03,200
Speaker 5: stage like that around all of these important people wasn't

1961
01:45:03,439 --> 01:45:08,040
Speaker 5: a fraction of a match to the type of capital

1962
01:45:08,120 --> 01:45:12,520
Speaker 5: and wisdom and game not just business and technical expertise,

1963
01:45:12,560 --> 01:45:17,320
Speaker 5: but like mental expertise, financial expertise that I even received

1964
01:45:17,360 --> 01:45:20,280
Speaker 5: outside of speaking on stage. And for y'all to have

1965
01:45:20,439 --> 01:45:23,360
Speaker 5: access to that for two hundred and fifty dollars and

1966
01:45:24,080 --> 01:45:27,160
Speaker 5: a massive audience of like twenty thousand people of other

1967
01:45:27,200 --> 01:45:30,960
Speaker 5: folks that you can immediately tap into into your community

1968
01:45:31,200 --> 01:45:34,920
Speaker 5: who can help you build successfully across whatever you're trying

1969
01:45:34,920 --> 01:45:37,599
Speaker 5: to create in the world or scale in the world. Man,

1970
01:45:37,600 --> 01:45:40,200
Speaker 5: and people inside of Milking Institute, the ain't really trying

1971
01:45:40,200 --> 01:45:41,920
Speaker 5: to collab like you.

1972
01:45:41,880 --> 01:45:44,800
Speaker 1: Know, we got to that part.

1973
01:45:45,160 --> 01:45:48,519
Speaker 4: You know it only you gotta be in it to

1974
01:45:48,600 --> 01:45:50,840
Speaker 4: appreciate it. You gotta be in it to appreciate it.

1975
01:45:53,240 --> 01:45:54,160
Speaker 4: I'm glad you said that.

1976
01:45:54,160 --> 01:45:58,280
Speaker 5: Thank you, so you understand, Like investments is something special,

1977
01:45:58,360 --> 01:46:00,280
Speaker 5: like to be in a room with twenty thousand and

1978
01:46:00,680 --> 01:46:04,120
Speaker 5: black and brown folks who are looking for information on

1979
01:46:04,160 --> 01:46:06,840
Speaker 5: how to build and shape their own reality and are

1980
01:46:06,880 --> 01:46:10,439
Speaker 5: getting that information from industry leaders in their space. Like

1981
01:46:10,760 --> 01:46:13,040
Speaker 5: you guys have me on stage, you have Van Jones

1982
01:46:13,080 --> 01:46:15,479
Speaker 5: on stage, You have fifty cent on stage, like fifty cent,

1983
01:46:15,560 --> 01:46:18,200
Speaker 5: So this steak envitamin whatever like one hundred million dollars.

1984
01:46:18,439 --> 01:46:20,479
Speaker 5: This isn't just a rapper who made the songs that

1985
01:46:20,560 --> 01:46:23,240
Speaker 5: power my childhood and middle school years. This man is

1986
01:46:23,240 --> 01:46:27,880
Speaker 5: a prolific businessman teaching you. I am yeah, will are

1987
01:46:27,920 --> 01:46:34,000
Speaker 5: you things you know for two hundred and fifty dollars

1988
01:46:34,040 --> 01:46:36,600
Speaker 5: you can learn from somebody else. Taking one company for

1989
01:46:36,640 --> 01:46:39,800
Speaker 5: one hundred million dollars like that is, and especially someone

1990
01:46:39,800 --> 01:46:42,280
Speaker 5: who comes from our culture, who speaks it in our culture,

1991
01:46:42,320 --> 01:46:43,960
Speaker 5: who can translate it for you in a way of

1992
01:46:44,000 --> 01:46:46,400
Speaker 5: our culture and you get it. Connect with everybody to

1993
01:46:46,479 --> 01:46:48,719
Speaker 5: your left and right that's trying to build something similar,

1994
01:46:48,760 --> 01:46:51,320
Speaker 5: so you could build your squad on site that doesn't

1995
01:46:51,360 --> 01:46:53,439
Speaker 5: exist anywhere else. So I hope all the all are

1996
01:46:53,439 --> 01:46:55,800
Speaker 5: back and investments next year, because if you not, I'm

1997
01:46:55,840 --> 01:47:00,360
Speaker 5: gonna block you on a chat or something. I'm use Yeah,

1998
01:47:00,360 --> 01:47:03,759
Speaker 5: I to find you and can take that that had guy.

1999
01:47:04,840 --> 01:47:08,799
Speaker 3: Lease, yeah, oh man, it's incredible and.

2000
01:47:08,640 --> 01:47:11,840
Speaker 2: Tell them to clip that up and said at least yeah,

2001
01:47:11,840 --> 01:47:14,559
Speaker 2: somebody saying in chat next year'all come and support them.

2002
01:47:14,640 --> 01:47:19,080
Speaker 1: It's not about supporting us, it's yourself like like that's

2003
01:47:19,080 --> 01:47:22,680
Speaker 1: the part. But thank you, thank you for that.

2004
01:47:23,200 --> 01:47:25,640
Speaker 5: Yeah, And I'm telling you, like Milking was cool. I

2005
01:47:25,720 --> 01:47:27,800
Speaker 5: was definitely in a suit. I wasn't in my like

2006
01:47:28,240 --> 01:47:30,600
Speaker 5: you know, my little off White Air Forces. I was

2007
01:47:30,640 --> 01:47:32,439
Speaker 5: definitely in a suit. And I'm not gonna lie like

2008
01:47:32,479 --> 01:47:35,439
Speaker 5: I didn't meet cool people. But the energy wasn't the same.

2009
01:47:35,560 --> 01:47:37,519
Speaker 5: Everyone was there because they were trying to meet the

2010
01:47:37,600 --> 01:47:40,360
Speaker 5: who's who, not because they were trying to build and connect.

2011
01:47:40,400 --> 01:47:43,559
Speaker 5: It was like a weird, like good old boys club

2012
01:47:43,600 --> 01:47:46,439
Speaker 5: type energy. I'm not knocking milking. It's valuable. That's also

2013
01:47:46,479 --> 01:47:48,240
Speaker 5: how I met the dean of the Goldman School of

2014
01:47:48,240 --> 01:47:50,840
Speaker 5: Public Policy, which is where I'm creating the first ever

2015
01:47:51,520 --> 01:47:54,519
Speaker 5: AI policy focused research lab in the country. I met

2016
01:47:54,560 --> 01:47:57,200
Speaker 5: him in like a closed door session at the Milking

2017
01:47:57,240 --> 01:48:00,280
Speaker 5: Institute in Abu Dhabi back in December, right, So I

2018
01:48:00,320 --> 01:48:02,680
Speaker 5: get value from it. It's just a different kind of

2019
01:48:02,760 --> 01:48:06,080
Speaker 5: value when we're thinking about culture and community, when you're

2020
01:48:06,120 --> 01:48:11,040
Speaker 5: talking about capital because of positions people hold versus capital

2021
01:48:11,080 --> 01:48:14,240
Speaker 5: because of our experience and our community and our ability

2022
01:48:14,240 --> 01:48:16,320
Speaker 5: to translate and communicate that to you. So I'm not

2023
01:48:16,360 --> 01:48:18,880
Speaker 5: trying to get disinvited from Milking because I love it.

2024
01:48:18,920 --> 01:48:21,000
Speaker 5: I love going there. I'm actually going to one in

2025
01:48:21,160 --> 01:48:24,879
Speaker 5: Argentina in a couple of weeks. There's just a different

2026
01:48:25,280 --> 01:48:28,240
Speaker 5: energy's provided.

2027
01:48:28,040 --> 01:48:30,920
Speaker 4: Vib It's a vibe that's been curated that just can't

2028
01:48:30,960 --> 01:48:34,040
Speaker 4: really you know, you can't when you put the sauce

2029
01:48:34,080 --> 01:48:34,719
Speaker 4: on it.

2030
01:48:34,720 --> 01:48:35,479
Speaker 1: It's just different.

2031
01:48:35,760 --> 01:48:38,439
Speaker 3: It's just a different you know, it's a different that

2032
01:48:38,520 --> 01:48:41,120
Speaker 3: you know, Man, I appreciate this so much.

2033
01:48:41,240 --> 01:48:45,519
Speaker 4: You are definitely a genius in this space. And just

2034
01:48:45,560 --> 01:48:49,439
Speaker 4: for you to take your time out to educate, you know,

2035
01:48:49,640 --> 01:48:53,920
Speaker 4: people for free, I think is very commendable. For sure,

2036
01:48:54,720 --> 01:48:58,839
Speaker 4: So thank you, Thank you for sure, and let's definitely

2037
01:48:59,479 --> 01:49:02,000
Speaker 4: you know, to back and stay in touch and hopefully

2038
01:49:02,040 --> 01:49:04,040
Speaker 4: we can do some more things together in the future.

2039
01:49:04,080 --> 01:49:05,200
Speaker 1: For sure. Definitely.

2040
01:49:05,880 --> 01:49:08,320
Speaker 5: Oh well, I mean I got some ideas, you know,

2041
01:49:08,320 --> 01:49:10,880
Speaker 5: we could chat about offline. But I really appreciate you

2042
01:49:10,960 --> 01:49:13,920
Speaker 5: all for you know, elevating me on the EIL platform

2043
01:49:13,960 --> 01:49:16,240
Speaker 5: starting with nineteen keys, from having me on high level

2044
01:49:16,320 --> 01:49:19,479
Speaker 5: conversations to you guys bringing me to invest FST to

2045
01:49:19,600 --> 01:49:22,200
Speaker 5: having me back on here now to share some critical

2046
01:49:22,200 --> 01:49:25,320
Speaker 5: game with our communities. And I really appreciate what y'all doing.

2047
01:49:25,560 --> 01:49:27,240
Speaker 5: Look forward to collaborating in the future.

2048
01:49:28,439 --> 01:49:31,160
Speaker 1: Thank you so I appreciate you so much. Appreciate you lovers.

2049
01:49:31,200 --> 01:49:33,120
Speaker 1: Love y'all have a good one. Be safe.

2050
01:49:33,400 --> 01:49:36,640
Speaker 4: Yes, oh and make sure y'all tap in market Mondays

2051
01:49:36,680 --> 01:49:38,439
Speaker 4: and then this will be on podcast hour less and

2052
01:49:38,439 --> 01:49:39,800
Speaker 4: this will be saved on YouTube as well.

2053
01:49:39,920 --> 01:49:42,160
Speaker 1: Yep, all right, it's been real peace.

2054
01:49:43,760 --> 01:49:47,439
Speaker 6: An illegal alien from Guatemala charged with raping a child

2055
01:49:47,479 --> 01:49:51,280
Speaker 6: in Massachusetts. An MS thirteen gang member from El Salvador

2056
01:49:51,520 --> 01:49:55,640
Speaker 6: accused of murdering a Texas man of Venezuelan charged with

2057
01:49:55,720 --> 01:49:59,600
Speaker 6: filming and selling child pornography in Michigan. These are just

2058
01:49:59,680 --> 01:50:03,479
Speaker 6: some of the heinous migrant criminals caught because of President

2059
01:50:03,479 --> 01:50:07,040
Speaker 6: Donald J. Trump's leadership. I'm Christy nom the United States

2060
01:50:07,080 --> 01:50:11,880
Speaker 6: Secretary of Homeland Security. Under President Trump, attempted illegal border

2061
01:50:11,920 --> 01:50:15,479
Speaker 6: crossings are at the lowest levels ever recorded, and over

2062
01:50:15,520 --> 01:50:18,760
Speaker 6: one hundred thousand illegal aliens have been arrested. If you

2063
01:50:18,840 --> 01:50:22,679
Speaker 6: are here illegally, your next you will be fine nearly

2064
01:50:22,760 --> 01:50:26,800
Speaker 6: one thousand dollars a day, imprisoned, and deported, you will

2065
01:50:26,800 --> 01:50:30,479
Speaker 6: never return. But if you register using our CBP home

2066
01:50:30,520 --> 01:50:33,879
Speaker 6: app and leave now, you could be allowed to return legally.

2067
01:50:34,240 --> 01:50:39,000
Speaker 6: Do what's right. Leave now. Under President Trump America's laws,

2068
01:50:39,160 --> 01:50:41,160
Speaker 6: border and families.

2069
01:50:40,760 --> 01:50:41,599
Speaker 5: Will be protected.

2070
01:50:41,680 --> 01:50:43,839
Speaker 1: Sponsored by the United States Department of Homeland Security,