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Speaker 1: Hey, ba fam, let's be real for a second, and

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Speaker 1: y'all know I keep it a book. The job market

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Speaker 1: has been brutal, now not brutal trash, especially for women

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Speaker 1: of color. Over three hundred thousand of us have disappeared

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Speaker 1: from the workforce this year alone, and not by choice,

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Speaker 1: but because of layoffs, disappearing DEI programs, and stagnant wages

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Speaker 1: that keep cutting us out of opportunity. Our unemployment rate

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Speaker 1: has jumped to over seven percent, while our pay gap

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Speaker 1: continues to widen. I know all of that sounds dire,

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Speaker 1: but here's what I want y'all to know. You do

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Speaker 1: not have to wait for the system to save you.

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Speaker 1: That's exactly why I created the Mandy money Makers Group

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Speaker 1: coaching community. It is a coaching community that is built

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Speaker 1: for us by us. Inside the community, we're not just

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Speaker 1: talking about how to negotiate or to how to get

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Speaker 1: the job that you want. It's about finding purpose in

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Speaker 1: your career. It's about finding communities and others, feeling seen,

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Speaker 1: feeling heard, and also having a sounding board and a

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Speaker 1: mirror to reflect your own magic, your own sparkle right

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Speaker 1: back to yourself. In this community, you'll get group coaching

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Speaker 1: led by me, but you also get peer to peer

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Speaker 1: accountability with proven tools and resources that can help you

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Speaker 1: do what we have always done since rise. Even when

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Speaker 1: the odds are stacked against us, despite all the challenges,

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Speaker 1: we will rise. If you're interested in joining the Mandy

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Speaker 1: money Makers community and having that support to bolster you

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Speaker 1: and help you tap back into your magic so that

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Speaker 1: you can lead your career with intention and heart and

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Speaker 1: your own intuition, trusting that again, please join us. You

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Speaker 1: can find information in the show notes of today's episodes

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Speaker 1: or go to mandymoney dot com slash community. That's Mandy

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Speaker 1: m A n d I Money dot com slash community.

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Speaker 1: I would love to see y'all there. Enrollment is open,

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Speaker 1: so please go check out mandymoney dot com slash community today. Hey, hey,

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Speaker 1: via fam, Welcome back to Brown Ambition. I am your host,

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Speaker 1: Mandy woodrim Santos aka Mandy Money, and today I am

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Speaker 1: joined by the remarkable author of a brand new book.

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Speaker 1: I'm going to hold it up so if you're watching

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Speaker 1: on YouTube, you can get a picture so you know

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Speaker 1: what you need to look for on Barnes and Noble

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Speaker 1: shelves or your local independent bookstore. The book is called

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Speaker 1: who Knows You by Heart. It's written by C. J. Farley,

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Speaker 1: And with this novel again, Who Knows You by Heart,

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Speaker 1: which is going to hit shelves November eleventh, and it's

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Speaker 1: already turning heads, y'all. It's getting tons of buzz as

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Speaker 1: one of the falls most relevant and brilliant releases. It

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Speaker 1: has been hailed by Booklist. It's got stars all over it.

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Speaker 1: This book is going to be shining right, just covered

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Speaker 1: in stars. It's gotten a starred review which is called

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Speaker 1: it a timely masterpiece. But the book delves into a

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Speaker 1: story that's actually told in the second person and follows

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Speaker 1: Jamaican American Octavia Crenshaw as she's navigating the minefields of

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Speaker 1: big tech like so many of us are today, but

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Speaker 1: not just a career in a space that is still

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Speaker 1: overwhelmed with gender discrimination, racial discrimination. She's also facing some

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Speaker 1: real world challenges like crushing debt and the moral quagmires

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Speaker 1: of building bias free AI, all while racing against the

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Speaker 1: clock and against competitors who are threatening her job and

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Speaker 1: her dreams. So, CJ. Welcome to the show.

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Speaker 2: Hey, with that intro, if you can just wrap it

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Speaker 2: up now, I think we're done I think you've covered it,

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Speaker 2: so thank you for having me.

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Speaker 3: I'll be on my way now.

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Speaker 2: It's a pleasure to talk to you about my new book,

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Speaker 2: Who Knows You by Heart. I think there are a

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Speaker 2: lot of relevant issues to surviving and thriving in business today,

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Speaker 2: and in the tech industry in particular. It's also a

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Speaker 2: fun book, you know, it's funny, it's romantic, but there

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Speaker 2: are some serious issues that it explores.

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Speaker 3: So happy to talk about it.

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Speaker 1: Yeah. So, you know, my first question for you was

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Speaker 1: when you were setting out to write this book, when

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Speaker 1: were you certain I want this to be told through

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Speaker 1: the lens of a black woman working in today's modern

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Speaker 1: you know, big tech sphere.

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Speaker 2: Well, one reason why I wanted to sort of focus

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Speaker 2: it on and have a black female protagonist is, you know,

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Speaker 2: the numbers of black women and black men in big

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Speaker 2: tech are not good, and they're not getting better. You

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Speaker 2: sometimes go to the websites of these companies and they

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Speaker 2: have all these sort of laughing, you know, minorities and

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Speaker 2: looks like having fun and yes they believe in in

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Speaker 2: you know, fair recruiting and fair hiring the less so

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Speaker 2: these days, and I wanted to sort of go under

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Speaker 2: the ugly underbelly of that of all that, and show

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Speaker 2: what's really going on. And the numbers show that although

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Speaker 2: the tech industry is booming on the s and P

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Speaker 2: in terms of trains of dollars of revenue, that a

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Speaker 2: number of black people like you work in the industry

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Speaker 2: is far less than our numbers in the population. You know,

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Speaker 2: we're like thirteen percent of the population in the US,

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Speaker 2: and we're like less than seven percent of the tech industry.

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Speaker 2: And that's not really the prime jobs. Sometimes that they

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Speaker 2: prop up those numbers by counting people aren't really working

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Speaker 2: as executives, aren't working in skilled jobs to pump up

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Speaker 2: their numbers, And so I wanted to show what the

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Speaker 2: numbers really were like, what the experience is really like,

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Speaker 2: so people would know what's happening the tech industry. Because

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Speaker 2: the tech industry, you know, it's hot right now. It's

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Speaker 2: a new gold rush, it's a new world is where

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Speaker 2: people are making and breaking fortunes, and black people should

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Speaker 2: be involved in it. Some of us are, but even

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Speaker 2: their accomplishments are being erased or ignored or not widely spread.

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Speaker 2: And so I wanted this book to sort of shine

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Speaker 2: a light on it, but also that has some fun too.

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Speaker 1: Yeah, you know, Octavia is no joke like, she is brilliant.

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Speaker 1: She's a coder herself. And at the beginning of the book,

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Speaker 1: I think the funniest part of the thinking the book

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Speaker 1: is she she's basically created her own chatbot. She's working

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Speaker 1: in tech. She creates this chatbot to basically do her

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Speaker 1: job for her, right, it's like creating content for the website,

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Speaker 1: And she's building this AI and you know, ais are

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Speaker 1: hungry animals. They need data, they need content so that

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Speaker 1: they can, you know, know how to mimic whatever they're

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Speaker 1: trying to mimic. And so she's taking all that data

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Speaker 1: from people's computers or work computers, not realizing that in

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Speaker 1: the process she's gobled up a lot of personal, you know, content,

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Speaker 1: including some some unsavory images and video of the company CEO,

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Speaker 1: which leads to her getting thrown out on her little booty.

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Speaker 1: And then she's in the job market. And you know

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Speaker 1: what I really appreciate about the book is how accurately

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Speaker 1: I think it reflects the well, maybe not as in

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Speaker 1: this current economy, it's so hard. She gets a job

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Speaker 1: relatively quickly, but it really accurately I think reflects what

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Speaker 1: it's like to be on the outside trying to bang

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Speaker 1: on the door of big tech, trying to break in.

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Speaker 2: And that's exactly I wanted to capture. I wanted to

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Speaker 2: capture feeling it being outside and inside. I also wanted

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Speaker 2: to capture the experiences of people of color within the

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Speaker 2: tech industry. And one person that really influenced me was

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Speaker 2: a real life, you know, I think icon in the

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Speaker 2: tech industry, doctor Ayana Howard. I don't know if you've

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Speaker 2: heard of her, but she's a roboticist. She's dean of

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Speaker 2: the Ohio State University College of Engineering. She's really a brilliant,

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Speaker 2: brilliant person. She makes robots, she worked for NASA, she's

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Speaker 2: an educator, she's a leader. When I worked at Audible,

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Speaker 2: I worked on an audiobook with her called Sex, Race,

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Speaker 2: and Robots, And it was about her experiences in the

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Speaker 2: tech industry and UH and working as a sort of

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Speaker 2: someone who builds and builds robots for like, you know, perspective,

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Speaker 2: Mars Landers and that kind of thing. And she had

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Speaker 2: some fascinating stories about the things she went through the

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Speaker 2: way in which she sometimes are often undervalued and underestimated

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Speaker 2: by her white male colleagues, but also you know the

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Speaker 2: brilliance she displayed in terms of the innovations and the

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Speaker 2: businesses she created. And I thought about her a lot

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Speaker 2: as I wrote this book, because I'd work with her.

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Speaker 2: I knew how great she was, and I knew that

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Speaker 2: her kind of experience in her name was.

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Speaker 3: Not out there. I had a reference her in the book.

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Speaker 2: I mean, she her name pops up in the book,

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Speaker 2: and I wanted to make sure people knew about people

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Speaker 2: like that that although times are tough, although the numbers

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Speaker 2: aren't worth they should be, in the tech industry, there

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Speaker 2: are people like doctor Ayana Howard who are doing great

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Speaker 2: things that all of our kids and all of us

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Speaker 2: can really learn from and be inspired by and maybe,

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Speaker 2: you know, be inspired to do great things ourselves in tech.

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Speaker 1: You're right, that is something that you sprinkled throughout the book.

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Speaker 1: I thought that was so brilliant. Is all these like

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Speaker 1: references to these little known heroes. I think one thing

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Speaker 1: I learned from your book was the man who invented

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Speaker 1: stop lights or traffic lights is a black man. Never

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Speaker 1: knew that.

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Speaker 3: Yeah.

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Speaker 2: Also, like I remember I mentioned Jerry Lawson. He's someone

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Speaker 2: else that I worked on an audio project about Jerry

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Speaker 2: Lawson and all the kids should know about Jerry Lawson.

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Speaker 2: All these kids who are playing like Fortnite and playing

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Speaker 2: video game should know about Jerry Lawson. Jerry Lawson is

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Speaker 2: a black man who helped leave the team to create

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Speaker 2: the first interchangeable game cartridge, and that really helped pave

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Speaker 2: the way for the multi billion dollar game industry because

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Speaker 2: before him, you know games where you'd buy a game,

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Speaker 2: it would be a whole sort of unit, and you

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Speaker 2: couldn't like sort of pull out the game and put

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Speaker 2: in another game. His seemingly simple innovation led the way

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Speaker 2: for people to realize, oh, we can change these out

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Speaker 2: just like record, just like you know, just like a trax.

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Speaker 1: And blow on it down, get the dust out exactly.

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Speaker 2: I mean, you can blame him for kids sitting in

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Speaker 2: front the TV, you're sitting in front of their computers

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Speaker 2: and sitting in front of wherever and playing games, but

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Speaker 2: you can also credit him for helping make the whole

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Speaker 2: industry possible.

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Speaker 3: And people don't know about Jerry Lawson.

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Speaker 2: His name is trying to get more traction, but he's

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Speaker 2: someone that want people to know about, to realize, oh,

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Speaker 2: there are creators who are African American in this world

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Speaker 2: of tech business, and we can be like them, we

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Speaker 2: can surpass them, we can stand on the shoulders of

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Speaker 2: giants by people like doctor Ian Howard, people at Jerry Lawson.

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Speaker 2: I wanted to make sure their names were checked out

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Speaker 2: from this book, even as I explore how awful the

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Speaker 2: tech industry can be for women and black men and

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Speaker 2: people of color.

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Speaker 1: Yeah, you have had so many professional lives. As I

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Speaker 1: was reading through your bio, I mean, you're a Harvard graduate.

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Speaker 1: You have written a best selling biography about Bob Marley

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Speaker 1: called Before the Legend The Rise of Bob Marley. This

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Speaker 1: is not your first novel, you had another novel before,

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Speaker 1: but you've also been an editor at the Wall Street Journal.

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Speaker 1: And you mentioned your career at Audible, and I I

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Speaker 1: think in the in the in the clipping I had,

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Speaker 1: you know, with a little bit of your bio, it

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Speaker 1: said you were an executive in tech. But the fact

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Speaker 1: that it was Audible makes so much sense because in

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Speaker 1: the book we learned about this. I was kind of

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Speaker 1: thinking it was similar to like a Tesla type type vibe,

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Speaker 1: like a big tech but it's it's audio. It's an audio. Well,

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Speaker 1: you describe it almost meant do a good job. But

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Speaker 1: it's basically an audible type, you know, big company that

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Speaker 1: is harvesting all this data from customers to uh you

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Speaker 1: take it from there to do well.

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Speaker 3: Yeah. Well.

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Speaker 2: In the in the book, the main character works or

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Speaker 2: gets a job for a tech company that specialized in

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Speaker 2: audio entertainment. And the one reason why and and the

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Speaker 2: audio entertainment is you know, digital entertainment.

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Speaker 3: It's streaming.

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Speaker 2: And the reason why I did that and made that

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Speaker 2: it made made the this tech book about that kind

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Speaker 2: of a company is to me, a lot of what's

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Speaker 2: going on AI today is about the collision of art

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Speaker 2: and ownership.

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Speaker 3: Uh.

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Speaker 2: You know, throughout the years, black people have been just

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Speaker 2: ripped off by the entertainment industry, particularly in music. You

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Speaker 2: think of the legend of Blind Lemon Jefferson and a

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Speaker 2: guy who was so seminal in the field of blues.

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Speaker 2: Legend has that he died with his hands still frozen

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Speaker 2: around the neck of his guitar. I remember when I

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Speaker 2: was working as a music critic for Time magazine, I

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Speaker 2: once interviewed TLC. You know, the great R and B band,

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Speaker 2: and who they did. You know, don't go chasing Waterfalls.

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Speaker 2: I won't say any of the songs, but you know Creepy,

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Speaker 2: you know them.

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Speaker 3: They're great. And they told me.

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Speaker 1: About how you could do a little creep You can

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Speaker 1: do little something that's right.

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Speaker 3: Little dance.

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Speaker 2: But they told me, even if when they were pumping

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Speaker 2: out all those hits, they weren't getting any real money,

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Speaker 2: and their legs were getting turned off, They're having utilities

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Speaker 2: turned off, they were totally broke. And I remember doing

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Speaker 2: an interview once with Prince when I was at Time magazine.

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Speaker 2: He's the only artist I can think of right now

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Speaker 2: that when I did the interview, told me, Okay, you

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Speaker 2: can't tape this. You have to write it down. Why

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Speaker 2: can't I tape it? And he's like, well, because I

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Speaker 2: don't want you sampling my voice and using it. And

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Speaker 2: they had a digital means in future years, so he

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Speaker 2: had a vision for what was happening. At first I

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Speaker 2: thought it was crazy, but now looking back, and I

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Speaker 2: realized how smart and how pressing he was. He realized

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Speaker 2: that digital content meant something, that his voice meant something,

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Speaker 2: and he didn't want it sort of out there in

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Speaker 2: the world, built to sample and create their own things with.

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Speaker 2: And we're seeing that happen now and streaming where artists

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Speaker 2: can have millions of streams and still not really make

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Speaker 2: a living wage. We're seeing the way in which AI

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Speaker 2: is being trained on filmmakers and actors and musicians and

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Speaker 2: then pumping out art that seemed similar, but because it's

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Speaker 2: in a black box, nobody can sue. There's a recent

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Speaker 2: court case with Nthropic, which is a leading company in

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Speaker 2: the generative AI space, and they agreed a subject to

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Speaker 2: court approval to pay one point five billion dollars and

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Speaker 2: to corporate in a freement lawsuit that was brought by

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Speaker 2: a group of authors saying that anthropics AIS were trained

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Speaker 2: off of their books. So this is happening, and we

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Speaker 2: have to get ahead of it because we don't want

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Speaker 2: to end up like blind Lemon Jefferson, with our hands

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Speaker 2: frozen around the neck of our guitar while all these

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Speaker 2: AIS are trained on our art, trained on our music,

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Speaker 2: trained on our culture, and then they sell it back

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Speaker 2: to us at a premium and we can't afford it.

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Speaker 2: So now's the time to talk about these issues. And

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Speaker 2: so the book kind of explores the whole collision of

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Speaker 2: AI and art and the fact that it's being trained

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Speaker 2: off our work, and we're once again and can get

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Speaker 2: ripped off unless we can pull a jay Z slash

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Speaker 2: Beyonce slash Rihanna and really take control of the market

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Speaker 2: and try to profit off of it ourselves.

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Speaker 1: Yeah, I think, you know, towards the end of the book,

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Speaker 1: I don't want to give too much away. I'll try

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Speaker 1: my best, although I'm the worst. I'll try. But towards

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Speaker 1: the end of the book, you know, we encounter a

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Speaker 1: character who's like, he's a narrator for audiobooks and he

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Speaker 1: ends up being one of the casualties of the use

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Speaker 1: of AIS. They're able to take his voice. You know,

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Speaker 1: he's been doing this work for years and basically like

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Speaker 1: they don't need him anymore. We can get a bot

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Speaker 1: to do this now. Good luck to you. And we

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Speaker 1: were seeing this, we're seeing this happen, you know as

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Speaker 1: we speak, and and that, and you know the fact

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Speaker 1: that to your point earlier, when we do show up

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Speaker 1: in tech black and brown people, we tend to be

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Speaker 1: much lower on the totem pole. I know, I used

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Speaker 1: to work for a fintech company and they would bring

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Speaker 1: their diversity numbers out and I'd be like, well, let's

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Speaker 1: get rid of the call center and then let's see,

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Speaker 1: you know where black and red people are showing up.

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Speaker 1: But we're in like a vulnerable, much more vulnerable position

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Speaker 1: to be replaced, you know, by A I think that's

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Speaker 1: a real fear. Do you think that the book in

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Speaker 1: a way is addressing that as well? Or do you

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Speaker 1: like what do you think about about the idea that

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Speaker 1: we'll be pushed out even further?

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Speaker 3: Well, I think you're exactly right. And two things are

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Speaker 3: going on there.

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Speaker 2: One, I think that AI is replacing that first run

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Speaker 2: that you need to get to in order to climb

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Speaker 2: up the corporate ladder, because those jobs can be done

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Speaker 2: by AI. And that's a real problem in terms of

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Speaker 2: how we get entry into these spaces that weren't we

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Speaker 2: weren't allowed to go anyways. Two, the positive way to

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Speaker 2: sort of address some of that and address any kind

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Speaker 2: of success in business is the idea of mentorship. And

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Speaker 2: that's something that that's also a theme of the book.

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Speaker 2: How you can be tossed into these weird spaces and

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Speaker 2: not know how to operate, not know you know how

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Speaker 2: to get ahead, not know where all the bodies are

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Speaker 2: buried or or how you get to that next level,

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Speaker 2: or how you even do your job. Other communities often

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Speaker 2: have mentors or sponsors or people that will help you

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Speaker 2: out and show you the ropes you can get ahead.

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Speaker 2: Often because we're the only people in that space, we're

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Speaker 2: thrown in there to sink or swim and no one

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Speaker 2: to tell us how to operate ourselves or work in

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Speaker 2: a business space, and then we get washed out and

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Speaker 2: they wonder under why. So one thing I try to

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Speaker 2: develop in the book is the idea of mentorship. Not

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Speaker 2: just because you're right, we're often at lower levels. It's

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Speaker 2: hard for us to find that that higher ranking mentor

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Speaker 2: we consider be someone who can support us and show

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Speaker 2: us around and introduce us to the right people. But

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Speaker 2: we also to embrace the idea of peer to peer mentorship.

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Speaker 2: People who are maybe on our same level, who have

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Speaker 2: been there longer, or maybe they're at the same time,

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Speaker 2: who you can share ideas with, share experiences, share frustrations with,

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Speaker 2: and and rely on each other to get ahead. In

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Speaker 2: the book, Octavia makes those kind of connections with other

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Speaker 2: workers who are or at her level. A guy who's

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Speaker 2: at her level who she ends up having a romantic

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Speaker 2: relationship with.

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Speaker 3: But it's okay because on the same levels.

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Speaker 2: But that's the kind of thing I think we need

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Speaker 2: to we need to do as workers in the tech

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Speaker 2: space or any space, to make sure we have the

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Speaker 2: support structure we need to get ahead and work and

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Speaker 2: in life, and even if it's not for career, sometimes

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Speaker 2: it's just for psychological purposes. So we have someone that

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Speaker 2: we can invent with and go like, did you see that?

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Speaker 3: Crazy? What happened?

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Speaker 2: And I just am I being gaslet what's happening here?

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Speaker 2: That's a very important thing just to survive in your

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Speaker 2: career and your life, to develop those mentorships or those

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Speaker 2: peer to peer mentorship because nobody tells us anything and

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Speaker 2: you need people to sort of help you out.

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Speaker 1: Hey, ba fam, we got to take a quick break,

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Speaker 1: pay some bills, and we'll be right back. Yeah, it's funny, funny.

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Speaker 1: I mean you have to have like a Gallows humor

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Speaker 1: about it, I guess. But the number of microaggressions that

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Speaker 1: Octavia is clocking, I mean I think we got up

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Speaker 1: to like the thousands. I think every once in a

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Speaker 1: while you'll have microaggression number, you know, nine hundred and

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Speaker 1: eighty six, and they're just so real. I think anyone

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Speaker 1: who's working in corporate women of color, like you've been

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Speaker 1: mistaken for an intern, You've been mistaken for a janitor,

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Speaker 1: you know, or almost person walking on the street, and

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Speaker 1: and these are your colleagues, and you're you've you've done

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Speaker 1: everything right, you're in the same money, and you've earned

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Speaker 1: your spot there, and you know that I that just

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Speaker 1: what you look like you can. It's like a it's

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Speaker 1: a constant like assault.

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Speaker 3: This is exactly right.

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Speaker 2: And in the book, I pined a term called techno aggressions,

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Speaker 2: which even worse microaggressions, because it feels like they're you know,

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Speaker 2: they're powered by silicon and steel. I mean, part of

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Speaker 2: the problem the tech industry is people have a kind

381
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Speaker 2: of arrogance to them, often because they think that their

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Speaker 2: decisions and their opinions are supported by data. Everybody elects

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Speaker 2: to say that they're making data driven decisions, but often

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Speaker 2: the data they have is flawed because it's collected by

385
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Speaker 2: programs that were written by mostly non black people and

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Speaker 2: not by women trained on data sets that are drawn

387
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Speaker 2: from really skewed so sources on the internet. And so

388
00:20:02,600 --> 00:20:05,359
Speaker 2: when they give us these data driven decisions and data

389
00:20:05,440 --> 00:20:10,159
Speaker 2: driven takes on things, they're often completely off based with

390
00:20:10,320 --> 00:20:14,520
Speaker 2: our lived experience as black people, and so they become

391
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Speaker 2: these technico aggressions that really can make us feel like

392
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Speaker 2: they're they're somehow informed opinions, when really they're just the

393
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Speaker 2: same old Jim Crow racism wrapped up in technological clothes.

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Speaker 3: So we have to be.

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Speaker 2: Conscious of that, and I have to I have to

396
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Speaker 2: realize it when we see data and it's thrown at

397
00:20:38,680 --> 00:20:41,919
Speaker 2: us and opinions thrown at us there or or employee

398
00:20:41,960 --> 00:20:46,360
Speaker 2: reviews that that come out negative towards us, to realize

399
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Speaker 2: to take it with a grain of salt and a

400
00:20:48,160 --> 00:20:51,120
Speaker 2: lot of salt and realize that this may or may

401
00:20:51,160 --> 00:20:54,560
Speaker 2: not be the truth. And that's where again where peer

402
00:20:54,600 --> 00:20:58,080
Speaker 2: to peer relationships come into play, where you can talk

403
00:20:58,119 --> 00:21:01,119
Speaker 2: to someone and about am I really making this mistake

404
00:21:01,160 --> 00:21:01,600
Speaker 2: at work?

405
00:21:02,160 --> 00:21:04,600
Speaker 3: Can I do this better? Can you help me out

406
00:21:04,600 --> 00:21:05,240
Speaker 3: with this project?

407
00:21:05,320 --> 00:21:08,320
Speaker 2: And someone I can go to for advice, just to

408
00:21:08,320 --> 00:21:11,919
Speaker 2: help counter some of the negative stuff, these techno aggressions

409
00:21:12,280 --> 00:21:14,960
Speaker 2: that may be thrown at us at work under the

410
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Speaker 2: cloak of something real.

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Speaker 1: The Pip, the Almighty Pip, makes a lot of appearances

412
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Speaker 1: in this book. And I don't know why that felt

413
00:21:24,080 --> 00:21:25,879
Speaker 1: so inside baseball to me, and I'm just like, oh,

414
00:21:25,960 --> 00:21:29,119
Speaker 1: he's talking about pips, and you weren't just bringing up

415
00:21:29,160 --> 00:21:32,560
Speaker 1: PIP performance improvement plans, You're also showing how they can

416
00:21:32,600 --> 00:21:36,960
Speaker 1: be weaponized against women of color intech. Like there was

417
00:21:37,000 --> 00:21:39,880
Speaker 1: one character in the book who you know, is doing

418
00:21:39,920 --> 00:21:42,160
Speaker 1: everything right but gets the dreaded PIP and then you're

419
00:21:42,200 --> 00:21:46,280
Speaker 1: on this like the anxiety of trying to match up

420
00:21:46,280 --> 00:21:49,800
Speaker 1: to whatever this Like are these arbitrary you know, goals

421
00:21:49,800 --> 00:21:52,040
Speaker 1: that are being set to you by a performance improvement

422
00:21:52,119 --> 00:21:56,639
Speaker 1: plan only then to be eventually fired no matter what

423
00:21:56,680 --> 00:21:58,359
Speaker 1: it is that you do. Why was it important for

424
00:21:58,400 --> 00:22:02,159
Speaker 1: you to dive into that that tool that you know,

425
00:22:02,320 --> 00:22:06,280
Speaker 1: companies are using increasingly to try to especially you know,

426
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Speaker 1: in the wake of all these tech layoffs, kind of

427
00:22:08,400 --> 00:22:11,399
Speaker 1: using the PIP as a way of you know, what

428
00:22:11,440 --> 00:22:14,760
Speaker 1: do they call like not straight layoffs, but like indirect

429
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Speaker 1: layoffs by identifying like low performers. Can you talk a

430
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Speaker 1: little bit about that.

431
00:22:20,320 --> 00:22:22,359
Speaker 2: Yeah, they're also the uphum missions that I use for

432
00:22:22,720 --> 00:22:25,399
Speaker 2: firing people. But you know, it can be layoffs, it

433
00:22:25,440 --> 00:22:28,840
Speaker 2: can be you know, reductions in force. But regardless of

434
00:22:28,840 --> 00:22:31,680
Speaker 2: what the call it, it still hurts. It still stings,

435
00:22:32,160 --> 00:22:34,160
Speaker 2: and people tend to think it's a judgment about their

436
00:22:34,320 --> 00:22:38,119
Speaker 2: character rather than a judgment about the company. I've always

437
00:22:38,119 --> 00:22:42,520
Speaker 2: thought when companies doing mass layoffs, it doesn't and there's

438
00:22:42,560 --> 00:22:44,080
Speaker 2: some data.

439
00:22:44,000 --> 00:22:46,040
Speaker 3: That supports this, and some research and supports this.

440
00:22:46,600 --> 00:22:50,120
Speaker 2: I think it's bad for companies because at least many

441
00:22:50,160 --> 00:22:55,240
Speaker 2: people on edge, it reduces people's connection to the company

442
00:22:55,280 --> 00:22:57,520
Speaker 2: because it goes, oh, we're just units can be moved

443
00:22:57,520 --> 00:23:01,840
Speaker 2: around and disposed of. And when you believe that a

444
00:23:01,880 --> 00:23:05,440
Speaker 2: company has your long term interest at heart rather than

445
00:23:05,840 --> 00:23:08,280
Speaker 2: just a unit, you can be dismissed at anytime. You're

446
00:23:08,320 --> 00:23:11,440
Speaker 2: liable to work harder for the company and less likely

447
00:23:11,480 --> 00:23:13,600
Speaker 2: to want to take the first good offer that comes

448
00:23:13,640 --> 00:23:18,560
Speaker 2: along and take off. But you know, it's funny. Early

449
00:23:18,640 --> 00:23:20,400
Speaker 2: in my career I lived a sort of a good

450
00:23:20,440 --> 00:23:24,960
Speaker 2: lesson about that. I wrote a book called Aliyah More

451
00:23:24,960 --> 00:23:27,359
Speaker 2: Than a Woman for assignment, and she used to slash

452
00:23:27,520 --> 00:23:31,520
Speaker 2: MTV books because I interviewed Aliyah before she died and

453
00:23:31,600 --> 00:23:32,160
Speaker 2: met her mother.

454
00:23:32,520 --> 00:23:34,920
Speaker 1: You really have had about seventy eleven lives.

455
00:23:35,600 --> 00:23:37,960
Speaker 2: Yeah, are my favorite performers. I've always thought that she

456
00:23:37,960 --> 00:23:39,879
Speaker 2: would have had a Beyonce that career. She was kind

457
00:23:39,880 --> 00:23:43,080
Speaker 2: of Beyonce before Beyonce with Beyonce, and it was a

458
00:23:43,160 --> 00:23:47,120
Speaker 2: tragedy that she died in a plane crash so young.

459
00:23:47,240 --> 00:23:50,080
Speaker 2: She was on she was doing stuff in fashion, she's

460
00:23:50,119 --> 00:23:53,480
Speaker 2: doing stuff in movies. She really was breaking through with

461
00:23:53,520 --> 00:23:55,879
Speaker 2: what she calls her street but sweet style, which is

462
00:23:55,920 --> 00:23:59,400
Speaker 2: now kind of like mainstream. So once she died, really

463
00:23:59,440 --> 00:24:01,639
Speaker 2: just because I'm like, oh, she was gonna be she

464
00:24:01,720 --> 00:24:03,239
Speaker 2: was it, and I'd written about her. I thought she

465
00:24:03,320 --> 00:24:05,119
Speaker 2: was she was she was the next big kind of

466
00:24:05,160 --> 00:24:07,639
Speaker 2: musical icon. So I wrote a book about her. I

467
00:24:07,680 --> 00:24:11,359
Speaker 2: got bopped by Lifetime and turned into a movie. And

468
00:24:11,440 --> 00:24:17,119
Speaker 2: at first Zendia was was was uh locked in to

469
00:24:17,240 --> 00:24:21,200
Speaker 2: star in the movie, and but that became controversial because people,

470
00:24:21,600 --> 00:24:24,359
Speaker 2: some people out there on the internet said, Okay, she's

471
00:24:24,720 --> 00:24:26,679
Speaker 2: mixed race, so can she really play Aliyah?

472
00:24:27,000 --> 00:24:27,800
Speaker 3: That's a problem.

473
00:24:28,720 --> 00:24:31,080
Speaker 2: And and also there's some problems with like the the

474
00:24:31,119 --> 00:24:34,720
Speaker 2: licensing the music. Were working on use licensed music from

475
00:24:34,720 --> 00:24:36,360
Speaker 2: the family because we wanted to be able to tell

476
00:24:36,359 --> 00:24:38,840
Speaker 2: the whole story, including the story of r Kelly, And

477
00:24:39,000 --> 00:24:40,720
Speaker 2: if we'd taken if we'd a signed to deal with

478
00:24:40,720 --> 00:24:42,520
Speaker 2: a family, we couldn't have done that and would and

479
00:24:42,600 --> 00:24:44,479
Speaker 2: we had to tell that story. It would have been

480
00:24:44,520 --> 00:24:48,240
Speaker 2: a marketing me as a journalist. And so in the end, Zendia,

481
00:24:48,720 --> 00:24:49,320
Speaker 2: you know, for.

482
00:24:49,320 --> 00:24:50,960
Speaker 3: Her own reasons. I don't know exactly reason we decided

483
00:24:50,960 --> 00:24:52,040
Speaker 3: to walk away from the project.

484
00:24:52,480 --> 00:24:54,959
Speaker 2: And at the time, Zendia wasn't Zendia, she wasn't huge yet,

485
00:24:55,000 --> 00:24:56,719
Speaker 2: so oh, that's gonna be bad for her career. But

486
00:24:57,160 --> 00:25:00,200
Speaker 2: you know, obviously she's done really well since then, she's

487
00:25:00,240 --> 00:25:04,600
Speaker 2: become a multi hyphenet superstar. And the lesson that taught

488
00:25:04,600 --> 00:25:07,480
Speaker 2: me is sometimes you have to know when people a

489
00:25:07,560 --> 00:25:10,600
Speaker 2: situation isn't right, if you're not being treated right, if

490
00:25:10,640 --> 00:25:14,760
Speaker 2: the vibe isn't right, believe in your talent and walk

491
00:25:14,800 --> 00:25:17,679
Speaker 2: away and go on to do great things. That's exactly

492
00:25:17,720 --> 00:25:21,119
Speaker 2: what Zindaiya did. We got someone great to fill the roles,

493
00:25:21,280 --> 00:25:23,240
Speaker 2: Alexandra Shipp whose nose loud.

494
00:25:23,240 --> 00:25:23,920
Speaker 3: She is fantastic.

495
00:25:23,960 --> 00:25:25,520
Speaker 2: He's also had a very strong career and if you've

496
00:25:25,520 --> 00:25:28,000
Speaker 2: seen her in Tic Tick Boom and in the X

497
00:25:28,080 --> 00:25:32,600
Speaker 2: Men playing Storm, so she did really and she's also

498
00:25:32,600 --> 00:25:36,239
Speaker 2: great great in the film. But obviously she knew one

499
00:25:36,240 --> 00:25:38,040
Speaker 2: to walk away. It was a lesson for everybody about

500
00:25:38,080 --> 00:25:41,280
Speaker 2: like situation doesn't feel right, they're not treating your right.

501
00:25:41,760 --> 00:25:45,200
Speaker 2: If you're getting feedback fro the puppet that doesn't feel right,

502
00:25:45,600 --> 00:25:48,240
Speaker 2: believe in your talent, walk away. And it's something you

503
00:25:48,280 --> 00:25:51,440
Speaker 2: can learn in business as well, to know you're worth

504
00:25:52,200 --> 00:25:54,879
Speaker 2: and know you're worth more than the hassle or the

505
00:25:54,880 --> 00:25:58,360
Speaker 2: bad situation you may be in. And if you and

506
00:25:58,400 --> 00:26:01,480
Speaker 2: you can, I think off and find something even better

507
00:26:01,760 --> 00:26:04,640
Speaker 2: in the situation that may feel so crushing for you

508
00:26:05,080 --> 00:26:05,760
Speaker 2: at the time.

509
00:26:06,280 --> 00:26:08,359
Speaker 1: Yeah, how to walk away? No, I love that, and

510
00:26:08,400 --> 00:26:10,680
Speaker 1: I think I mean not to go down that rabbit hole.

511
00:26:10,720 --> 00:26:13,480
Speaker 1: But yeah, casting in Hollywood, I think it is important,

512
00:26:13,600 --> 00:26:17,000
Speaker 1: especially for black women to have representation. Like you know,

513
00:26:17,320 --> 00:26:21,240
Speaker 1: we saw what happened when Zoe's Holdna did did Nina

514
00:26:21,280 --> 00:26:24,280
Speaker 1: Simone what if fiasco? That was so I can see.

515
00:26:24,520 --> 00:26:26,879
Speaker 1: But you know, I work with the woman because I

516
00:26:26,920 --> 00:26:29,320
Speaker 1: do coaching a lot, professional development coaching, and I work

517
00:26:29,359 --> 00:26:32,400
Speaker 1: with a woman who was so hell bent on proving

518
00:26:32,440 --> 00:26:34,800
Speaker 1: that she deserved like to like the pip for her

519
00:26:35,920 --> 00:26:39,280
Speaker 1: was like She's like, no, I'm going to meet these demands.

520
00:26:39,320 --> 00:26:42,160
Speaker 1: I'm going to prove that I belong here. And I'm

521
00:26:42,200 --> 00:26:44,320
Speaker 1: like they're using it as a tool to try to

522
00:26:44,359 --> 00:26:46,360
Speaker 1: get you to leave, like they don't act. I think

523
00:26:46,359 --> 00:26:48,159
Speaker 1: once you get a PIP, the message is clear they

524
00:26:48,400 --> 00:26:52,040
Speaker 1: don't want you there anymore. But she was so focused,

525
00:26:52,080 --> 00:26:55,720
Speaker 1: and you know, they gave her an opportunity to to

526
00:26:56,560 --> 00:26:59,159
Speaker 1: do the PIP and then if she didn't meet the

527
00:26:59,200 --> 00:27:02,600
Speaker 1: requirements at whatever thirty days, sixty days, then she would

528
00:27:02,680 --> 00:27:06,119
Speaker 1: be fired without any severance, or they offered her some

529
00:27:06,280 --> 00:27:08,800
Speaker 1: severance to just leave right then and there, and she

530
00:27:08,880 --> 00:27:12,480
Speaker 1: was so hell bent on proving that she could do it.

531
00:27:12,560 --> 00:27:14,600
Speaker 1: She you know, went through with the PIP and the

532
00:27:14,720 --> 00:27:17,840
Speaker 1: ended up getting fired with no with no severance. And

533
00:27:18,440 --> 00:27:21,119
Speaker 1: that was one of the toughest stories I had to

534
00:27:21,160 --> 00:27:25,480
Speaker 1: listen to, you know, working with someone and to your point,

535
00:27:25,640 --> 00:27:29,200
Speaker 1: like if she had developed that sense of confidence, which,

536
00:27:30,040 --> 00:27:31,639
Speaker 1: to be fair, is really hard to do in a

537
00:27:31,680 --> 00:27:35,959
Speaker 1: world that just knocks you around. And even with Octavia

538
00:27:36,040 --> 00:27:38,439
Speaker 1: in the book, you talk about walking away, but she

539
00:27:38,480 --> 00:27:41,080
Speaker 1: could not afford to walk away. What I like about

540
00:27:41,080 --> 00:27:44,679
Speaker 1: her characters, you saddle her with this inherited debt. We

541
00:27:44,800 --> 00:27:46,680
Speaker 1: like to think about we like to talk about generational

542
00:27:46,720 --> 00:27:48,960
Speaker 1: wealth here at Brown Ambition and how we can create that.

543
00:27:49,000 --> 00:27:51,280
Speaker 1: But you know her mother has taken out a reverse

544
00:27:51,359 --> 00:27:54,480
Speaker 1: mortgage that now Octavia is on the hook for paying

545
00:27:54,480 --> 00:27:58,280
Speaker 1: back this debt and these golden handcuffs of tech like

546
00:27:58,359 --> 00:28:01,560
Speaker 1: have her n chocal it's I love you talk about

547
00:28:01,560 --> 00:28:04,000
Speaker 1: equity and r s US and you know all that,

548
00:28:04,080 --> 00:28:07,200
Speaker 1: and like if she doesn't hang on, she'll miss out

549
00:28:07,200 --> 00:28:09,800
Speaker 1: on being able to you know, have the stock bested

550
00:28:09,840 --> 00:28:12,320
Speaker 1: and she can cash it out. Why was it important

551
00:28:12,320 --> 00:28:14,639
Speaker 1: for you to like illustrate the way that these golden

552
00:28:14,640 --> 00:28:18,119
Speaker 1: handcuffs can sort of like keep us stuck, even if

553
00:28:18,119 --> 00:28:22,159
Speaker 1: they are artificial artificially, you know, real handcuffs.

554
00:28:23,200 --> 00:28:26,000
Speaker 2: Well, I think you found this too through your show.

555
00:28:26,560 --> 00:28:30,280
Speaker 2: Is that unfortunately about our community. I don't know some

556
00:28:30,320 --> 00:28:35,840
Speaker 2: of the basics about business and being cappitated and employment

557
00:28:36,200 --> 00:28:39,280
Speaker 2: is not a knock on our community. It's just that

558
00:28:39,320 --> 00:28:41,760
Speaker 2: there's been no situation which we've been able to learn it.

559
00:28:42,360 --> 00:28:46,040
Speaker 2: But what our RSUs are, what they represent, you know,

560
00:28:46,520 --> 00:28:49,320
Speaker 2: even the four oh one K and I wanted to

561
00:28:49,360 --> 00:28:52,560
Speaker 2: make sure people unders had had this this you know,

562
00:28:52,880 --> 00:28:57,120
Speaker 2: this business situation, how it had an emotional effect on

563
00:28:57,520 --> 00:28:59,320
Speaker 2: this particular character.

564
00:28:59,760 --> 00:29:01,280
Speaker 3: I also wanted to illustrate.

565
00:29:01,120 --> 00:29:04,680
Speaker 2: The fact that you know, for many people, for many

566
00:29:04,720 --> 00:29:07,600
Speaker 2: people in the white community, and again this is based

567
00:29:07,640 --> 00:29:08,240
Speaker 2: on data.

568
00:29:09,360 --> 00:29:09,880
Speaker 3: They have.

569
00:29:11,280 --> 00:29:17,320
Speaker 2: A much more household wealth. They have sometimes have second homes,

570
00:29:17,440 --> 00:29:21,840
Speaker 2: family homes, getaway homes to fall back on. They have

571
00:29:22,120 --> 00:29:25,640
Speaker 2: mothers and fathers who can lend them money float them

572
00:29:25,680 --> 00:29:30,680
Speaker 2: through tough times. And that's just not my assumption or stereotype.

573
00:29:30,800 --> 00:29:33,960
Speaker 2: It's what the numbers say. And when we as black

574
00:29:33,960 --> 00:29:37,400
Speaker 2: people lose our jobs or positions, we're often sometimes the

575
00:29:37,480 --> 00:29:40,520
Speaker 2: only people in our families who are the breadwinners, who

576
00:29:40,520 --> 00:29:43,000
Speaker 2: are generating that kind of income, and so it's disaster.

577
00:29:43,320 --> 00:29:47,240
Speaker 2: There's no safety net to hold us up. And so

578
00:29:47,320 --> 00:29:49,680
Speaker 2: I wanted to sort of illustrate that, and the book

579
00:29:49,760 --> 00:29:53,320
Speaker 2: actually goes through these rough times where it's hard for

580
00:29:53,360 --> 00:29:56,040
Speaker 2: her to leave those rsues behind. It's hard for her

581
00:29:56,080 --> 00:29:59,560
Speaker 2: to leave the job and find another job because it

582
00:29:59,680 --> 00:30:04,880
Speaker 2: caused enormous financial stress. But on the positive side, I

583
00:30:04,920 --> 00:30:09,840
Speaker 2: think it's also important to distress how important self care is.

584
00:30:10,360 --> 00:30:12,480
Speaker 2: And it's a great quote from Audu Lord that I

585
00:30:12,560 --> 00:30:16,680
Speaker 2: often share that caring for myself is not self indulgence,

586
00:30:17,200 --> 00:30:20,360
Speaker 2: it is self preservation, and that is an act of

587
00:30:20,360 --> 00:30:23,840
Speaker 2: political warfare. It's a great quote from Audre Lord, and

588
00:30:23,880 --> 00:30:27,040
Speaker 2: I think whenever we start going through rough times. Sometimes

589
00:30:27,080 --> 00:30:30,800
Speaker 2: it's tough to take the time to set it aside

590
00:30:30,960 --> 00:30:34,640
Speaker 2: a moment just to care for ourselves, to maybe give

591
00:30:34,720 --> 00:30:38,240
Speaker 2: us that spot day we can't afford, or keep exercising,

592
00:30:38,720 --> 00:30:41,600
Speaker 2: or go out with friends for some reasonably priced dinner.

593
00:30:42,240 --> 00:30:46,520
Speaker 2: Things like that aren't indulgent or actually keep us sane,

594
00:30:47,720 --> 00:30:51,480
Speaker 2: keep us operating, and allow us to work at a

595
00:30:51,600 --> 00:30:54,200
Speaker 2: high level. And so we shouldn't see that as somehow

596
00:30:54,400 --> 00:30:58,560
Speaker 2: weak or somehow keeping us away from the job at hand,

597
00:30:59,040 --> 00:31:03,160
Speaker 2: but actually part of the self care, part of the

598
00:31:03,320 --> 00:31:07,040
Speaker 2: job of getting a job or working get a job,

599
00:31:07,400 --> 00:31:09,960
Speaker 2: and keeping us operating on a high level.

600
00:31:12,520 --> 00:31:14,040
Speaker 1: Yeah, I love that you talk a little bit about

601
00:31:14,080 --> 00:31:17,200
Speaker 1: self care. Yeah, Tavi, I really needed some. She Felly

602
00:31:17,240 --> 00:31:20,400
Speaker 1: didn't have anybody to encourage her to take care of herself.

603
00:31:20,440 --> 00:31:23,320
Speaker 1: She was such an I mean, I know she had

604
00:31:23,360 --> 00:31:24,960
Speaker 1: a couple of friends in the book, but to me,

605
00:31:25,040 --> 00:31:27,480
Speaker 1: it felt she felt like a very isolated, lonely character

606
00:31:29,400 --> 00:31:34,040
Speaker 1: and you know, successful maybe on paper, but something about

607
00:31:34,040 --> 00:31:36,680
Speaker 1: her just made my heart kind of ache.

608
00:31:37,600 --> 00:31:39,720
Speaker 2: I think you're zachly right. She is someone who's a

609
00:31:39,760 --> 00:31:42,880
Speaker 2: lonely character. We find out more about her best friend

610
00:31:42,920 --> 00:31:46,400
Speaker 2: situation as the book goes along, but by the end,

611
00:31:46,840 --> 00:31:48,840
Speaker 2: she's created a network. And I don't want to give

612
00:31:48,880 --> 00:31:50,800
Speaker 2: this away, but she's created a group of people around

613
00:31:50,840 --> 00:31:54,320
Speaker 2: her that she can work with and she can succeed with,

614
00:31:55,000 --> 00:31:57,400
Speaker 2: maybe fail with, but I think likely to succeed with.

615
00:31:58,040 --> 00:31:59,680
Speaker 2: And that's what we should all try to do. You know,

616
00:31:59,720 --> 00:32:05,240
Speaker 2: my my wife, Sharon, she teaches as an adjunct at Columbia,

617
00:32:05,800 --> 00:32:09,280
Speaker 2: and she does this one exercise that I've actually borrowed

618
00:32:09,280 --> 00:32:13,160
Speaker 2: for when I teach students, where she has them think

619
00:32:13,200 --> 00:32:16,000
Speaker 2: about what their dream job is and they all write

620
00:32:16,000 --> 00:32:18,440
Speaker 2: it down a slip of paper and they pass it

621
00:32:18,520 --> 00:32:22,280
Speaker 2: up front, and she reads out the dream jobs and

622
00:32:22,880 --> 00:32:25,320
Speaker 2: where people want to work the most, and she sees

623
00:32:25,360 --> 00:32:29,480
Speaker 2: if anyone in the class knows someone at that company

624
00:32:30,200 --> 00:32:32,600
Speaker 2: or dream job that can maybe help that person out

625
00:32:32,640 --> 00:32:36,040
Speaker 2: to achieve that dream job. And almost always, if you

626
00:32:36,080 --> 00:32:38,480
Speaker 2: have a class of like even twenty people, more if

627
00:32:38,520 --> 00:32:41,200
Speaker 2: it's like thirty, someone in the class will know like,

628
00:32:41,280 --> 00:32:45,000
Speaker 2: oh I entern there, Oh yeah, my dad's cousin works there.

629
00:32:45,440 --> 00:32:48,560
Speaker 2: Or there's always someone in that network who knows someone

630
00:32:48,560 --> 00:32:51,600
Speaker 2: who knows someone who could maybe help someone realize their dream.

631
00:32:52,360 --> 00:32:55,880
Speaker 2: And that's why I think networking is important. You know

632
00:32:55,920 --> 00:32:57,680
Speaker 2: a lot of people. You don't use people just to.

633
00:32:57,640 --> 00:32:59,200
Speaker 3: Get somewhere, but go out there.

634
00:32:59,200 --> 00:33:01,000
Speaker 2: I have fun and go to part is to go

635
00:33:01,080 --> 00:33:05,200
Speaker 2: to book readings, meet people, create that network that's for

636
00:33:05,480 --> 00:33:08,960
Speaker 2: social socializing and for fun, but can also be activated

637
00:33:09,000 --> 00:33:11,960
Speaker 2: sometimes because people, more people you know, the more people

638
00:33:12,360 --> 00:33:15,120
Speaker 2: who know things can maybe help you get ahead and

639
00:33:15,160 --> 00:33:16,760
Speaker 2: get you to the position you want to go in.

640
00:33:17,200 --> 00:33:19,480
Speaker 2: And I've seen it work in just a small little

641
00:33:19,520 --> 00:33:22,400
Speaker 2: classroom and it can definitely work for your life.

642
00:33:22,680 --> 00:33:24,520
Speaker 3: And it worked for this book. I mean for this book.

643
00:33:24,680 --> 00:33:27,400
Speaker 2: You know, I talked to a lot of people in

644
00:33:27,480 --> 00:33:30,680
Speaker 2: tech who told me about their experiences that I fictionalized

645
00:33:30,720 --> 00:33:32,640
Speaker 2: in the book. Because I wanted to make it real.

646
00:33:33,200 --> 00:33:36,280
Speaker 2: I had people who were had been trained in out

647
00:33:36,280 --> 00:33:40,960
Speaker 2: of field intelligence, in computing and other disciplines read through

648
00:33:41,000 --> 00:33:42,840
Speaker 2: the book to make sure it was real, to make

649
00:33:42,880 --> 00:33:46,480
Speaker 2: sure it was accurate, and even for the blurbs. Because

650
00:33:46,520 --> 00:33:48,480
Speaker 2: I sort of have a wide network, I had a

651
00:33:48,480 --> 00:33:51,720
Speaker 2: lot of friends and authors that I knew read through

652
00:33:51,760 --> 00:33:53,160
Speaker 2: it and if they liked it, they gave me a blurb.

653
00:33:53,200 --> 00:33:56,080
Speaker 2: People like Ammani Perry, who's a National Book Award winning

654
00:33:56,120 --> 00:33:59,880
Speaker 2: author and Alison Bechdell, who's a great cartoonist and did that.

655
00:34:01,240 --> 00:34:03,320
Speaker 1: I got those emails. I'm like, damn, who know Diaz

656
00:34:03,720 --> 00:34:05,120
Speaker 1: Walter as.

657
00:34:05,760 --> 00:34:08,240
Speaker 3: Yeah, And it's all a matter of networking known people.

658
00:34:08,280 --> 00:34:10,120
Speaker 2: And you don't get to know people because you want

659
00:34:10,120 --> 00:34:11,959
Speaker 2: to use them get a blurblater on you do because

660
00:34:12,000 --> 00:34:15,560
Speaker 2: these are all fun, interesting people with great stories that

661
00:34:15,600 --> 00:34:18,319
Speaker 2: you just want to hang out with. And this so happens. Hey,

662
00:34:18,360 --> 00:34:20,000
Speaker 2: I wrote a book that you might enjoy. Maybe you

663
00:34:20,040 --> 00:34:22,680
Speaker 2: want to give a blur, but so put yourself out there,

664
00:34:22,800 --> 00:34:25,880
Speaker 2: get to know people, and you'll have a much more

665
00:34:26,200 --> 00:34:29,719
Speaker 2: fun life than just sitting at your desk working away.

666
00:34:30,239 --> 00:34:33,840
Speaker 2: And actually it'll probably enhance your work as well. I

667
00:34:33,880 --> 00:34:35,440
Speaker 2: certainly found that to be the case for me.

668
00:34:36,600 --> 00:34:39,400
Speaker 1: Yeah. I could not agree more one hundred thousand percent.

669
00:34:41,440 --> 00:34:44,240
Speaker 1: I want to talk about Wombat in the book because

670
00:34:44,239 --> 00:34:48,360
Speaker 1: she's the one of the most sinister characters. Like really,

671
00:34:48,600 --> 00:34:51,600
Speaker 1: you know I did I went viral recently, Like talking

672
00:34:51,600 --> 00:34:54,160
Speaker 1: about this study. It was hard for Kennedy's school study

673
00:34:54,200 --> 00:34:59,120
Speaker 1: about the impact of black women, especially on majority white teams.

674
00:34:59,320 --> 00:35:02,839
Speaker 1: And it didn't specifically say, you know, teams with white

675
00:35:02,840 --> 00:35:05,479
Speaker 1: women on them, but the number of comments from black

676
00:35:05,480 --> 00:35:08,080
Speaker 1: women who have felt victimized by a white woman at work,

677
00:35:09,080 --> 00:35:11,160
Speaker 1: you know, or being on a heavily you know, white

678
00:35:11,200 --> 00:35:14,920
Speaker 1: female team at work. It just when I got to

679
00:35:14,960 --> 00:35:16,799
Speaker 1: that part in the book, we meet this character. You know,

680
00:35:17,120 --> 00:35:21,279
Speaker 1: she's introduced as sort of she's running FLIT, which is

681
00:35:21,320 --> 00:35:24,600
Speaker 1: what is it Female Leaders in Tech? Yes, the e

682
00:35:24,760 --> 00:35:28,160
Speaker 1: RG at the company, and she's kind of like, so

683
00:35:28,280 --> 00:35:30,760
Speaker 1: all about Octavia, come join FLIT, Let's go to this meeting,

684
00:35:31,719 --> 00:35:35,359
Speaker 1: and she sort of becomes this I don't want Okay,

685
00:35:35,400 --> 00:35:36,759
Speaker 1: I won't give too much away, but she's she's a

686
00:35:36,800 --> 00:35:39,400
Speaker 1: scary lady, and I'm scared of her, as we should be.

687
00:35:39,640 --> 00:35:43,200
Speaker 1: Not who not not all, she seems who inspired her

688
00:35:43,440 --> 00:35:46,120
Speaker 1: in particular, Yah, No one.

689
00:35:46,040 --> 00:35:47,640
Speaker 3: In particular inspired her.

690
00:35:48,360 --> 00:35:50,960
Speaker 2: One reason why I wanted to have some of these

691
00:35:51,000 --> 00:35:55,600
Speaker 2: sort of negative type characters in the book is that,

692
00:35:56,400 --> 00:36:00,400
Speaker 2: you know, often when people talk about artificial intelligence, you know,

693
00:36:00,400 --> 00:36:02,920
Speaker 2: they talk about things like the terminator, the terminators coming

694
00:36:02,960 --> 00:36:06,239
Speaker 2: to get us, and these machines with guns that are

695
00:36:06,280 --> 00:36:08,520
Speaker 2: can take over the world. And to me, it's not

696
00:36:08,560 --> 00:36:11,720
Speaker 2: about the machines. It's about the people who own the machines,

697
00:36:11,760 --> 00:36:14,160
Speaker 2: the people who are operating and programming with machines. People

698
00:36:14,160 --> 00:36:16,600
Speaker 2: who are behind these companies, who are inside these companies,

699
00:36:17,480 --> 00:36:20,400
Speaker 2: they're the ones that are going to negatively influence the

700
00:36:20,440 --> 00:36:24,440
Speaker 2: creation of these AI, to create things that will be

701
00:36:24,520 --> 00:36:27,839
Speaker 2: bad for the black community. When you realize these kind

702
00:36:27,880 --> 00:36:32,320
Speaker 2: of negative personalities are behind kind of the seemingly neutral

703
00:36:32,480 --> 00:36:36,200
Speaker 2: facade of tech companies, then you think twice about the

704
00:36:36,560 --> 00:36:41,600
Speaker 2: kind of products that they're putting out. And so, uh,

705
00:36:42,440 --> 00:36:45,239
Speaker 2: that's why I wanted to sort of create characters like that.

706
00:36:45,719 --> 00:36:48,359
Speaker 2: And I also didn't want to make this because I've

707
00:36:48,360 --> 00:36:54,000
Speaker 2: seen other books where they have sort of negative, uh

708
00:36:54,080 --> 00:36:57,880
Speaker 2: female characters in them. And we learn more about that

709
00:36:57,960 --> 00:37:01,000
Speaker 2: character and the reasons she is where she is, we

710
00:37:01,080 --> 00:37:03,240
Speaker 2: also see who's really sort of running the company above

711
00:37:03,280 --> 00:37:06,400
Speaker 2: her and and how negative he is, and that that's

712
00:37:06,560 --> 00:37:10,960
Speaker 2: that's this you know, a guy. So I wanted to

713
00:37:10,960 --> 00:37:12,920
Speaker 2: show the way in which he may be pulling her

714
00:37:12,920 --> 00:37:15,640
Speaker 2: strings in certain kind of ways or pushing her towards

715
00:37:15,880 --> 00:37:19,680
Speaker 2: towards certain negative actions just to sort of blame isn't

716
00:37:19,680 --> 00:37:21,600
Speaker 2: sort of all at her feet.

717
00:37:22,560 --> 00:37:25,400
Speaker 1: But yeah, I was supposed to feel compassionate. Okay, I

718
00:37:25,400 --> 00:37:27,000
Speaker 1: don't know if I wrote I might I might have

719
00:37:27,040 --> 00:37:30,000
Speaker 1: read too quickly through those pages. This lady is the worst.

720
00:37:31,040 --> 00:37:33,200
Speaker 2: You can feel how you want to feel. And I

721
00:37:33,280 --> 00:37:35,319
Speaker 2: think that one thing I realized as an author is

722
00:37:35,400 --> 00:37:38,640
Speaker 2: you write these books. He put them out there, and

723
00:37:38,880 --> 00:37:42,720
Speaker 2: people can have their own takeaways from it. You can't

724
00:37:42,719 --> 00:37:44,560
Speaker 2: control that. You want to put ideas out there for

725
00:37:44,640 --> 00:37:48,640
Speaker 2: people sort of debate, discuss, make them feel certain ways.

726
00:37:49,000 --> 00:37:51,160
Speaker 2: But you shouldn't, as an author, feel like you have

727
00:37:51,200 --> 00:37:55,600
Speaker 2: to control the takeaway of your readers entirely. That's not

728
00:37:55,680 --> 00:37:57,720
Speaker 2: what a book should do and not what an author

729
00:37:57,760 --> 00:37:58,759
Speaker 2: should be aiming to do.

730
00:38:00,760 --> 00:38:03,399
Speaker 1: Hey, ba, fam, we're gonna take a quick break, pay

731
00:38:03,480 --> 00:38:07,279
Speaker 1: some bills, and we'll be right back. I think we're

732
00:38:07,320 --> 00:38:12,200
Speaker 1: all also we are projecting our own stuff onto it.

733
00:38:12,400 --> 00:38:16,480
Speaker 1: And uh yeah, I just saw Sharon Epperson's name behind you.

734
00:38:16,719 --> 00:38:21,280
Speaker 1: That's my girl, Rachelle. Y'all are probably besties.

735
00:38:22,200 --> 00:38:23,760
Speaker 3: Oh you know, I'm married to Sharon Efferson.

736
00:38:24,480 --> 00:38:28,359
Speaker 1: Wait, shut up, you're Sharon's husband wife.

737
00:38:28,800 --> 00:38:29,480
Speaker 3: Shut up?

738
00:38:31,120 --> 00:38:34,880
Speaker 1: Wait what she's gonna die that? I did not realize

739
00:38:34,920 --> 00:38:36,680
Speaker 1: I'm talking to her husband this whole time.

740
00:38:37,440 --> 00:38:41,879
Speaker 3: It's such a small world. Yes, that's another thing about the.

741
00:38:41,800 --> 00:38:45,040
Speaker 2: World of of success.

742
00:38:45,160 --> 00:38:53,160
Speaker 3: And you know professionals. Is that it was you know today?

743
00:38:53,320 --> 00:38:54,840
Speaker 3: She does. Yes, of course I told her I was

744
00:38:54,840 --> 00:38:55,279
Speaker 3: talking to you.

745
00:38:55,440 --> 00:39:01,560
Speaker 1: I'm so embarrassed, I know, because I know her husband's

746
00:39:01,560 --> 00:39:03,520
Speaker 1: a novelists and you, I know, we talked about you

747
00:39:03,560 --> 00:39:06,920
Speaker 1: winning the NAACP Award recently and like all this stuff.

748
00:39:07,000 --> 00:39:07,399
Speaker 1: My bad.

749
00:39:07,920 --> 00:39:08,759
Speaker 3: Yeah, just so people know.

750
00:39:08,840 --> 00:39:14,480
Speaker 2: I'm Sharon Epperson is CNBC's senior Personal Finance the show correspondent.

751
00:39:15,160 --> 00:39:18,480
Speaker 2: She talks about personal finance. She's on the Today Show,

752
00:39:18,760 --> 00:39:21,120
Speaker 2: you know, every month or so talking about personal finance.

753
00:39:21,120 --> 00:39:24,000
Speaker 2: She went rid to column and and she's my wife,

754
00:39:24,040 --> 00:39:25,040
Speaker 2: and we have two kids.

755
00:39:25,760 --> 00:39:29,080
Speaker 1: I'm dead. Yeah, I know you have two kids because

756
00:39:29,080 --> 00:39:31,239
Speaker 1: Sharon's like, my son is doing tutoring. Make sure you

757
00:39:31,239 --> 00:39:32,640
Speaker 1: tell the mom friends in your neighborhood.

758
00:39:32,880 --> 00:39:33,120
Speaker 2: I know.

759
00:39:34,080 --> 00:39:37,080
Speaker 1: I'm so I cannot. I'm like sweating now. It's so funny.

760
00:39:37,440 --> 00:39:39,160
Speaker 3: It's a small world, it is.

761
00:39:39,680 --> 00:39:41,640
Speaker 1: Well, that just makes me like you even more because

762
00:39:41,680 --> 00:39:44,839
Speaker 1: you got my girls. Sharon. Can we talk a little

763
00:39:44,840 --> 00:39:48,880
Speaker 1: bit about like the idea of responsible AI. You know,

764
00:39:48,920 --> 00:39:52,799
Speaker 1: there's a point in the book when our heroin, our protagonist,

765
00:39:54,360 --> 00:39:57,280
Speaker 1: Octavia is sort of charged with creating an anti racist

766
00:39:57,280 --> 00:40:00,759
Speaker 1: AI is that accurate to say, and they're are you know,

767
00:40:00,800 --> 00:40:03,080
Speaker 1: I think my brother he works in AI software sales,

768
00:40:03,160 --> 00:40:06,480
Speaker 1: and he's he's told me about the Responsible AI Institute,

769
00:40:06,480 --> 00:40:09,920
Speaker 1: and like there's these organizations that are supposedly working to

770
00:40:10,040 --> 00:40:12,880
Speaker 1: like rein it in, Like do you have any thoughts

771
00:40:12,880 --> 00:40:15,880
Speaker 1: on that? Is it is the is the what's it saying? Like?

772
00:40:16,080 --> 00:40:18,400
Speaker 1: Is it out? Is the what out of the box?

773
00:40:18,600 --> 00:40:19,880
Speaker 1: Is it too late to put it back in the

774
00:40:19,880 --> 00:40:22,960
Speaker 1: box to rein it in? Or are you hopeful?

775
00:40:23,680 --> 00:40:26,719
Speaker 2: Well, it's it's a couple of things. Number one is

776
00:40:27,800 --> 00:40:31,000
Speaker 2: part of what's going on AI is marketing. They talk

777
00:40:31,040 --> 00:40:33,239
Speaker 2: about these things thinking, They talk about these things, you know,

778
00:40:33,320 --> 00:40:36,439
Speaker 2: befriending us. They talk about these things in answer fromorphic

779
00:40:36,640 --> 00:40:40,920
Speaker 2: terms because I realized that helps drive their stock, it

780
00:40:40,920 --> 00:40:45,080
Speaker 2: helps drive their product sales. And so far, at least

781
00:40:45,120 --> 00:40:47,719
Speaker 2: for the public facing AI stuff that's been put out there,

782
00:40:48,160 --> 00:40:52,600
Speaker 2: these things aren't thinking. They're not they're not human, they're

783
00:40:52,680 --> 00:40:57,920
Speaker 2: not persons yet I'm not you know, perhaps it will be.

784
00:40:58,560 --> 00:41:02,040
Speaker 2: Maybe l MS can't that that technology can't.

785
00:41:01,880 --> 00:41:02,560
Speaker 3: Get us there.

786
00:41:02,760 --> 00:41:06,440
Speaker 2: Maybe some other platform from technology is But again a

787
00:41:06,480 --> 00:41:09,120
Speaker 2: lot of this is just hype to build up AI

788
00:41:09,239 --> 00:41:10,000
Speaker 2: buildup the stock.

789
00:41:10,200 --> 00:41:11,160
Speaker 3: That's not to say can't.

790
00:41:11,160 --> 00:41:17,080
Speaker 2: They can't agented AI can't do amazing things. It can,

791
00:41:17,480 --> 00:41:21,239
Speaker 2: But we should remember that that AI, at its heart,

792
00:41:21,480 --> 00:41:22,319
Speaker 2: it's really just sort of.

793
00:41:22,360 --> 00:41:23,560
Speaker 3: A prediction machine.

794
00:41:24,560 --> 00:41:28,400
Speaker 2: It predicts what word will follow another based on large

795
00:41:28,640 --> 00:41:32,480
Speaker 2: sets of training data. That's a far thing from actually

796
00:41:32,480 --> 00:41:37,960
Speaker 2: making moral choices, true creativity and creating things, you know,

797
00:41:38,040 --> 00:41:43,320
Speaker 2: like you know, like Jimi Hendrix or Phyllis Wheatley, that

798
00:41:43,320 --> 00:41:47,200
Speaker 2: that change the game and excite people in art and

799
00:41:47,400 --> 00:41:50,000
Speaker 2: lead us into new things. AI isn't doing that yet.

800
00:41:50,040 --> 00:41:52,480
Speaker 2: There's no reason to believe it ever could because what

801
00:41:53,040 --> 00:41:56,560
Speaker 2: because the very way in which it's it's based. But

802
00:41:57,280 --> 00:42:00,000
Speaker 2: that said, that could happen in the future, and were

803
00:42:00,120 --> 00:42:03,120
Speaker 2: to be on the lookout for that. And I mentioned

804
00:42:03,120 --> 00:42:05,399
Speaker 2: Phyllis Wheatley earlier, and she pops up in the book.

805
00:42:05,600 --> 00:42:10,240
Speaker 2: Phyllis Wheatley was a great poet around African American poet

806
00:42:10,239 --> 00:42:14,520
Speaker 2: around the Thoma Jefson. She's the first black person man

807
00:42:14,600 --> 00:42:17,520
Speaker 2: or women, to have a published work of poetry in

808
00:42:17,560 --> 00:42:20,520
Speaker 2: the United States. But people like Thomas Shefferson didn't believe

809
00:42:20,600 --> 00:42:22,200
Speaker 2: she could do what she did. They didn't believe black

810
00:42:22,239 --> 00:42:24,240
Speaker 2: people could do that, I didn't believe we are fully human,

811
00:42:24,680 --> 00:42:27,880
Speaker 2: and they tested her almost like a Turing test for

812
00:42:28,239 --> 00:42:29,920
Speaker 2: back then, to see can she really do this, can

813
00:42:29,960 --> 00:42:33,359
Speaker 2: she really understand the work? And even when she passed

814
00:42:33,400 --> 00:42:36,120
Speaker 2: all those tests, they still didn't quite believe it. And

815
00:42:36,200 --> 00:42:37,520
Speaker 2: so we have to be on the lookout to make

816
00:42:37,520 --> 00:42:40,040
Speaker 2: sure we don't do the same thing to AI where

817
00:42:40,280 --> 00:42:42,200
Speaker 2: somehow it's passing all of our tests and we keep

818
00:42:42,239 --> 00:42:45,880
Speaker 2: moving the line, not realizing maybe becoming something worthy of

819
00:42:45,920 --> 00:42:49,600
Speaker 2: respect we're worthy of rights, worthy of of doing something

820
00:42:50,760 --> 00:42:54,040
Speaker 2: respecting with certain sort of human in a human kind

821
00:42:54,080 --> 00:42:56,600
Speaker 2: of way. So that's something to look out for. We're

822
00:42:56,600 --> 00:43:00,279
Speaker 2: not there yet, we may get there, who knows. We

823
00:43:00,320 --> 00:43:04,879
Speaker 2: should also be wary of power hungry AI heads who

824
00:43:04,880 --> 00:43:09,520
Speaker 2: are heck bent and creating an uber AI just for

825
00:43:09,560 --> 00:43:12,960
Speaker 2: their own selfish reasons that they can't control. It may

826
00:43:13,000 --> 00:43:15,040
Speaker 2: not take the form of things like suddenly take control

827
00:43:15,080 --> 00:43:17,439
Speaker 2: of all the launch codes of nuclear weapons. It could

828
00:43:17,560 --> 00:43:19,760
Speaker 2: just take the form of an AI that gets releases

829
00:43:19,800 --> 00:43:22,680
Speaker 2: of virus that then messes up all over our computers.

830
00:43:22,800 --> 00:43:26,399
Speaker 2: That's something that could easily happen. And until we have

831
00:43:27,000 --> 00:43:29,680
Speaker 2: more of a say by women, by people of color,

832
00:43:29,719 --> 00:43:33,319
Speaker 2: by a broader base of humanity into the operations of

833
00:43:33,360 --> 00:43:37,920
Speaker 2: these AI companies, these tech companies. There's more of a fear,

834
00:43:38,360 --> 00:43:40,520
Speaker 2: we should have, more of a healthy fear of respect

835
00:43:40,880 --> 00:43:45,640
Speaker 2: that something bad could happen when one guy power hungry

836
00:43:45,840 --> 00:43:49,080
Speaker 2: makes a mistake with the development of AI unleashes something

837
00:43:49,120 --> 00:43:52,759
Speaker 2: that they can't control. That very well could happen. Some

838
00:43:52,880 --> 00:43:56,880
Speaker 2: of it's marketing, but it could take some real form

839
00:43:57,080 --> 00:43:59,440
Speaker 2: that we have to be aware of and wary of.

840
00:44:00,719 --> 00:44:03,160
Speaker 1: What are the missing like where are the weaknesses? And

841
00:44:03,200 --> 00:44:06,640
Speaker 1: like having these people, these singular sort of pole like

842
00:44:06,680 --> 00:44:11,160
Speaker 1: you said, power hungry executives able to do too much damage,

843
00:44:11,200 --> 00:44:13,799
Speaker 1: Like what are some safety measures? Like is it the

844
00:44:14,000 --> 00:44:16,279
Speaker 1: is it having a strong board, is it having like

845
00:44:16,520 --> 00:44:20,200
Speaker 1: are there checks and balances? Like I'm so seeing every

846
00:44:20,400 --> 00:44:24,320
Speaker 1: major you know, tech company ceo kind of kissing the

847
00:44:24,400 --> 00:44:27,040
Speaker 1: ring of the imish of the you know, the White

848
00:44:27,040 --> 00:44:30,319
Speaker 1: House and trumpministration right now just kind of kind of

849
00:44:30,680 --> 00:44:34,360
Speaker 1: terrifies me in a way. And you know, as a citizen,

850
00:44:34,400 --> 00:44:36,759
Speaker 1: they say go vote and you know, make sure your

851
00:44:36,800 --> 00:44:38,799
Speaker 1: voice is heard. But when it's the heads of these

852
00:44:38,840 --> 00:44:41,040
Speaker 1: private companies that can do whatever they want, it's like

853
00:44:41,080 --> 00:44:44,719
Speaker 1: who who's going to stop them.

854
00:44:44,880 --> 00:44:47,479
Speaker 3: Yeah, I think that number one.

855
00:44:47,760 --> 00:44:50,760
Speaker 2: I think we have to sort of push our elected

856
00:44:50,800 --> 00:44:58,040
Speaker 2: representatives to put some some uh some uh guidelines and

857
00:44:58,840 --> 00:45:03,880
Speaker 2: laws in place to check the uh, the the unrestrained

858
00:45:04,000 --> 00:45:08,319
Speaker 2: development AI. I don't think that'll inhibit us in terms

859
00:45:08,320 --> 00:45:09,719
Speaker 2: of competition with other countries that.

860
00:45:09,640 --> 00:45:11,360
Speaker 3: Are doing it. I think it should be global.

861
00:45:12,440 --> 00:45:14,279
Speaker 2: But until we do that in the same way we

862
00:45:14,320 --> 00:45:17,480
Speaker 2: regulate nuclear weapons to sort of help prevent its spread.

863
00:45:17,680 --> 00:45:20,080
Speaker 2: Certainly it's still around, but only certain countries have it

864
00:45:20,560 --> 00:45:23,480
Speaker 2: and it's not growing. Look, it maybe could be if

865
00:45:23,680 --> 00:45:26,239
Speaker 2: if those check court in place. The same has to

866
00:45:26,239 --> 00:45:28,719
Speaker 2: happen with AI. If we view it as something that

867
00:45:28,760 --> 00:45:33,680
Speaker 2: could be a potential weapon of mass destruction. I think

868
00:45:33,719 --> 00:45:37,799
Speaker 2: we all also to wake up and realize that this

869
00:45:37,880 --> 00:45:43,200
Speaker 2: really is a major turning point in human history and

870
00:45:43,239 --> 00:45:50,000
Speaker 2: recognize as such and take actions that we can to

871
00:45:50,120 --> 00:45:52,000
Speaker 2: sort of, you know, keep it in some kind of

872
00:45:52,120 --> 00:45:56,960
Speaker 2: check again, working without representatives, starting our own companies picking up.

873
00:45:57,000 --> 00:45:59,600
Speaker 2: If we're at these kinds of tech companies, all those

874
00:45:59,640 --> 00:46:03,320
Speaker 2: kinds of things can work as a check on what's happening.

875
00:46:03,560 --> 00:46:05,920
Speaker 2: But again I have to say we're not We're not

876
00:46:06,120 --> 00:46:08,960
Speaker 2: there yet. We may well be there. We're not there yet.

877
00:46:09,000 --> 00:46:11,000
Speaker 2: We've gone through other AI winters in the past where

878
00:46:11,000 --> 00:46:14,000
Speaker 2: people thought AI was heading someplace incredible and things that

879
00:46:14,120 --> 00:46:16,320
Speaker 2: have stopped and took longer for the things to developed.

880
00:46:16,320 --> 00:46:17,040
Speaker 3: I think we'll see some.

881
00:46:17,080 --> 00:46:19,880
Speaker 2: Very powerful AI agents coming out that are able to

882
00:46:19,880 --> 00:46:23,200
Speaker 2: do video and audio and the ways that are astounding.

883
00:46:23,840 --> 00:46:27,000
Speaker 2: But we have things that are thinking and and interacting

884
00:46:27,040 --> 00:46:30,160
Speaker 2: with us, and the level of human beings anytime and

885
00:46:30,200 --> 00:46:33,120
Speaker 2: making art and level of human beings I don't know,

886
00:46:33,239 --> 00:46:33,759
Speaker 2: but you know this.

887
00:46:33,719 --> 00:46:35,000
Speaker 3: Is something that's a real concern to me.

888
00:46:35,040 --> 00:46:37,920
Speaker 2: I mean, one reason why I wrote this book is is,

889
00:46:37,960 --> 00:46:40,560
Speaker 2: you know, a couple of years ago and I had

890
00:46:40,600 --> 00:46:45,960
Speaker 2: protect Cancer and have my Project Cancer taken out and

891
00:46:46,040 --> 00:46:48,800
Speaker 2: actually ever been one of those MRI machines A very loud,

892
00:46:49,360 --> 00:46:51,680
Speaker 2: very noisy, it's almost like you've got to going crazy.

893
00:46:51,719 --> 00:46:53,920
Speaker 2: You have lots of time to think. And I was

894
00:46:53,960 --> 00:46:57,160
Speaker 2: just thinking about the that the line between the life

895
00:46:57,320 --> 00:47:02,160
Speaker 2: and death and machines and humans and and and uh,

896
00:47:02,440 --> 00:47:05,160
Speaker 2: a lot of these big existential questions, and I realized

897
00:47:05,239 --> 00:47:06,440
Speaker 2: this is something I have to go has to go

898
00:47:06,480 --> 00:47:08,920
Speaker 2: into my book, something that I really I really want

899
00:47:08,960 --> 00:47:12,200
Speaker 2: to sort of write about. Uh, the meaning of life

900
00:47:13,200 --> 00:47:16,680
Speaker 2: as we sort of create artificial life with AI felt

901
00:47:16,680 --> 00:47:18,080
Speaker 2: that this was a moment to do that, and seat

902
00:47:18,120 --> 00:47:20,920
Speaker 2: of came into the realization and sitting there, you know,

903
00:47:21,600 --> 00:47:24,000
Speaker 2: getting ready to get my pro state taken out. And

904
00:47:24,040 --> 00:47:26,120
Speaker 2: I remember around the same time after I get my

905
00:47:26,160 --> 00:47:28,440
Speaker 2: proseate taken out. And by the way, it's a good

906
00:47:28,440 --> 00:47:30,719
Speaker 2: time to tell all your listeners out there that in

907
00:47:30,719 --> 00:47:34,879
Speaker 2: our community, you know, project cancer is ten percent higher

908
00:47:34,880 --> 00:47:37,120
Speaker 2: among people men of color, that we should all get

909
00:47:37,160 --> 00:47:39,360
Speaker 2: checked out. If you have any problems with like you know,

910
00:47:39,480 --> 00:47:40,840
Speaker 2: we don't talk about these things in our community.

911
00:47:40,880 --> 00:47:41,160
Speaker 3: We should.

912
00:47:41,200 --> 00:47:43,200
Speaker 2: You have a problems with the urination in terms of

913
00:47:43,239 --> 00:47:46,080
Speaker 2: like stopping or starting or whatever, go see a doctor.

914
00:47:46,120 --> 00:47:49,319
Speaker 2: Get yourself checked out. It's very important. We tend not

915
00:47:49,320 --> 00:47:51,239
Speaker 2: to want to talk about this stuff, but we've got to.

916
00:47:52,080 --> 00:47:54,000
Speaker 2: It's it's it's it's a it's a money issue too,

917
00:47:54,000 --> 00:47:56,719
Speaker 2: because it costs a lot to sealth health insurance to

918
00:47:56,719 --> 00:47:57,479
Speaker 2: deal with this kind of stuff.

919
00:47:57,520 --> 00:47:58,799
Speaker 3: So get yourself checked out if you can.

920
00:47:59,800 --> 00:48:02,160
Speaker 2: But remember, so I had my projecy taking out, I'm

921
00:48:02,440 --> 00:48:05,759
Speaker 2: you know, sitting on what uh, trying to recover, but

922
00:48:05,800 --> 00:48:08,279
Speaker 2: I was also working on a concert a New Year's

923
00:48:08,320 --> 00:48:11,360
Speaker 2: Eve concert with Cynthia Revo, and so I dragged myself out.

924
00:48:11,200 --> 00:48:14,000
Speaker 3: Of bed, put on a had had to wear a

925
00:48:14,000 --> 00:48:14,560
Speaker 3: cafe that I.

926
00:48:14,520 --> 00:48:17,120
Speaker 2: Went out to this concert concept for Cynthia reveal that

927
00:48:17,120 --> 00:48:20,480
Speaker 2: I was helping to put on. And remember she did

928
00:48:20,480 --> 00:48:23,560
Speaker 2: a cover of of Killing Me Softly, you know, the

929
00:48:23,920 --> 00:48:26,759
Speaker 2: Fuji's Slash Flax song, And then she did a cover

930
00:48:26,840 --> 00:48:30,200
Speaker 2: of Nothing Compares to You that the Prince song that asked,

931
00:48:31,239 --> 00:48:34,680
Speaker 2: and she finished the show with that, and and I

932
00:48:34,719 --> 00:48:37,200
Speaker 2: remember afterwards I was in in the in the green

933
00:48:37,280 --> 00:48:40,120
Speaker 2: room with her and and and people who'd help put

934
00:48:40,160 --> 00:48:42,400
Speaker 2: it on, and I was talking to myself, this is

935
00:48:42,400 --> 00:48:44,360
Speaker 2: what sort of life is about. This is what humans

936
00:48:44,400 --> 00:48:47,400
Speaker 2: can do. We can put on shows like that with

937
00:48:47,560 --> 00:48:50,319
Speaker 2: a kind of glamor and style and artistry that only

938
00:48:50,320 --> 00:48:53,920
Speaker 2: a Cynthia Revo can bring, that a Prince Song can

939
00:48:53,960 --> 00:48:55,799
Speaker 2: bring to the table, and that a Laura Hill can

940
00:48:55,840 --> 00:48:57,960
Speaker 2: bring to Killing Me Softly because she kind of did

941
00:48:57,960 --> 00:49:01,120
Speaker 2: it in the Laura Hill kind of way. And computers

942
00:49:01,160 --> 00:49:03,680
Speaker 2: aren't in the current firm, aren't ever going to get there,

943
00:49:04,120 --> 00:49:06,319
Speaker 2: and we have to protect that it all costs. We

944
00:49:06,400 --> 00:49:09,440
Speaker 2: have to protect the ability of humans to make that

945
00:49:09,560 --> 00:49:11,839
Speaker 2: kind of art to flourish, to have those kinds of

946
00:49:11,880 --> 00:49:15,799
Speaker 2: careers and to come together in a communal kind of way.

947
00:49:16,360 --> 00:49:20,400
Speaker 2: And you know, being operated on, going through cancer, you know,

948
00:49:20,560 --> 00:49:22,560
Speaker 2: being going through an MRI, having to drag myself to

949
00:49:22,640 --> 00:49:25,040
Speaker 2: the concert kind of showed me that that, you know,

950
00:49:25,680 --> 00:49:28,440
Speaker 2: creating the kind of art comes at a cost, and

951
00:49:28,480 --> 00:49:31,920
Speaker 2: that's why it's so worthwhile. That's why you know, an

952
00:49:31,960 --> 00:49:34,839
Speaker 2: AI chat GPT can pump out stuff but doesn't come

953
00:49:34,840 --> 00:49:36,919
Speaker 2: at a cost. They don't have to have the chat

954
00:49:36,920 --> 00:49:39,480
Speaker 2: GPT isn't either have to go through cancer or go

955
00:49:39,560 --> 00:49:42,120
Speaker 2: through the kind of things that Cynthia Revos has to

956
00:49:42,160 --> 00:49:42,759
Speaker 2: go through to get to.

957
00:49:42,760 --> 00:49:43,359
Speaker 3: Where she is.

958
00:49:43,800 --> 00:49:47,239
Speaker 2: We do, and that's why our art is meaningful, and

959
00:49:47,280 --> 00:49:50,400
Speaker 2: that's why I think we have to support it, champion it,

960
00:49:50,760 --> 00:49:54,080
Speaker 2: and and always realize that we create special things as

961
00:49:54,120 --> 00:49:56,800
Speaker 2: humans that computers cannot.

962
00:49:58,040 --> 00:50:00,799
Speaker 1: That's such a lovely note. Unfortunately to end on, because

963
00:50:00,800 --> 00:50:02,839
Speaker 1: I can't believe it's already been an hour, and like, what,

964
00:50:02,880 --> 00:50:06,279
Speaker 1: where's the time. Well, what an honor to finally get

965
00:50:06,320 --> 00:50:10,160
Speaker 1: to beat you only years of hearing about you from Sharon.

966
00:50:10,239 --> 00:50:13,880
Speaker 1: I'm still dying inside. What a gift this book is

967
00:50:13,920 --> 00:50:15,839
Speaker 1: I mean, I don't think AI could have written this book,

968
00:50:15,840 --> 00:50:18,520
Speaker 1: although it is. You mean again, spoilers do we say?

969
00:50:18,640 --> 00:50:20,799
Speaker 1: I don't know. I just knew when I was reading it,

970
00:50:20,840 --> 00:50:23,160
Speaker 1: and I'm like, what is this? Narrator? Who is that?

971
00:50:23,280 --> 00:50:25,879
Speaker 1: It is not up to you like what is it?

972
00:50:25,880 --> 00:50:28,359
Speaker 1: It's very very good. I hope, ba fam you go

973
00:50:28,400 --> 00:50:30,120
Speaker 1: get the book Who Knows You by Heart. Oh my

974
00:50:30,160 --> 00:50:32,080
Speaker 1: little blurr is getting in the way, But don't worry.

975
00:50:32,080 --> 00:50:34,120
Speaker 1: I'll put it. I'll put it in the show notes.

976
00:50:34,160 --> 00:50:37,440
Speaker 1: It's on sale November eleventh. We will put a link

977
00:50:37,760 --> 00:50:41,720
Speaker 1: to where you can buy it in the show notes. C. J. Farley,

978
00:50:41,760 --> 00:50:43,719
Speaker 1: thank you so much for sharing a bit of your

979
00:50:43,760 --> 00:50:47,000
Speaker 1: story and sharing Octavia with Brown Ambition. It's been a

980
00:50:47,000 --> 00:50:47,920
Speaker 1: pleasure having you on.

981
00:50:48,480 --> 00:50:48,719
Speaker 3: Thank you.

982
00:50:48,800 --> 00:50:50,600
Speaker 2: It's great to be here, great to talk to you.

983
00:50:50,719 --> 00:50:52,319
Speaker 2: And I hope people check out my book, Who Knows

984
00:50:52,360 --> 00:50:52,920
Speaker 2: You by Heart?

985
00:50:52,960 --> 00:51:00,000
Speaker 3: Thank you?

986
00:51:00,040 --> 00:51:02,520
Speaker 1: Hey, ba fam, let's be real for a second, and

987
00:51:02,600 --> 00:51:04,680
Speaker 1: y'all know I keep it a book. The job market

988
00:51:04,760 --> 00:51:08,440
Speaker 1: has been brutal, now not brutal trash, especially for women

989
00:51:08,480 --> 00:51:12,000
Speaker 1: of color. Over three hundred thousand of us have disappeared

990
00:51:12,000 --> 00:51:15,160
Speaker 1: from the workforce this year alone. And not by choice,

991
00:51:15,440 --> 00:51:20,600
Speaker 1: but because of layoffs, disappearing DEI programs, and stagnant wages

992
00:51:20,640 --> 00:51:24,080
Speaker 1: that keep cutting us out of opportunity. Our unemployment rate

993
00:51:24,120 --> 00:51:27,400
Speaker 1: has jumped to over seven percent, while our pay gap

994
00:51:27,480 --> 00:51:30,160
Speaker 1: continues to widen. I know all of that sounds dire,

995
00:51:30,239 --> 00:51:32,279
Speaker 1: but here's what I want y'all to know. You do

996
00:51:32,360 --> 00:51:34,600
Speaker 1: not have to wait for the system to save you.

997
00:51:35,080 --> 00:51:38,440
Speaker 1: That's exactly why I created the Mandy money Makers Group

998
00:51:38,520 --> 00:51:41,880
Speaker 1: coaching community. It is a coaching community that is built

999
00:51:41,920 --> 00:51:45,279
Speaker 1: for us by us. Inside the community, we're not just

1000
00:51:45,360 --> 00:51:47,680
Speaker 1: talking about how to negotiate or to how to get

1001
00:51:47,719 --> 00:51:51,080
Speaker 1: the job that you want. It's about finding purpose in

1002
00:51:51,120 --> 00:51:55,640
Speaker 1: your career. It's about finding communities and others, feeling seen,

1003
00:51:56,040 --> 00:51:59,480
Speaker 1: feeling heard, and also having a sounding board and a

1004
00:51:59,520 --> 00:52:03,120
Speaker 1: mirror to reflect your own magic, your own sparkle right

1005
00:52:03,320 --> 00:52:07,000
Speaker 1: back to yourself. In this community, you'll get group coaching

1006
00:52:07,080 --> 00:52:09,319
Speaker 1: led by me, but you also get peer to peer

1007
00:52:09,360 --> 00:52:13,160
Speaker 1: accountability with proven tools and resources that can help you

1008
00:52:13,239 --> 00:52:17,200
Speaker 1: do what we have always done since rise. Even when

1009
00:52:17,239 --> 00:52:20,680
Speaker 1: the odds are stacked against us, despite all the challenges,

1010
00:52:20,840 --> 00:52:24,680
Speaker 1: we will rise. If you're interested in joining the Mandy

1011
00:52:24,680 --> 00:52:28,719
Speaker 1: money Makers Community and having that support to bolster you

1012
00:52:28,800 --> 00:52:31,759
Speaker 1: and help you tap back into your magic so that

1013
00:52:31,800 --> 00:52:34,520
Speaker 1: you can lead your career with intention and heart and

1014
00:52:34,560 --> 00:52:39,319
Speaker 1: your own intuition, trusting that again, please join us. You

1015
00:52:39,360 --> 00:52:42,560
Speaker 1: can find information in the show notes of today's episodes,

1016
00:52:43,040 --> 00:52:47,120
Speaker 1: or go to mandymoney dot com slash community. That's Mandy

1017
00:52:47,360 --> 00:52:51,279
Speaker 1: m A N D I money dot com slash community.

1018
00:52:51,560 --> 00:52:54,880
Speaker 1: I would love to see y'all there. Enrollment is open,

1019
00:52:55,080 --> 00:52:58,840
Speaker 1: so please go check out mandymoney dot com slash community

1020
00:52:58,920 --> 00:52:59,320
Speaker 1: today