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Speaker 1: Looking for the longest time. It's coming for us, all

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Speaker 1: artificial intelligence, the robots taking our jobs. The question, of course,

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Speaker 1: what is it that you and your kids and I

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Speaker 1: and Alex Egano's producing or Sandy Collins can do or

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Speaker 1: are willing to skill up around what we already do

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Speaker 1: to embrace AI and have a future where we're not

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Speaker 1: going I have no idea what I'm going to do

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Speaker 1: with my life. A guy who knows all about these things.

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Speaker 1: The director of AI Strategy and Harry Professor the University

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Speaker 1: of Cincinnati lending their College of Business, Jeff Shaeffer. Welcome

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Speaker 1: back to seven hundred WLW with Sterling. How are you

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Speaker 1: this fine Sunday?

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Speaker 2: I'm good. Thanks for having me back.

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Speaker 3: I appreciate you making time.

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Speaker 1: So what is the first thing someone should look at

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Speaker 1: is they assess their current situation in looking ahead in

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Speaker 1: whatever industry, whatever trade, whatever it is that they do

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Speaker 1: to make a living or have a passion that they

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Speaker 1: should be looking to improve upon to be able to

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Speaker 1: stick around and keep doing something anything and have a future.

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Speaker 2: Well, these are all great questions and questions that I

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Speaker 2: think you know we're pondering in this age of AI.

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Speaker 2: Here right we have six thousand students as an example

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Speaker 2: at the Lennar College of Business, you see, and we're

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Speaker 2: we're talking about these same things. So one of the

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Speaker 2: things that we've done is is kind of put these

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Speaker 2: into i'll call them buckets of competencies. Right, and so

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Speaker 2: you have some basic things about career readiness, professionalism, you know,

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Speaker 2: things that before AI we'd still talk about, right, showing

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Speaker 2: up to work, being prepare, you know, accountability, respect, all

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Speaker 2: all of those things. What's really changed now is the

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Speaker 2: tech side of it, right, tech savviness and trying to

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Speaker 2: be fluent in that, in that, in those tools and

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Speaker 2: in those those language and the other thing that probably

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Speaker 2: doesn't get enough talk. And when we're all talking about technology,

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Speaker 2: are we skilled up in the AI space? With these

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Speaker 2: pools and uh uh, these these new things right that

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Speaker 2: that potentially make us better, faster, smarter, we don't lose

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Speaker 2: the human element. The human element may in fact become

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Speaker 2: more important than the tech element and all this. So

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Speaker 2: when you ask that question, I think the first thing

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Speaker 2: is don't be afraid of it. I think you got

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Speaker 2: to lean into the technology. At Pandora's boxes open. You know,

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Speaker 2: everything is AI and it's been around for a long time.

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Speaker 2: It's like you say, oh, I don't want to use AI. Well,

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Speaker 2: you're you're watching Netflix tonight right that that that has

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Speaker 2: AI in it, So you know everything has AI built

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Speaker 2: into these things. It's just getting more ingrained into the

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Speaker 2: things that we do.

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Speaker 4: Uh.

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Speaker 1: Talking to Jeff Schaeffer, he's the director of AI Strategy,

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Speaker 1: Perry Professor, University of Cincinnati's Lender College of Business with

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Speaker 1: Sterling on the big one. So as we look at

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Speaker 1: the future of jobs and what is coming next, you

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Speaker 1: mentioned about being more tech savvy for someone who's using

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Speaker 1: some tech but not a lot of tech. Talking to friends,

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Speaker 1: I've had other people that are supposedly experts about this.

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Speaker 1: They sort of say, a lot of the tree raids

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Speaker 1: are probably less likely to have a major hit to

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Speaker 1: their future earning possibilities by AI, but there is some

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Speaker 1: AI that can be utilized to help them and what

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Speaker 1: they're doing regardless of that. So this is already sort

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Speaker 1: of getting into every aspect of our lives anyway, whether

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Speaker 1: it's the algorithms for stuff we're streaming, or even your

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Speaker 1: car that gives you that you know, oh you got

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Speaker 1: you're out of your lane, or you're you're too close

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Speaker 1: to somebody. Let's hit that brake for you. I mean,

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Speaker 1: these are all elements that over the last decade have sorely,

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Speaker 1: you know, sort of like bounced into our everyday lives.

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Speaker 3: Correct.

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Speaker 5: Yeah, that's exactly right.

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Speaker 2: Now. If you look, if you want to talk about trades,

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Speaker 2: certainly the we call this the world model, you know,

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Speaker 2: the human elements. Again, AI doesn't have the concept of

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Speaker 2: physics and gravity and things that are in our natural world,

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Speaker 2: so it's a lot harder and people are chasing that problem.

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Speaker 2: So Elon Musk is building you know, millions of robots.

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Speaker 2: We'll get there and there will be people that that

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Speaker 2: are replaced from human tasks, just like in a factory.

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Speaker 2: But that doesn't mean everything is going to get replaced, right,

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Speaker 2: And so when you go back to any of this,

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Speaker 2: I think we got to look at what that is.

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Speaker 2: So from a technical standpoint, let's talk about that.

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Speaker 5: Learning what AI.

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Speaker 2: Is good at and what AI cannot do, at least

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Speaker 2: in the short term, in the midterm, and following that

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Speaker 2: and staying on top of it. We had an alumni

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Speaker 2: in this week and he said something to us that

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Speaker 2: we need to teach the students to learn how to

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Speaker 2: learn faster. Right, They need to be able to pick

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Speaker 2: up these things quicker. They need to be able to

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Speaker 2: adapt to change faster. Then they need to apply those

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Speaker 2: human things that I talked about, like question framing, how

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Speaker 2: do you frame the question? Like that's more important than

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Speaker 2: the technical side. Being technical in this day and age

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Speaker 2: is going to get wiped out before the critical thinking right,

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Speaker 2: interpreting the generation because understanding is this thing that just generated?

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Speaker 2: Is it right? Or is it wrong? How do I

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Speaker 2: know that right? And that comes from experience, business context right,

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Speaker 2: able to put it in a business context. I give

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Speaker 2: you an example, like for automating an invoice flow. You know,

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Speaker 2: every business has an invoice, Every invoice has a date

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Speaker 2: on it, and every invoice has an amount on it.

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Speaker 2: And so that sounds like something that could be easily automated.

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Speaker 2: And then you say, okay, well what about medical invoicing.

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Speaker 2: Now just think about the complexities I just introduced, right

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Speaker 2: with insurance companies and doctors and why it's being invoiced

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Speaker 2: and all those things. And so you have to have

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Speaker 2: a domain expertise, and then you have to be able

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Speaker 2: to apply that domain expertise to AI to get results

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Speaker 2: that are worthwhile because otherwise you're just relying on a generation, right,

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Speaker 2: I mean word generation that's going to give you answers.

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Speaker 2: How do you know it's right? How do you know

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Speaker 2: that workflow is right? How do you know that what

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Speaker 2: it produced is accurate? And so being able to understand

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Speaker 2: the technical side enough, the domain side enough, and then

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Speaker 2: being able to apply I'll call it the personal traits right,

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Speaker 2: the human elements, the critical thinking, the judgment, the ethical reasoning, accountability,

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Speaker 2: all of those things.

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Speaker 1: Is there still a need for people to do coding

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Speaker 1: and learn different programming languages? Because it was less than

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Speaker 1: a decade to fifteen years ago, hanging out with friends

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Speaker 1: and their kids and talking about the future and what

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Speaker 1: they need. We need coders, we need coders, we need coders.

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Speaker 1: A decade later, it seems like a lot of that

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Speaker 1: stuff is sort of fallen too. The wayside, is that

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Speaker 1: the case? Or am I misunderstanding where we are in

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Speaker 1: what's coming?

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Speaker 2: No, there is an absolute risk there of that. I

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Speaker 2: was vibe coding this week, which means that I'm not

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Speaker 2: writing any code myself. I'm giving directions to the computer

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Speaker 2: to generate the code. And it was in a language

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Speaker 2: called Python, and I also was doing some web page

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Speaker 2: stuff in EAHTML, and so these sound technical.

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Speaker 5: Then you say, okay, well, how did I know what.

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Speaker 2: Question to ask? How did I know enough about EACHTML

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Speaker 2: or Python, these languages to ask the question to get

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Speaker 2: what I want? Because I got exactly what I want

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Speaker 2: and I didn't have to write a line of code.

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Speaker 2: So then put that in perspective to students. You may

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Speaker 2: not need to know all the details of how to

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Speaker 2: do it, because I, you know, with code complete and

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Speaker 2: you know generation of code. Now with a GitHub copilot

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Speaker 2: and tools like that, it'll generate the code for me.

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Speaker 2: So I go back to how do I know that

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Speaker 2: code is correct? How do I put that code in production?

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Speaker 2: How do I monitor that code is doing the right thing?

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Speaker 2: How do I make sure that code is compliant legally?

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Speaker 2: If it's making a business decision, what kind of decision

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Speaker 2: is it making. Is it making a high risk decision

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Speaker 2: or is it making a low risk decision? And so,

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Speaker 2: while there's lots of tasks out there, if it's folding

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Speaker 2: my laundry like a you know, think about a robot

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Speaker 2: for an example, that's a low risk task. And if

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Speaker 2: it makes a mistake no problem. But if it's making

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Speaker 2: a medical decision, that could be a high risk, right.

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Speaker 2: And so the short answer, yes, I think humans are

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Speaker 2: critical in this in some form or fashion. But it

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Speaker 2: also acknowledging people make mistakes too, right, And so I

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Speaker 2: think the collaboration of the technology and the people is

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Speaker 2: what's key getting that balance.

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Speaker 1: Director of AI Strategy and Perry Professor, University of Cincinnati's

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Speaker 1: Lendar College of Business, Jeff Schaeffer was sterling on the

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Speaker 1: Big One talking about the future of jobs and how

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Speaker 1: we can better position ourselves moving ahead. These skills related

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Speaker 1: to artificial intelligence, and I don't know you call it

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Speaker 1: the right term here, whether it's future proofing whatever else.

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Speaker 1: I just want to place in the future regard. You know,

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Speaker 1: whether you're driving on seventy four now or two seventy five, whatever,

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Speaker 1: and you're thinking like what's next. You know, you're thirty

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Speaker 1: five years old, You've got a family, You're thinking how

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Speaker 1: to how do I make sure that I'm safe? And

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Speaker 1: you think about this protection issue. You talk about data interpretation,

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Speaker 1: the learning, machine learning stuff and problems solving. How do

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Speaker 1: you know is there if I go to UC or

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Speaker 1: if I go to somewhere else, are there online tools

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Speaker 1: that you can sort of take some type of tutorial

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Speaker 1: or something to sort of get a firm grip on it,

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Speaker 1: because if you're already you know, doing what you do

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Speaker 1: and making your way through the world, sometimes it's hard

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Speaker 1: to make time to do the continuing education that's necessary

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Speaker 1: to stay relevant.

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Speaker 2: Loads of resources out there, right, there's all kinds of

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Speaker 2: YouTube free resources that are available. But if you want

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Speaker 2: to dive deeper into those things, you know, yes, a

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Speaker 2: program like you see, we offer workshops for people who

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Speaker 2: are busy professionals and just want to come learn a

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Speaker 2: little bit. We have certificate programs if you want to

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Speaker 2: dive deeper. We have degree programs, right, if you want

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Speaker 2: to get a degree in this. So, if I'm out

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Speaker 2: there thinking about it, I'm gonna not be scared of it.

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Speaker 2: I'm going to try to embrace it in some sense.

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Speaker 2: Even if you're even if you put yourself in the

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Speaker 2: anti AI category, just recognize it's like saying I'm anti

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Speaker 2: automobile right there everywhere. Yeah, So so you know, you

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Speaker 2: have to figure out how it's going to incorporate into

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Speaker 2: those things from a standpoint of changing careers or advancing

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Speaker 2: or trying to do something else, I'd probably be thinking about, Okay, well,

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Speaker 2: how can I leverage AI to make me better and

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Speaker 2: faster at doing the things I want to do? Think

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Speaker 2: about if you ever had an idea and you want

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Speaker 2: to build a bitusiness, well, gosh, it's probably never been

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Speaker 2: a better time. Think about you know, years ago, how

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Speaker 2: would you get a website? Why you'd have to hire

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Speaker 2: somebody to go build a website for you. Right today,

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Speaker 2: you don't have to do that. There's loads of places

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Speaker 2: to go and you could spin up a website in

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Speaker 2: a few hours and your you know, your website would

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Speaker 2: be ready to go. And that goes for every single

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Speaker 2: task of what you're doing in the business. You could

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Speaker 2: have ideas and you could brainstorm faster, you can create

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Speaker 2: marketing products faster, you can create content faster, and so

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Speaker 2: it really leverages anybody to be able to kind of

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Speaker 2: level the playing field, if you will. Anybody can level

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Speaker 2: up to do what they need to do that in

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Speaker 2: that space. And so yes, go get some training, find

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Speaker 2: out how to do some of these things and see

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Speaker 2: if you can leverage them in whether it's your daily

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Speaker 2: work at your office or whether it's something new you

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Speaker 2: want to try.

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Speaker 3: You know, It's funny.

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Speaker 1: When I was coming up, it was always sterling as

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Speaker 1: it possible you could fix the VCR. So it's not

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Speaker 1: blinking twelve for my mom right And now I get

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Speaker 1: the I don't understand why I can't get from you

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Speaker 1: back to the iHeartRadio app and then back to Netflix.

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Speaker 1: Why can't I just just I'm like, look, it's a

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Speaker 1: different device. It's a whole nother scenario. There's always this

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Speaker 1: gap as we sit here now. If you have kids

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Speaker 1: and they're coming up, and I've seen it with my

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Speaker 1: friends and some of my relatives. Kids, they're born with

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Speaker 1: the device in their hand. They're already aware of what

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Speaker 1: this is, or maybe don't have an idea is to

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Speaker 1: giving it a name, to say it's artificial intelligence. But

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Speaker 1: they're born in, living in it and a washing it.

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Speaker 1: Are they feeling stressed? Should they feel stressed? Or is

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Speaker 1: this just as simple and easy for adaptation as it

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Speaker 1: was maybe for us coming up.

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Speaker 2: I think that's a real challenge for parents. That's not

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Speaker 2: just an AI thing. I think that was a result

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Speaker 2: of you know, our social media generation, as content got

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Speaker 2: shorter and faster and in your face right through reels,

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Speaker 2: tiktoks even now LinkedIn and Instagram and you know, all

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Speaker 2: these channels and it's just kind of flowing, and that

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Speaker 2: makes it hard because there's a lot more noise out there.

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Speaker 2: And then throw the AI component on that weaponized you know,

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Speaker 2: social media and now you have content you don't know

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Speaker 2: whether it's real, you don't know whether it's fake, you

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Speaker 2: don't know. You know, why we're so polarized. All those

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Speaker 2: things could probably come back to those those elements, so

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Speaker 2: that that is a hard challenge to counteract that. I

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Speaker 2: would say, you know, we have to slow down a

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Speaker 2: little bit, you know, work fast with technology, but slow

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Speaker 2: down when it comes to the critical thinking, the discernment,

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Speaker 2: those again, those human elements, because that's that's how we're

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Speaker 2: going to have to implement and apply these these tools.

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Speaker 1: Jeff Safer is the director of Artificial Intelligence Strategy, the

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Speaker 1: Perry and Profer Perry Professor, University of Cincinnati Lindner College

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Speaker 1: of Business. AI should help me be able to slow

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Speaker 1: down and enunciate.

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Speaker 3: That would help.

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Speaker 1: What about the issue of understanding what people are going through?

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Speaker 1: You know, in the eighties to the nineties, huge technological change,

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Speaker 1: industry change. Russ belt issues, people, you know, despondency in

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Speaker 1: all the things. As time moves on, we saw people

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Speaker 1: left behind, multi generations dealing with emotional stress, not necessarily

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Speaker 1: balancing back and taking a couple of decades really to

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Speaker 1: even navigate that in some cases where we sit right

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Speaker 1: now in twenty twenty six, I'm curious about the issue

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Speaker 1: of ethics and understanding what is coming because, as you've

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Speaker 1: described it and many others have, what's ahead of us

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Speaker 1: is supposed to be in what we're in the middle

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Speaker 1: of in the early stages, is supposed to be what

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Speaker 1: the most dramatic change in human history and advancement of technology.

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Speaker 1: And that's a lot to deal with, not just hey,

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Speaker 1: an auto industry or something was outsourced overseas, and now

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Speaker 1: how do I have purpose. There's a lot of layers

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Speaker 1: to that.

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Speaker 2: It's complicated and it's a lot to figure out. There

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Speaker 2: are things that we certainly should be scared of in

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Speaker 2: that realm, but there's so many unknowns.

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Speaker 6: Right.

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Speaker 2: We talked I think on the last time about you know,

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Speaker 2: lower level jobs are getting replaced, right, you know, they're

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Speaker 2: only keeping the higher level, more skilled people. And the

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Speaker 2: problem with that is is where do those highly skilled

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Speaker 2: people come from, right, they come from lower level jobs

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Speaker 2: that are trained up, and so we have to think

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Speaker 2: through that. You know, it's the same thing with coding.

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Speaker 2: You could say, oh, well, don't be a coder because

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Speaker 2: we don't know anybody. You know, we're not going to

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Speaker 2: need the code. Well, where did the computer learn to code?

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Speaker 4: Right?

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Speaker 2: It didn't learn to code on its own. It learned

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Speaker 2: to code from best practices of people doing it for decades,

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Speaker 2: and so we are the engine that feeds that knowledge

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Speaker 2: and we shouldn't forget that, right, And so yeah, where

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Speaker 2: do we fit in the picture? This is akin to

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Speaker 2: back in the day we had automation that replaced muscles.

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Speaker 2: You know. Now we're at automation trying to replace cognition

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Speaker 2: and it's not there, you know, it's it's getting there,

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Speaker 2: and it's moving, and it's moving very fast. It can

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Speaker 2: do things that are either very domain specific that humans

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Speaker 2: can't do, or it can do generalized things not as

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Speaker 2: well as humans can do. And so that's where we

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Speaker 2: are at the moment. Where we go in the future,

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Speaker 2: you know, people are chasing the idea that a computer

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Speaker 2: will be better at us than everything. At some point,

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Speaker 2: agi artificial general intelligence. I don't know if we'll get

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Speaker 2: there anytime soon, but that is the that is the goal, right,

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Speaker 2: and so in that world, then you really have to

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Speaker 2: think about where we are. That's that's a human race

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Speaker 2: sort of question, right. But I think today we have

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Speaker 2: to think about keeping that human in the loop in

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Speaker 2: some form or fashion, and at least for all of

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Speaker 2: those things I outlined right, just to make sure that

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Speaker 2: we are providing that critical thinking, the problem solving, those

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Speaker 2: things that today are very very important in those processes.

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Speaker 1: Before we let you go, and I really appreciate you

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Speaker 1: you making time, and I hope you'll come back. Jeff Schaeffer,

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Speaker 1: by the way, is the director of AI Strategy and

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Speaker 1: Perry Professor, University of Cincinnat add he's Lendar College of Business.

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Speaker 1: In about a minute or so, is someone's driving around

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Speaker 1: right now, They got the kids in the car coming

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Speaker 1: back from brunch church, whatever else it is that they

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Speaker 1: got going on in this beautiful day in the Tri State.

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Speaker 1: If you like cold weather especially, and you're trying to

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Speaker 1: think of Okay, if I want to get a plan

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Speaker 1: together and I want to start working this, you don't

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Speaker 1: want to stress out. You don't want to be overwhelmed.

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Speaker 1: What's the first thing someone should do if they're looking

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Speaker 1: to try to find a way to get to the

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Speaker 1: next level.

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Speaker 2: How about a fun exercise. How about we use AI

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Speaker 2: to help us answer that question. Go out to many

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Speaker 2: many free resources, Google, Gemini, Chat, GPT, Claude. You can

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Speaker 2: use their free tool and use AI and try it

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Speaker 2: and ask those questions and see if you can formulate

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Speaker 2: a plan. Spend ten or fifteen minutes going back and

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Speaker 2: forth prompting and asking questions like that, what could I do?

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Speaker 2: Where could I go? What about researching? And just give

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Speaker 2: it a try and see what it finds for you,

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Speaker 2: because you'll, I think most people who aren't familiar with

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Speaker 2: that will be amazed as what you could get done

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Speaker 2: and what you could do in fifteen minutes or twenty

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Speaker 2: minutes or thirty minutes. And that would be a way

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Speaker 2: to kind of get your feet wet into something like that.

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Speaker 2: And then maybe go explore some training on this, whether

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Speaker 2: it's at UC at the University of Cincinnati or somewhere else.

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Speaker 2: Go out and look for some classes and say, hey,

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Speaker 2: you know what, I'm going to sit through a few

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Speaker 2: hours of this and see what I can learn. See

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Speaker 2: what I can pick up from that and then see

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Speaker 2: what I can apply back at my job.

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Speaker 3: Give me a solution for my future.

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Speaker 1: It's the right question prompted the right way to the

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Speaker 1: right AI could perhaps be the difference in everything. It's

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Speaker 1: good to talk to you. I appreciate you making time.

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Speaker 1: I hope you enjoyed the rest of your weekend. Jeff Schaeffer,

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Speaker 1: he's the director of AI Strategy, Perry Professor, University of

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Speaker 1: Cincinnati Linder College of Business.

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Speaker 3: Thank you for making time. Take care of yourself, Jeff.

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Speaker 2: We'll check ag in so always great talking with you.

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Speaker 3: Take care of.

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Speaker 1: Yourself straight away. The news more Sterling Home of the

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Speaker 1: Red seven hundred WLW. Maybe get a conversation I have

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Speaker 1: with Kevin Carr about that new movie stuff. That'll be

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Speaker 1: a while to hang out for that.

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Speaker 3: And I'm just.

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Speaker 1: Kind of curious, have you embraced the cold? Have you

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Speaker 1: become acclimated to the cold. We're expecting more snow Tuesday.

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Speaker 1: I know a lot of people in a lot of

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Speaker 1: neighborhoods right now still dealing with snow issues in and

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Speaker 1: around their neighborhood streets and getting out of their driveway

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Speaker 1: is plows that push snow up and piled up, and

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Speaker 1: that continues to be an issue. With the cold. We

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Speaker 1: may see above freezing temperatures. What maybe a day or

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Speaker 1: two this week and then next weekend, maybe if we're lucky.

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Speaker 1: I'm hopeful. I'm always an optimist. Five point three seven

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Speaker 1: four nine seven eight hundred the Big One. Tons and

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Speaker 1: tons of people out and about today. I mean, you

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Speaker 1: can't get locked down and be in the house and

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Speaker 1: not go out. I mean, we've got to get stuff,

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Speaker 1: We've got to do things. I know there's a fitness

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Speaker 1: gym kind of place across the road here. Earlier today

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Speaker 1: it was just packed. So people are trying to take

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Speaker 1: care of themselves, trying to make sure they're still getting

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Speaker 1: their exercise in and work that cardio or whatever else.

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Speaker 1: It's not as easy to get out and about and

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Speaker 1: run if that's what you do in this time of

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Speaker 1: year normally. Obviously, my poor dog is still navigating the

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Speaker 1: paths that I carved out in the snow in the yard.

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Speaker 1: He's fifteen pounds of crazy love and stuff. But he

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Speaker 1: was overwhelmed. He walks out of here and he's looking

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Speaker 1: up and he's like the wall of snow is bigger

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Speaker 1: than me, and that's still an issue, and desperately. A

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Speaker 1: tired dog is a good dog from my experience, and

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Speaker 1: he's not getting the exercise he needs, so there may

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Speaker 1: be more shoveling to be doing in and around the yard.

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Speaker 1: To see how that goes. Five point three seven four

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Speaker 1: nine seven eight hundred, the big one. I'm wondering how

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Speaker 1: acclimated you've become. This is one of those things where

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Speaker 1: I've seen it. I've noticed it people coming in out

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Speaker 1: of their houses wearing smaller jackets.

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Speaker 3: I mean, it's just one of those.

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Speaker 1: Thanks Kevin Carr will have a conversation with a little

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Speaker 1: bit later. He wears shorts all year round. I don't

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Speaker 1: know if you're one of those people or not. Everywhere

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Speaker 1: I've ever worked, in any group of friends or circles

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Speaker 1: that I have had, I've noticed that there is at

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Speaker 1: least one par that I know that is all about

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Speaker 1: the shorts all year round. And it's not even always

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Speaker 1: like big oversized people. Sometimes you know there's somebody a

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Speaker 1: little big boned or hefty, whatever you want to call it, larger. Uh,

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Speaker 1: maybe they sort of embrace that. But I've seen it

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Speaker 1: with others and I know there's a difference between coming

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Speaker 1: and going out of the gym or otherwise and doing that.

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Speaker 1: But it's a It's an interesting thing, is we we

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Speaker 1: sort of get to this. I know it's winter, i

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Speaker 1: know it's cold, it's pretty basic stuff. But the acclamation

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Speaker 1: situation is what is weird. And I've never really believed

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Speaker 1: that that would be the case. And I know, being

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Speaker 1: up north in the Minnesota area and the winter in

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Speaker 1: the past, it was one of those after two or

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Speaker 1: three four days, I'd seen everybody in and around there

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Speaker 1: seemingly with the lighter jackets and everything else, and I

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Speaker 1: had the big nook of the Northport parka because I'm

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Speaker 1: coming from here and you know, we're not you to

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Speaker 1: that zero issue under you know, below zero kind of scenario,

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Speaker 1: and people will embrace it and feel it and live

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Speaker 1: it and learn it and get through it. And I

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Speaker 1: guess that's just a matter of keeping it going. But

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Speaker 1: I do know some people that just don't go out.

434
00:21:13,640 --> 00:21:16,040
Speaker 1: They'll just have everything delivered if they can or otherwise.

435
00:21:16,240 --> 00:21:20,280
Speaker 1: And who I really feel bad for is those who

436
00:21:20,320 --> 00:21:26,000
Speaker 1: deliver the mail. It is astounding so many people of

437
00:21:26,440 --> 00:21:29,359
Speaker 1: you know, you don't necessarily shovel a lot of sidewalks.

438
00:21:29,560 --> 00:21:32,080
Speaker 1: I see a lot of driveways. Some sidewalks have been

439
00:21:32,119 --> 00:21:35,119
Speaker 1: cleaned and post to workers, male men, male women. I

440
00:21:35,119 --> 00:21:37,600
Speaker 1: don't know what the proper term is, letter carriers. I

441
00:21:37,600 --> 00:21:41,200
Speaker 1: think that's the right thing. Alex again. Yeah, in that circumstance,

442
00:21:41,240 --> 00:21:44,040
Speaker 1: trying to get get around or whatever else, I mean,

443
00:21:44,200 --> 00:21:46,760
Speaker 1: they got to sort of trudge through it. And I

444
00:21:46,800 --> 00:21:49,880
Speaker 1: saw my male guy the other day and I was like, hey, man,

445
00:21:49,880 --> 00:21:51,000
Speaker 1: he was driving around on his truck.

446
00:21:51,040 --> 00:21:52,639
Speaker 3: He's like, I'll get to you eventually. I'm like, no,

447
00:21:52,760 --> 00:21:53,240
Speaker 3: rush man.

448
00:21:53,280 --> 00:21:56,240
Speaker 1: I tried to clear it for it, and even between

449
00:21:56,280 --> 00:22:00,000
Speaker 1: the houses, because otherwise it's driveway street or driveway side

450
00:22:00,200 --> 00:22:03,200
Speaker 1: walk and then back rather than cutting through. It makes

451
00:22:03,240 --> 00:22:05,960
Speaker 1: the day longer. So I received a bundle of mail

452
00:22:06,040 --> 00:22:08,679
Speaker 1: that probably was three or four days worth of it,

453
00:22:08,720 --> 00:22:11,320
Speaker 1: which is fine. Half of it was junk. They filtered

454
00:22:11,320 --> 00:22:13,560
Speaker 1: out some of it and some was you know, sort

455
00:22:13,600 --> 00:22:16,200
Speaker 1: of important. But I mean, acclamation, they have no choice.

456
00:22:16,680 --> 00:22:19,960
Speaker 1: People out there moving snow, law enforcement out there doing

457
00:22:20,000 --> 00:22:22,000
Speaker 1: their thing. I've seen a number of people pulled over

458
00:22:22,840 --> 00:22:25,000
Speaker 1: last night, even on the way home after the show.

459
00:22:25,400 --> 00:22:27,800
Speaker 1: And it's like ten thirty eleven o'clock on motoring on

460
00:22:28,240 --> 00:22:30,520
Speaker 1: and I see two people pulled over. I don't know

461
00:22:30,560 --> 00:22:33,960
Speaker 1: what the circumstance. It didn't look like a vehicle problem

462
00:22:34,480 --> 00:22:36,840
Speaker 1: as much as it was maybe some type of infraction

463
00:22:37,000 --> 00:22:41,040
Speaker 1: or whatever else. I mean, just imagine how if you're

464
00:22:41,080 --> 00:22:43,720
Speaker 1: a cop, how motivated you're going to be to get

465
00:22:43,720 --> 00:22:46,320
Speaker 1: out of your car in single digits a five degree

466
00:22:46,400 --> 00:22:49,479
Speaker 1: ten degree weather to go give somebody a ticket. If

467
00:22:49,520 --> 00:22:51,680
Speaker 1: I got to get out, I mean, I listen, I'm

468
00:22:51,680 --> 00:22:54,679
Speaker 1: no cop. I have friends who were police. I just

469
00:22:54,720 --> 00:22:57,280
Speaker 1: want to say this. If I have to get out

470
00:22:57,320 --> 00:22:59,880
Speaker 1: of my car and it's ten degrees or five degrees,

471
00:23:00,200 --> 00:23:02,800
Speaker 1: and it's not like a major situation right now, I mean,

472
00:23:02,800 --> 00:23:05,520
Speaker 1: we're toasty and warm, it's twenty one, it's like summertime.

473
00:23:05,560 --> 00:23:07,240
Speaker 1: All of a sudden it is almost shorts weather. I

474
00:23:07,240 --> 00:23:10,199
Speaker 1: can feel it. It feels almost like Opening Day. Maybe a

475
00:23:10,200 --> 00:23:14,159
Speaker 1: slight exaggeration there, but in that circumstance, I think I'm

476
00:23:14,200 --> 00:23:16,800
Speaker 1: writing every ticket I could possibly write for someone if

477
00:23:16,840 --> 00:23:18,320
Speaker 1: I've got to get out of my car and it's

478
00:23:18,359 --> 00:23:21,840
Speaker 1: ten fifteen degrees or less. I mean, because I'm gonna

479
00:23:21,880 --> 00:23:24,919
Speaker 1: be irritable, I'm gonna be frustrated. I'm gonna be cold,

480
00:23:25,200 --> 00:23:27,480
Speaker 1: my feet might be getting wet from the slop that's

481
00:23:27,480 --> 00:23:29,400
Speaker 1: on the ground or the side of the road, depending

482
00:23:29,440 --> 00:23:33,800
Speaker 1: on the circumstance. And I'm gonna be writing for everything

483
00:23:33,840 --> 00:23:34,720
Speaker 1: I possibly could.

484
00:23:35,000 --> 00:23:36,720
Speaker 3: And you know, it's like I'm going to give you

485
00:23:36,720 --> 00:23:37,080
Speaker 3: a warning.

486
00:23:37,280 --> 00:23:39,960
Speaker 1: There'd be no warnings if I was the cop who

487
00:23:39,960 --> 00:23:44,760
Speaker 1: pulled you over in the miserable, evil, bitter, frozen tundra.

488
00:23:45,000 --> 00:23:47,680
Speaker 1: Kind of cold to the dry state these days. Although

489
00:23:47,960 --> 00:23:51,840
Speaker 1: today we are going to be around twenty two, which

490
00:23:51,920 --> 00:23:55,720
Speaker 1: is a nice heat wave, twenty nine tomorrow they say

491
00:23:55,760 --> 00:23:58,120
Speaker 1: thirty one Tuesday. That's a downgrade. I thought I saw

492
00:23:58,200 --> 00:24:01,840
Speaker 1: thirty two, so I'm not even sure or when we

493
00:24:01,880 --> 00:24:05,360
Speaker 1: will see thirty two degrees. How acclimated are you? Are

494
00:24:05,359 --> 00:24:07,639
Speaker 1: you out about? Are you having fun? Is it still

495
00:24:07,720 --> 00:24:10,199
Speaker 1: sledding time in and around Eaton Park? I know I

496
00:24:10,240 --> 00:24:12,640
Speaker 1: saw some people that were having some fun there when

497
00:24:12,680 --> 00:24:14,720
Speaker 1: I was driving around, which is kind of nice. I

498
00:24:14,800 --> 00:24:16,359
Speaker 1: used to live right around the corner and used to

499
00:24:16,359 --> 00:24:18,760
Speaker 1: do some sledding there myself and always found that one

500
00:24:19,359 --> 00:24:21,680
Speaker 1: part there on the low side of where Mere Lake is,

501
00:24:21,720 --> 00:24:24,400
Speaker 1: you don't want to end up out in the road,

502
00:24:24,480 --> 00:24:25,959
Speaker 1: So that's one of those things you always have to to,

503
00:24:26,000 --> 00:24:28,160
Speaker 1: you know, worry about five one, three, seven, four, nine, seven,

504
00:24:28,600 --> 00:24:30,760
Speaker 1: eight hundred, the big one, your chance to get interactive,

505
00:24:30,760 --> 00:24:34,840
Speaker 1: wondering about acclamation situation and how you're getting with it.

506
00:24:35,280 --> 00:24:38,280
Speaker 1: Other stuff to get to as well, in relation to

507
00:24:38,359 --> 00:24:40,960
Speaker 1: a whole bunch of stuff. And I've had a bunch

508
00:24:41,040 --> 00:24:43,320
Speaker 1: of people opinion me saying they want me to talk

509
00:24:43,359 --> 00:24:45,080
Speaker 1: about the Epstein files again.

510
00:24:47,080 --> 00:24:48,240
Speaker 3: I'll lay that out there.

511
00:24:48,960 --> 00:24:50,679
Speaker 1: I mean, I don't want to say it's old news,

512
00:24:50,880 --> 00:24:54,919
Speaker 1: but I mean there's three million documents. They missed the deadline,

513
00:24:55,600 --> 00:24:59,119
Speaker 1: and you can certainly sound off. I'm just wondering, are

514
00:24:59,160 --> 00:25:03,840
Speaker 1: you satisfied because so many people have been like Epstein files,

515
00:25:03,840 --> 00:25:07,159
Speaker 1: Epstein files, bring them out, and Blanche Wo's the Deputy

516
00:25:07,200 --> 00:25:10,680
Speaker 1: Attorney General, has stepped up to defend how they've released them.

517
00:25:11,359 --> 00:25:14,720
Speaker 1: A lot of people who have been found themselves. I

518
00:25:14,760 --> 00:25:19,040
Speaker 1: guess the docks in some way the victims, which is

519
00:25:19,359 --> 00:25:24,600
Speaker 1: just mind blowing to me that you know, you don't

520
00:25:24,640 --> 00:25:29,240
Speaker 1: protect the victim's identity in the midst of this document

521
00:25:29,600 --> 00:25:35,040
Speaker 1: sharing or dump. And uh, you know there there have

522
00:25:35,040 --> 00:25:38,199
Speaker 1: been videos and all kinds of other things, and a

523
00:25:38,240 --> 00:25:41,080
Speaker 1: lot of other redactions. A lot of people's names and

524
00:25:41,080 --> 00:25:43,920
Speaker 1: stuff that sort of have gone along with what has

525
00:25:43,960 --> 00:25:48,080
Speaker 1: been released of men that high powerful men you know

526
00:25:48,359 --> 00:25:51,760
Speaker 1: or thought to have either been I yes, purchasers of

527
00:25:52,760 --> 00:25:55,480
Speaker 1: quality time whatever you want to call it in the

528
00:25:55,520 --> 00:25:59,160
Speaker 1: sex trade with these young girls is what they were

529
00:25:59,240 --> 00:26:03,359
Speaker 1: underage mind her children effectively and then maybe some over

530
00:26:03,359 --> 00:26:05,880
Speaker 1: the age of eighteen or whatever else. But I mean,

531
00:26:06,160 --> 00:26:09,600
Speaker 1: I can't imagine a situation. The number one thing that

532
00:26:09,680 --> 00:26:12,080
Speaker 1: they should be, at least in my opinion, worried about

533
00:26:12,359 --> 00:26:15,760
Speaker 1: is protecting the victim. So if you're going to release

534
00:26:15,840 --> 00:26:18,800
Speaker 1: all this information by order or otherwise, how hard would

535
00:26:18,840 --> 00:26:21,640
Speaker 1: it be, since all this stuff has been digitized, to

536
00:26:21,680 --> 00:26:23,679
Speaker 1: just do a search for the names that you know

537
00:26:23,800 --> 00:26:26,280
Speaker 1: are in there, because that would be easily found, one

538
00:26:26,280 --> 00:26:28,800
Speaker 1: would think, with the help of maybe artificial intelligence, even

539
00:26:28,840 --> 00:26:31,120
Speaker 1: if you don't, just do a basic search and then

540
00:26:31,119 --> 00:26:37,240
Speaker 1: you go through and you redact the accuser. Those that

541
00:26:37,440 --> 00:26:39,159
Speaker 1: have been on the other side of this that was

542
00:26:39,200 --> 00:26:43,040
Speaker 1: a complaint that we're outing the people who were violating them,

543
00:26:43,080 --> 00:26:46,000
Speaker 1: whether it's Maxwell who's in a Kushi More country club

544
00:26:46,040 --> 00:26:49,560
Speaker 1: prison now, or whether it's Epstein, who of course allegedly

545
00:26:49,800 --> 00:26:53,320
Speaker 1: hung himself while he was in detention in sitting there

546
00:26:53,359 --> 00:26:56,960
Speaker 1: and Maxwell, of course with some more time, and apparently

547
00:26:57,040 --> 00:27:00,400
Speaker 1: the president supposedly has been completely exonerated. Now I think

548
00:27:00,600 --> 00:27:03,879
Speaker 1: I've heard that out there and seen that published, so

549
00:27:03,960 --> 00:27:07,080
Speaker 1: perhaps that's the case. I want to know how fixated

550
00:27:07,160 --> 00:27:10,600
Speaker 1: and concerned and worried and engaged and involved you have

551
00:27:10,760 --> 00:27:14,800
Speaker 1: been in this Epstein thing. I still get inundated with it,

552
00:27:14,840 --> 00:27:17,600
Speaker 1: and it's not communicated by bots, which is one of

553
00:27:17,640 --> 00:27:19,439
Speaker 1: those things you always have to sort of look at

554
00:27:19,480 --> 00:27:21,879
Speaker 1: when you start getting people asking you about stuff. But

555
00:27:21,920 --> 00:27:24,439
Speaker 1: I'm wondering if you're concerned about the victims, if you

556
00:27:24,480 --> 00:27:26,960
Speaker 1: were one of those people who decided that this was

557
00:27:27,000 --> 00:27:29,880
Speaker 1: a lynch pin issue for the last election to bring

558
00:27:29,920 --> 00:27:32,800
Speaker 1: Trump back to office, are you happy with the way

559
00:27:32,840 --> 00:27:35,720
Speaker 1: these files have been released, the way this has been

560
00:27:36,320 --> 00:27:39,640
Speaker 1: moving through the system in processing. Do you think there

561
00:27:39,680 --> 00:27:42,760
Speaker 1: will be more I have so many questions, will be

562
00:27:43,240 --> 00:27:46,040
Speaker 1: more people held accountable? Or is this just a lot

563
00:27:46,080 --> 00:27:47,840
Speaker 1: of nothing. There'll be some docs in and the people

564
00:27:47,840 --> 00:27:49,199
Speaker 1: will go, well, you don't have any proof of me

565
00:27:49,240 --> 00:27:52,200
Speaker 1: doing anything else and carry on, Because that really has

566
00:27:52,240 --> 00:27:54,000
Speaker 1: been some of what has been talked about, and how

567
00:27:54,000 --> 00:27:56,919
Speaker 1: it's moved through five point three seven four nine seven thousand,

568
00:27:57,240 --> 00:27:59,880
Speaker 1: eight hundred to the big one talk back the iHeartRadio.

569
00:28:00,200 --> 00:28:02,520
Speaker 1: You can click on the microphone there. I'm also on

570
00:28:03,000 --> 00:28:04,919
Speaker 1: x what used to be Twitter for those who are

571
00:28:04,920 --> 00:28:08,520
Speaker 1: still living in the past, at Stirling Radio. You can

572
00:28:08,520 --> 00:28:13,320
Speaker 1: get interactive that way as well. It's it's still hard

573
00:28:13,359 --> 00:28:16,159
Speaker 1: for me to imagine, and you hear it on a local,

574
00:28:16,240 --> 00:28:19,880
Speaker 1: regional basis regularly in the news. I mean they've had

575
00:28:19,880 --> 00:28:22,719
Speaker 1: the dateline kind of sting or whatever else it is

576
00:28:23,040 --> 00:28:25,320
Speaker 1: where they have those people show up in you know,

577
00:28:25,400 --> 00:28:28,800
Speaker 1: suburban homes where they've been trolled by law enforcement or

578
00:28:28,840 --> 00:28:32,919
Speaker 1: some other groups of people acting is underage type people

579
00:28:33,440 --> 00:28:38,240
Speaker 1: out there or somebody looking to sell their company or otherwise,

580
00:28:38,680 --> 00:28:40,440
Speaker 1: and then they would bust them, like to catch a

581
00:28:40,440 --> 00:28:43,760
Speaker 1: predator kind of scenario. Now, those people, because they are

582
00:28:43,800 --> 00:28:46,280
Speaker 1: not of means, because they are not somebody, they're just

583
00:28:46,360 --> 00:28:49,120
Speaker 1: regular everyday people. They're the ones who lose their career.

584
00:28:49,320 --> 00:28:52,040
Speaker 1: They're the ones who lose their home life situation. They're

585
00:28:52,040 --> 00:28:54,239
Speaker 1: the ones who get locked up. They're the ones that

586
00:28:54,280 --> 00:28:56,600
Speaker 1: are ostracized. They're the ones that you get the card

587
00:28:56,600 --> 00:28:59,760
Speaker 1: from the Sheriff's office, it says sexual predator in the neighborhood,

588
00:29:00,040 --> 00:29:03,720
Speaker 1: so you can identify and as you're driving around to go, yeah, okay,

589
00:29:03,840 --> 00:29:06,360
Speaker 1: keep the kid away from that guy. Kind of scenario.

590
00:29:06,720 --> 00:29:10,080
Speaker 1: But the level that we're talking about where it engages

591
00:29:10,160 --> 00:29:13,200
Speaker 1: the President of the United States and the federal government

592
00:29:13,240 --> 00:29:17,240
Speaker 1: at the highest levels. These individuals around there, I mean,

593
00:29:17,280 --> 00:29:20,160
Speaker 1: where are they held accountable? Are they going to go

594
00:29:20,200 --> 00:29:22,920
Speaker 1: in front of Congress and be asked questions?

595
00:29:24,360 --> 00:29:26,720
Speaker 3: Highly doubtful. What do you think is going to happen

596
00:29:26,760 --> 00:29:27,600
Speaker 3: as a result of this?

597
00:29:28,840 --> 00:29:33,440
Speaker 1: I mean really, I mean it almost is seemingly with

598
00:29:33,520 --> 00:29:37,400
Speaker 1: the exposure of some of these women's names and we

599
00:29:37,520 --> 00:29:40,560
Speaker 1: know what they've dealt with and including suicide and other stuff,

600
00:29:43,080 --> 00:29:45,480
Speaker 1: I just kind of look at it and go, it's

601
00:29:45,520 --> 00:29:48,320
Speaker 1: almost like a distraction. Look over here, pay no attention

602
00:29:48,440 --> 00:29:51,160
Speaker 1: to other stuff that's going on. Nothing's going to really

603
00:29:51,160 --> 00:29:55,920
Speaker 1: happen with it. Probably maybe government's partially shut down. They

604
00:29:55,920 --> 00:29:57,600
Speaker 1: have other stuff to worry about, right, They got to

605
00:29:57,600 --> 00:30:00,160
Speaker 1: get the government fully open again. They got to worry

606
00:30:00,200 --> 00:30:04,959
Speaker 1: about other things. This seems six million pieces of paper,

607
00:30:05,520 --> 00:30:08,920
Speaker 1: that's a lot to review, thousands and thousands of videos,

608
00:30:09,960 --> 00:30:15,200
Speaker 1: tens of thousands of images, and that's what they were

609
00:30:15,240 --> 00:30:18,360
Speaker 1: required to go through. That's what they were required to release,

610
00:30:18,760 --> 00:30:22,040
Speaker 1: and they still held back apparently for ongoing or pending

611
00:30:22,080 --> 00:30:26,320
Speaker 1: investigation whatever that means. It's hard to say. It's a

612
00:30:26,440 --> 00:30:29,520
Speaker 1: vague kind of description or whatever else, and you kind

613
00:30:29,520 --> 00:30:32,560
Speaker 1: of look at it and go okay. So now that

614
00:30:32,600 --> 00:30:37,840
Speaker 1: these files are out there, who benefits not the exposed

615
00:30:37,920 --> 00:30:43,480
Speaker 1: victim or the accuser. Maybe not the exposed person who

616
00:30:43,520 --> 00:30:46,760
Speaker 1: was in the list, who was there socially somehow entertaining

617
00:30:46,800 --> 00:30:49,880
Speaker 1: some type of whatever relation to ship with Epstein and

618
00:30:50,080 --> 00:30:52,840
Speaker 1: hanging out of these supposed parties or whatever they happen

619
00:30:52,960 --> 00:30:56,880
Speaker 1: to be. But I don't see the upside to any

620
00:30:56,920 --> 00:31:00,000
Speaker 1: of this at this point. I mean, are you satisfied?

621
00:31:00,240 --> 00:31:02,320
Speaker 1: Is this an issue of all the issues you worry

622
00:31:02,320 --> 00:31:04,080
Speaker 1: about it? I mean, you worry about the economy, You

623
00:31:04,120 --> 00:31:08,400
Speaker 1: worry about national security, right, you worry Maybe in national

624
00:31:08,480 --> 00:31:11,560
Speaker 1: security can be border issues, can be you know, illegal

625
00:31:12,000 --> 00:31:15,520
Speaker 1: immigration issues. It can be an issue of all kinds

626
00:31:15,520 --> 00:31:19,200
Speaker 1: of criminal stuff that goes on. Where does this fall

627
00:31:19,240 --> 00:31:21,800
Speaker 1: if five one, three, seven, four, nine, seven, eight hundred,

628
00:31:21,800 --> 00:31:24,719
Speaker 1: the Big One Quick Break, Sterling hanging out Nation Station

629
00:31:24,880 --> 00:31:30,200
Speaker 1: seven hundred. Wow, Remember the album. I mean, you can

630
00:31:30,240 --> 00:31:33,720
Speaker 1: still stream an album in its entirety at the iHeartRadio app.

631
00:31:33,800 --> 00:31:36,360
Speaker 1: You can go buy some my you know albums on

632
00:31:36,480 --> 00:31:38,840
Speaker 1: vinyl or CD if you care to, I mean vinyls

633
00:31:38,880 --> 00:31:41,320
Speaker 1: come back. Jack White even bought a record plan if

634
00:31:41,320 --> 00:31:43,840
Speaker 1: I'm not mistaken, so he's pressing those and if you

635
00:31:43,840 --> 00:31:45,640
Speaker 1: don't know who he is from the White Stripes, of course,

636
00:31:45,680 --> 00:31:48,360
Speaker 1: he's been doing it for quite a while now and

637
00:31:48,600 --> 00:31:50,920
Speaker 1: thinking about the best albums of all time that you

638
00:31:51,080 --> 00:31:52,560
Speaker 1: I mean, you're having a little fun. You have put

639
00:31:52,560 --> 00:31:53,840
Speaker 1: it on and you're like, you know what, I don't

640
00:31:53,880 --> 00:31:56,240
Speaker 1: have to hit next. I don't have to skip a track,

641
00:31:56,600 --> 00:31:58,760
Speaker 1: you don't have to go Okay, here's three good songs

642
00:31:58,760 --> 00:32:00,840
Speaker 1: on an album with ten songs and the other seven

643
00:32:00,920 --> 00:32:03,280
Speaker 1: or a bunch of just filler or whatever else that

644
00:32:03,320 --> 00:32:06,560
Speaker 1: goes along with that. I still think the ACDC Back

645
00:32:06,560 --> 00:32:08,520
Speaker 1: in Black album is probably one of the best of

646
00:32:08,560 --> 00:32:10,959
Speaker 1: all time front to back that there's ever been. I

647
00:32:11,000 --> 00:32:13,400
Speaker 1: love London Calling from the Clash. I know these are

648
00:32:13,400 --> 00:32:16,240
Speaker 1: some older records or whatever else that go along with that.

649
00:32:16,920 --> 00:32:17,320
Speaker 3: What is it?

650
00:32:17,360 --> 00:32:20,640
Speaker 1: A good Kid, Mad City Kendrick Lamar that's more contemporary

651
00:32:20,680 --> 00:32:22,520
Speaker 1: and stuff that's sort of god gone along with that

652
00:32:22,640 --> 00:32:26,000
Speaker 1: as well. Five one, three, seven, four, nine, seven, eight hundred,

653
00:32:26,040 --> 00:32:28,080
Speaker 1: the Big One. I'm kind of wondering what it is

654
00:32:28,120 --> 00:32:30,840
Speaker 1: that sort of goes along with that concept of is

655
00:32:31,760 --> 00:32:34,680
Speaker 1: full album front to back your favorite when you put

656
00:32:34,680 --> 00:32:37,080
Speaker 1: that playlist in you go to the gym, and it

657
00:32:37,320 --> 00:32:37,640
Speaker 1: could be.

658
00:32:37,760 --> 00:32:38,760
Speaker 3: I mean you sometimes you have a.

659
00:32:38,680 --> 00:32:41,880
Speaker 1: Playlist that's a mixture of stuff, your hits for whatever

660
00:32:41,960 --> 00:32:42,280
Speaker 1: it is.

661
00:32:42,560 --> 00:32:44,320
Speaker 3: But I still like Jane's addiction.

662
00:32:44,680 --> 00:32:48,880
Speaker 1: Nothing shocking front to back an incredible album that sort

663
00:32:48,880 --> 00:32:52,520
Speaker 1: of goes along with that soul whole concept of that too.

664
00:32:53,280 --> 00:32:57,480
Speaker 1: Johnny Cash, who a whole generation and now maybe into

665
00:32:57,520 --> 00:33:01,600
Speaker 1: the second generation of people who have become fans after

666
00:33:01,640 --> 00:33:04,120
Speaker 1: he had started forever and ever ago. He's even been

667
00:33:04,160 --> 00:33:06,960
Speaker 1: gone for quite a while for that matter. But those

668
00:33:07,000 --> 00:33:11,440
Speaker 1: American recordings he did going back a good number of

669
00:33:11,520 --> 00:33:16,880
Speaker 1: years now, uh, you know, covering Sound Garden, doing a

670
00:33:17,000 --> 00:33:19,160
Speaker 1: ton of other records that sort of go along with that,

671
00:33:20,000 --> 00:33:22,560
Speaker 1: and working with Rick Rubin sort of brought a lot

672
00:33:22,560 --> 00:33:25,520
Speaker 1: of other people into the fold to see exactly what

673
00:33:25,600 --> 00:33:28,440
Speaker 1: he was about. And that live record of his that

674
00:33:28,520 --> 00:33:31,320
Speaker 1: he did at fulsome Prison had me as a kid

675
00:33:31,520 --> 00:33:34,040
Speaker 1: I'll tell you. I thought he had really been locked

676
00:33:34,160 --> 00:33:38,480
Speaker 1: up and done a stretch someplace for something, because it

677
00:33:38,640 --> 00:33:40,440
Speaker 1: just seemed like that was the case. And then my

678
00:33:40,520 --> 00:33:42,440
Speaker 1: mom and then my uncle too were like, no, no,

679
00:33:42,560 --> 00:33:44,880
Speaker 1: he was never locked up. He just understand that's why

680
00:33:44,880 --> 00:33:47,160
Speaker 1: he where's black. And then of course they were like, here,

681
00:33:47,200 --> 00:33:49,440
Speaker 1: listen to this, and I disappeared into my room and

682
00:33:49,480 --> 00:33:51,720
Speaker 1: I put the earbuds in or the headphones on and

683
00:33:51,840 --> 00:33:55,200
Speaker 1: was like, okay, I got it, and now I understand

684
00:33:55,360 --> 00:33:57,960
Speaker 1: five one, three, seven, four, nine, seven, eight hundred to

685
00:33:58,000 --> 00:33:59,680
Speaker 1: a big one having a little fun on a Sunday

686
00:33:59,680 --> 00:34:02,360
Speaker 1: after noon, Sterling, Let's get to college. He'll job be first.

687
00:34:02,600 --> 00:34:04,520
Speaker 1: We got a mic and a'brian and a JT and

688
00:34:04,560 --> 00:34:07,040
Speaker 1: love to hear from you as well. Albums front to

689
00:34:07,240 --> 00:34:10,600
Speaker 1: back that you don't have to hit skip, you don't

690
00:34:10,640 --> 00:34:12,799
Speaker 1: have to you go. Oh, I can't believe they put

691
00:34:12,960 --> 00:34:15,319
Speaker 1: I paid. However, that was the thing. Is a kid

692
00:34:15,320 --> 00:34:17,600
Speaker 1: that used to drive you nuts. I'd ride my bike

693
00:34:17,640 --> 00:34:20,680
Speaker 1: down and dating to second time around or my buddies

694
00:34:20,719 --> 00:34:24,319
Speaker 1: and I would drive down to everybody's records here and

695
00:34:24,400 --> 00:34:26,600
Speaker 1: you'd go through and you'd look for you stuff or

696
00:34:26,600 --> 00:34:28,520
Speaker 1: new and sometimes you didn't know. It's like, well, this

697
00:34:28,600 --> 00:34:30,680
Speaker 1: guy wrote this song and played on this and that's

698
00:34:30,680 --> 00:34:32,640
Speaker 1: at least how I went from one thing to another

699
00:34:32,680 --> 00:34:35,759
Speaker 1: with music. And then you get at home or you'd

700
00:34:35,840 --> 00:34:37,560
Speaker 1: listen and then you go, well, that's cool song. That's

701
00:34:37,560 --> 00:34:40,319
Speaker 1: a cool song, and then you'd go, what's the rest

702
00:34:40,360 --> 00:34:41,920
Speaker 1: of this? And it was like, well, they had to

703
00:34:41,920 --> 00:34:44,359
Speaker 1: put an album out. I guess joby, it's your turn

704
00:34:44,440 --> 00:34:46,160
Speaker 1: with Sterling on the Big One. Hey, what do you have?

705
00:34:47,320 --> 00:34:47,560
Speaker 2: Oh?

706
00:34:47,719 --> 00:34:51,120
Speaker 5: Well, real briefly here i'd have to say to who

707
00:34:51,800 --> 00:34:52,920
Speaker 5: and Who's next?

708
00:34:53,200 --> 00:34:57,839
Speaker 3: Oh tremendous, O'Reilly and m's really well there it really does.

709
00:34:58,480 --> 00:35:01,400
Speaker 1: Who made a bunch of incredible music? And I know

710
00:35:01,480 --> 00:35:03,680
Speaker 1: that at one point they had I think they'd retired

711
00:35:03,719 --> 00:35:07,279
Speaker 1: a couple of times over the years, and still, you know,

712
00:35:07,600 --> 00:35:09,719
Speaker 1: just until recent years, have been out there doing it.

713
00:35:09,719 --> 00:35:12,120
Speaker 1: So yeah, I think Who's Next is tremendous. That's a

714
00:35:12,160 --> 00:35:15,359
Speaker 1: great choice. Uh five point three seven, four nine, eight

715
00:35:15,640 --> 00:35:19,439
Speaker 1: hundred The Big One. Let's uh get to Cincinnati's JT

716
00:35:19,800 --> 00:35:21,319
Speaker 1: was Stirling on the Big One, j T, what do

717
00:35:21,360 --> 00:35:21,600
Speaker 1: you have?

718
00:35:23,360 --> 00:35:23,560
Speaker 7: Hey?

719
00:35:23,640 --> 00:35:27,640
Speaker 8: I have the Beatles White Album, I have Abby Road,

720
00:35:28,560 --> 00:35:31,879
Speaker 8: I have the BAC Boys Licensed to.

721
00:35:31,880 --> 00:35:36,080
Speaker 3: Ill tremendous and I have you know run DMC.

722
00:35:36,640 --> 00:35:40,160
Speaker 8: I can go on and on, but led Zeppelin two,

723
00:35:40,600 --> 00:35:41,640
Speaker 8: leb Zeplam four.

724
00:35:43,239 --> 00:35:45,239
Speaker 1: Now see, I think Zeppelin three is one of their

725
00:35:45,280 --> 00:35:48,280
Speaker 1: best and underrated and just sort of overlooked.

726
00:35:47,840 --> 00:35:49,719
Speaker 3: For somewhat reason. Don't you love Zeppelin three?

727
00:35:51,280 --> 00:35:51,960
Speaker 2: I do too.

728
00:35:52,480 --> 00:35:54,120
Speaker 8: I wasn't gonna say it because I want to. I

729
00:35:54,120 --> 00:35:56,239
Speaker 8: don't want to come across on air like I'm some

730
00:35:56,280 --> 00:35:57,080
Speaker 8: sort of believest.

731
00:35:57,120 --> 00:35:59,640
Speaker 1: But oh wait, I know now I'm an elitist, don't know,

732
00:36:04,120 --> 00:36:07,040
Speaker 1: but no Beast Boys Licensed to Ill?

733
00:36:07,640 --> 00:36:10,600
Speaker 8: Like I can recite that entire album. I won't do

734
00:36:10,960 --> 00:36:13,719
Speaker 8: it for you now, but from beginning to end for

735
00:36:13,880 --> 00:36:16,360
Speaker 8: all sixty seven point three four minutes.

736
00:36:16,560 --> 00:36:18,040
Speaker 3: There you go. That's tremendous.

737
00:36:18,120 --> 00:36:18,319
Speaker 2: JT.

738
00:36:18,440 --> 00:36:19,880
Speaker 3: Thank you, my man. I appreciate the call.

739
00:36:19,920 --> 00:36:21,759
Speaker 1: It the wide Oak and Mike was sterling on the

740
00:36:21,760 --> 00:36:24,359
Speaker 1: Big One talking about albums that are good front to back.

741
00:36:24,400 --> 00:36:26,279
Speaker 1: You don't have to skip any tracks. You're like, no,

742
00:36:26,560 --> 00:36:28,160
Speaker 1: this is a work of brilliance, Mike.

743
00:36:28,200 --> 00:36:28,799
Speaker 3: What do you have.

744
00:36:30,239 --> 00:36:30,680
Speaker 5: Another?

745
00:36:30,760 --> 00:36:31,279
Speaker 2: Vote for?

746
00:36:31,360 --> 00:36:32,880
Speaker 6: Who's Next? By the Who?

747
00:36:33,040 --> 00:36:34,760
Speaker 3: Hard to go wrong with them.

748
00:36:35,600 --> 00:36:37,960
Speaker 8: But the one song that is not good enough Love

749
00:36:38,960 --> 00:36:41,799
Speaker 8: was not even written by Townsend, not sung by Roger.

750
00:36:41,600 --> 00:36:46,320
Speaker 9: Daltrey, but John Intwistle wrote in song and sang my wife,

751
00:36:46,600 --> 00:36:48,600
Speaker 9: and that's definitely a song that I just crank it

752
00:36:48,680 --> 00:36:51,879
Speaker 9: up to eleven and my son and I he's thirty five.

753
00:36:51,920 --> 00:36:53,799
Speaker 9: We both went up to Madison Square Garden to see

754
00:36:53,840 --> 00:36:55,040
Speaker 9: him play their last concert.

755
00:36:55,200 --> 00:36:56,560
Speaker 3: How was that at the gardens?

756
00:36:57,760 --> 00:36:59,759
Speaker 6: It was It was great. You didn't want it to end.

757
00:37:00,160 --> 00:37:01,319
Speaker 2: Yeah, it was so great.

758
00:37:01,560 --> 00:37:03,600
Speaker 6: It was sad, yeah, playing the last song, but it

759
00:37:03,640 --> 00:37:03,959
Speaker 6: was great.

760
00:37:04,040 --> 00:37:05,680
Speaker 3: That's pretty cool. What an amazing thing.

761
00:37:05,680 --> 00:37:07,640
Speaker 1: When you think about what was the Cincinnati Gardens, then

762
00:37:07,640 --> 00:37:10,040
Speaker 1: you think about Madison Square Garden, Mike, I appreciate the call.

763
00:37:10,520 --> 00:37:12,800
Speaker 1: Madison Square Garden with a bunch of boxing over the weekend.

764
00:37:13,000 --> 00:37:17,200
Speaker 1: They had to warn people about brawling. And then there

765
00:37:17,239 --> 00:37:19,520
Speaker 1: was the whole two pay thing, which I'll get into later,

766
00:37:19,560 --> 00:37:22,200
Speaker 1: which sounds like a big time like a wrestling kind

767
00:37:22,239 --> 00:37:24,560
Speaker 1: of scenario. You had a title bout and the guy

768
00:37:24,560 --> 00:37:26,000
Speaker 1: with the two pay in the ring. Why wouldn't you

769
00:37:26,040 --> 00:37:27,680
Speaker 1: just go in there with your head shaved and go

770
00:37:27,760 --> 00:37:30,399
Speaker 1: to work and got that two paid knocked off? Which

771
00:37:30,440 --> 00:37:33,319
Speaker 1: is just one of the best things ever. I mean,

772
00:37:33,360 --> 00:37:35,600
Speaker 1: it's not, but it is. And then they were nice enough.

773
00:37:35,719 --> 00:37:39,480
Speaker 1: And I actually heard Ken mention this too. Somebody or

774
00:37:39,520 --> 00:37:42,320
Speaker 1: a group of people would ever grab that dead animal

775
00:37:42,360 --> 00:37:44,600
Speaker 1: skin whatever it is on the guy's head and tossed

776
00:37:44,640 --> 00:37:46,239
Speaker 1: it back to him so we could go get that

777
00:37:46,320 --> 00:37:48,480
Speaker 1: glue and get it back attached to his dome, which

778
00:37:48,520 --> 00:37:51,440
Speaker 1: is pretty wild to pick what and Fred was Sterling

779
00:37:51,480 --> 00:37:55,520
Speaker 1: on seven hundred WLW, what's your favorite or top albums

780
00:37:56,120 --> 00:37:58,480
Speaker 1: that are front to back the just Unstoppable.

781
00:38:00,120 --> 00:38:03,760
Speaker 4: He's always enjoyed listening the whole album Tress ham.

782
00:38:03,560 --> 00:38:05,120
Speaker 5: Braised by Zzy Top.

783
00:38:06,600 --> 00:38:10,640
Speaker 3: Nice. Yeah, always rid songs, absolutely, Arro.

784
00:38:10,719 --> 00:38:13,320
Speaker 10: Smith out of my like is I like the Rocks album?

785
00:38:13,440 --> 00:38:13,600
Speaker 3: Yeah?

786
00:38:13,680 --> 00:38:16,239
Speaker 1: Rocks is tremendous, you know, and Done with Mirrors is

787
00:38:16,239 --> 00:38:18,640
Speaker 1: pretty okay, but I mean it's certainly not quite the

788
00:38:18,680 --> 00:38:22,080
Speaker 1: same as the other two, that's for sure, but that's tremendous.

789
00:38:22,120 --> 00:38:22,520
Speaker 2: Go ahead.

790
00:38:22,920 --> 00:38:26,000
Speaker 5: Yeah, And of course you know ac DC, you know,

791
00:38:26,160 --> 00:38:31,000
Speaker 5: back in Black. And I also like the first live Kiss.

792
00:38:30,719 --> 00:38:32,160
Speaker 3: Album, oh yeah, to cover.

793
00:38:32,280 --> 00:38:33,759
Speaker 1: Yeah, I think that's how a lot of us ended

794
00:38:33,840 --> 00:38:35,879
Speaker 1: up sort of like getting into that Kiss army kind

795
00:38:35,920 --> 00:38:37,480
Speaker 1: of thing. Was that the first record and then you

796
00:38:37,560 --> 00:38:39,359
Speaker 1: kind of like, you know, and going for there too, Fred,

797
00:38:39,400 --> 00:38:41,239
Speaker 1: I appreciate the call man, thank you if I've went

798
00:38:41,280 --> 00:38:43,680
Speaker 1: three seven, four, nine, eight hundred, the big one. Looking

799
00:38:43,680 --> 00:38:45,640
Speaker 1: for a favorite albums front the back that are just

800
00:38:45,680 --> 00:38:49,160
Speaker 1: a flat out unstoppable, Uh, that you don't have to

801
00:38:49,320 --> 00:38:52,320
Speaker 1: deal with anything. Oh this is nice. Price Hill marks

802
00:38:52,400 --> 00:38:55,000
Speaker 1: his system of a down steal this album. Yeah, that's

803
00:38:55,040 --> 00:38:57,680
Speaker 1: pretty strong. A good call there. I appreciate you getting

804
00:38:57,719 --> 00:38:59,759
Speaker 1: interactive that way at Sterling Radio. By the way, on

805
00:38:59,880 --> 00:39:01,719
Speaker 1: x you can do it too. To Waynesville where they

806
00:39:01,719 --> 00:39:04,879
Speaker 1: had that Souerkrapt festival and it's usually much warmer than now.

807
00:39:04,960 --> 00:39:07,640
Speaker 1: So Dale, how's everything? What do you have for us?

808
00:39:08,200 --> 00:39:10,759
Speaker 10: I hate doing pretty good sunshining here, so you know,

809
00:39:10,800 --> 00:39:11,760
Speaker 10: but it's still cold.

810
00:39:11,800 --> 00:39:14,920
Speaker 1: Well, it's warmed up. I mean it is almost shorts weather.

811
00:39:15,000 --> 00:39:17,920
Speaker 1: I mean it's twenty three now here, so I mean

812
00:39:17,960 --> 00:39:21,080
Speaker 1: I'm feeling like it's summer. We should be mowing the

813
00:39:21,080 --> 00:39:23,000
Speaker 1: grass a great American ball park and getting ready for

814
00:39:23,160 --> 00:39:23,560
Speaker 1: ending day.

815
00:39:24,760 --> 00:39:27,720
Speaker 10: Yeah, buddy, well I look forward to this. I retired

816
00:39:27,760 --> 00:39:31,080
Speaker 10: where I was cutting steel with torches. Oh, so I

817
00:39:31,200 --> 00:39:33,080
Speaker 10: really looked forward to coldad So.

818
00:39:33,200 --> 00:39:35,640
Speaker 1: Yeah, because I mean doing that, I had a buddy

819
00:39:35,680 --> 00:39:38,480
Speaker 1: of Mine's father used to work at a still plant

820
00:39:38,520 --> 00:39:40,839
Speaker 1: there between here and Dayton, and he used to talk

821
00:39:40,840 --> 00:39:44,160
Speaker 1: about how just unreasoned, like it was hotter than hades,

822
00:39:44,239 --> 00:39:44,959
Speaker 1: is what he'd say.

823
00:39:46,040 --> 00:39:48,640
Speaker 10: Yeah, buddy, fifteen twenty degrees hotter inside the shop than

824
00:39:48,680 --> 00:39:49,400
Speaker 10: it was outside.

825
00:39:49,480 --> 00:39:51,640
Speaker 3: Wow, in the summertime. That'll get to you. What do

826
00:39:51,680 --> 00:39:53,880
Speaker 3: you have album wise? Like a favorite to you?

827
00:39:55,400 --> 00:39:57,680
Speaker 10: I'm stuck on two yeah, you know, and it's kind

828
00:39:57,680 --> 00:40:00,640
Speaker 10: of tough of my age here, but a lot of

829
00:40:00,719 --> 00:40:04,960
Speaker 10: revival Cosmos Factory. Oh and uh, you know, just just

830
00:40:05,840 --> 00:40:09,919
Speaker 10: great music, you know. And uh uh the other uh

831
00:40:10,480 --> 00:40:12,960
Speaker 10: that that can't be beaten as far as the music

832
00:40:13,000 --> 00:40:14,399
Speaker 10: and the vocals and everything like.

833
00:40:14,360 --> 00:40:19,920
Speaker 3: That is Eagle's greatest hits. Man, there you go and.

834
00:40:19,920 --> 00:40:22,239
Speaker 1: See and I'm on the fence, I gotta tell you, Dale,

835
00:40:22,320 --> 00:40:25,040
Speaker 1: And I wondered about this before I got into the topic.

836
00:40:25,080 --> 00:40:27,200
Speaker 1: And this comes around usually do this once or twice

837
00:40:27,239 --> 00:40:30,600
Speaker 1: a year, and uh, I always kind of go, does

838
00:40:30,640 --> 00:40:33,600
Speaker 1: the greatest hits album count? Because it is already greatest hits?

839
00:40:33,640 --> 00:40:35,920
Speaker 1: But I mean it does it just brutalizes you, smacks

840
00:40:35,960 --> 00:40:37,799
Speaker 1: you in the head. Boy, love this, You're gonna love

841
00:40:37,800 --> 00:40:40,799
Speaker 1: this more, gonna love that more, I know.

842
00:40:40,760 --> 00:40:42,960
Speaker 10: But you know, front to back, you know who you

843
00:40:43,000 --> 00:40:43,520
Speaker 10: talk about.

844
00:40:43,640 --> 00:40:47,000
Speaker 3: That's what I'm saying. Unstoppable, We'll have.

845
00:40:47,239 --> 00:40:50,040
Speaker 10: Yeah, most albums will have two hits and the rest

846
00:40:50,040 --> 00:40:51,680
Speaker 10: of them just kind of there.

847
00:40:51,960 --> 00:40:54,440
Speaker 1: That's right, filler, you got it. That's the thing. And

848
00:40:54,680 --> 00:40:56,080
Speaker 1: in times are different now you don't have to worry

849
00:40:56,080 --> 00:40:58,239
Speaker 1: about the filler. Dale, Thanks for the call, man. Maybe

850
00:40:58,280 --> 00:41:01,040
Speaker 1: we'll run into you, uh at the this Outkraut Fest

851
00:41:01,040 --> 00:41:03,040
Speaker 1: at some point. Buddies of mine used to go down

852
00:41:03,040 --> 00:41:04,279
Speaker 1: there and work that when I was a kid. We're

853
00:41:04,280 --> 00:41:07,279
Speaker 1: actually my buddy's kids who are now grown now. So

854
00:41:07,600 --> 00:41:09,920
Speaker 1: I'm showing my age a little bit to Richmond, Indiana,

855
00:41:10,520 --> 00:41:13,360
Speaker 1: wondering a little bit in the land of John Mellencamp

856
00:41:13,400 --> 00:41:15,880
Speaker 1: of course, and not too far from there. And dar

857
00:41:15,960 --> 00:41:18,200
Speaker 1: Jim Scott too. Ian what's going on? You were sterling

858
00:41:18,239 --> 00:41:18,400
Speaker 1: on the.

859
00:41:18,360 --> 00:41:22,360
Speaker 11: Big One, Good afternoon. I want to throw two albums

860
00:41:22,400 --> 00:41:25,200
Speaker 11: at you. One would be Hysteria by Death Leppard.

861
00:41:25,320 --> 00:41:25,920
Speaker 3: Yeah strong.

862
00:41:26,239 --> 00:41:29,000
Speaker 11: I don't think anyone pays attention to just how many

863
00:41:29,080 --> 00:41:32,120
Speaker 11: great songs came off of that. Yes, and I guess

864
00:41:32,120 --> 00:41:34,080
Speaker 11: it's for kind of the greatest hits. But I always

865
00:41:34,200 --> 00:41:36,640
Speaker 11: was partial a legend by Bob Marley.

866
00:41:36,800 --> 00:41:38,960
Speaker 1: Oh, how do you go wrong with Bob Marley? Just

867
00:41:39,000 --> 00:41:41,200
Speaker 1: in general? Yeah, I'm totally with you there.

868
00:41:41,520 --> 00:41:44,239
Speaker 11: Yeah, thank you for your time, man, every great day.

869
00:41:44,280 --> 00:41:48,839
Speaker 1: Appreciate you listening, man, Thank you. Okay computer Elizabeth doesn't

870
00:41:48,880 --> 00:41:51,640
Speaker 1: say where she's from. Just Elizabeth said, okay, computer, that's Radiohead.

871
00:41:51,680 --> 00:41:53,640
Speaker 1: She didn't even put Radiohead, but I know what that is.

872
00:41:53,920 --> 00:41:56,520
Speaker 1: That's going back to the nineties, my time at Channel

873
00:41:56,600 --> 00:42:02,200
Speaker 1: ZO before Kiss became Kiss South Lebanon. Carl with Sterling

874
00:42:02,239 --> 00:42:03,680
Speaker 1: on the Big One. What do you have best album

875
00:42:03,760 --> 00:42:07,160
Speaker 1: front to back that you can think of? That's you

876
00:42:07,280 --> 00:42:08,480
Speaker 1: and it's me too on the Big One.

877
00:42:08,520 --> 00:42:09,040
Speaker 3: What's going on?

878
00:42:09,080 --> 00:42:09,360
Speaker 4: Carl?

879
00:42:09,480 --> 00:42:12,000
Speaker 3: You're talking to Sterling? Hold on, Hey, how are you.

880
00:42:14,360 --> 00:42:14,960
Speaker 2: Okay?

881
00:42:15,160 --> 00:42:18,280
Speaker 7: So I've got I've got two albums and any gritty

882
00:42:18,320 --> 00:42:18,799
Speaker 7: dark bands?

883
00:42:18,800 --> 00:42:20,120
Speaker 6: Will the Circle be Unbroken?

884
00:42:20,280 --> 00:42:22,759
Speaker 7: Which introduced bluegrass to the mainstream.

885
00:42:23,000 --> 00:42:25,400
Speaker 5: Oh, and by Fleetwood Mac.

886
00:42:25,680 --> 00:42:28,120
Speaker 1: Yeah, hard to go wrong with the Mac, that's for sure.

887
00:42:28,680 --> 00:42:31,239
Speaker 1: You know, I wasn't around. I mean, I may have

888
00:42:31,280 --> 00:42:33,879
Speaker 1: been a Hatchling at that point, but one of those

889
00:42:33,920 --> 00:42:36,440
Speaker 1: things coming up and I heard this, I don't know

890
00:42:36,480 --> 00:42:38,000
Speaker 1: if it's true that at one point and this is

891
00:42:38,040 --> 00:42:38,520
Speaker 1: a bad thing.

892
00:42:38,680 --> 00:42:39,680
Speaker 3: Just just sort of share.

893
00:42:39,880 --> 00:42:42,400
Speaker 1: But I guess Stevie Nick couldn't it doesn't see so

894
00:42:42,480 --> 00:42:44,840
Speaker 1: well and may or may not have been a little bit, uh,

895
00:42:44,920 --> 00:42:48,320
Speaker 1: you know, keyed up for the show. Apparently the allegation

896
00:42:48,480 --> 00:42:50,839
Speaker 1: is that she walked off a stage, couldn't see because

897
00:42:50,840 --> 00:42:52,160
Speaker 1: of the spotlight or whatever else.

898
00:42:52,239 --> 00:42:53,280
Speaker 3: Do you know what that's true?

899
00:42:54,360 --> 00:42:54,920
Speaker 6: I do not.

900
00:42:55,360 --> 00:42:55,920
Speaker 2: I do not.

901
00:42:56,120 --> 00:42:58,560
Speaker 1: I just think of that being a horrible nightmare scenario.

902
00:42:58,680 --> 00:43:01,160
Speaker 1: And she's a small, little wi thing. I I mean,

903
00:43:01,160 --> 00:43:03,560
Speaker 1: that would be a it's not an easy fall.

904
00:43:04,239 --> 00:43:06,280
Speaker 3: I just told that would be horrible.

905
00:43:06,480 --> 00:43:07,360
Speaker 2: That would be horrible.

906
00:43:07,480 --> 00:43:10,440
Speaker 1: It certainly all right, man, I just thought i'd ask you.

907
00:43:10,640 --> 00:43:12,359
Speaker 1: Maybe you might have been aware and around a little

908
00:43:12,360 --> 00:43:13,160
Speaker 1: bit longer than I'd been.

909
00:43:13,280 --> 00:43:13,520
Speaker 3: Carl.

910
00:43:13,520 --> 00:43:15,160
Speaker 1: I appreciate the call me and thank you for listening

911
00:43:15,200 --> 00:43:16,560
Speaker 1: to being a part of the show. Let's get to

912
00:43:16,600 --> 00:43:18,719
Speaker 1: Florence and Todd with Sterling on the big one. What

913
00:43:18,719 --> 00:43:20,439
Speaker 1: do you have best album? Front to back?

914
00:43:22,200 --> 00:43:25,080
Speaker 12: I would go with Van Halen's debut album nineteen seventy

915
00:43:25,160 --> 00:43:29,400
Speaker 12: from nineteen seventy eight, Oh Hard titled Yeah Yeah, great

916
00:43:29,440 --> 00:43:32,839
Speaker 12: songs absolutely, Or I would go with nineteen nineteen eighty four.

917
00:43:32,880 --> 00:43:34,520
Speaker 12: That's a great one to Both of those albums have

918
00:43:34,560 --> 00:43:36,360
Speaker 12: sold in excess of ten million copies.

919
00:43:36,520 --> 00:43:38,560
Speaker 3: Yeah that's massive. Yeah, that's huge.

920
00:43:38,760 --> 00:43:40,719
Speaker 12: The greatest thing to me, The greatest thing to me

921
00:43:40,760 --> 00:43:43,560
Speaker 12: about van Halen is their music is fun. You know,

922
00:43:43,600 --> 00:43:46,560
Speaker 12: they're like hard rock for not heavy metal with their

923
00:43:46,680 --> 00:43:48,120
Speaker 12: hard rock, but their music's fun.

924
00:43:48,160 --> 00:43:48,359
Speaker 11: Man.

925
00:43:48,520 --> 00:43:51,000
Speaker 1: Now, did you like van Hagar with Sammy fronting the Man?

926
00:43:51,000 --> 00:43:53,080
Speaker 1: Because I loved both. But it was an either or

927
00:43:53,160 --> 00:43:55,000
Speaker 1: thing when I was a kid coming up. People were

928
00:43:55,120 --> 00:43:57,600
Speaker 1: very passionate and very hateful, even on the Sam and

929
00:43:57,680 --> 00:44:00,399
Speaker 1: Dave tour that they did, which they didn't exactly along

930
00:44:00,440 --> 00:44:01,080
Speaker 1: for some reason.

931
00:44:02,200 --> 00:44:04,960
Speaker 12: I like Van Hagar because Eddy van Halen's playing the guitar,

932
00:44:05,320 --> 00:44:08,160
Speaker 12: but the music was definitely different. My favorite is the

933
00:44:08,200 --> 00:44:09,000
Speaker 12: original lineup.

934
00:44:09,080 --> 00:44:10,759
Speaker 3: I gotcha. I don't disagree.

935
00:44:10,840 --> 00:44:13,839
Speaker 1: I mean this, there was no bigger album at all

936
00:44:13,840 --> 00:44:14,319
Speaker 1: at this point.

937
00:44:14,360 --> 00:44:15,799
Speaker 3: I mean it was everywhere and you.

938
00:44:15,719 --> 00:44:17,560
Speaker 1: Were like a lot of people are like synthesizers and

939
00:44:17,640 --> 00:44:21,040
Speaker 1: van Halen that's sacrilege. But people started to love it then.

940
00:44:21,520 --> 00:44:24,239
Speaker 1: And I love the Balance album with Sammy Hagar from

941
00:44:24,239 --> 00:44:26,000
Speaker 1: the mid nineties. I thought that was strong too. In

942
00:44:26,000 --> 00:44:28,000
Speaker 1: hell that's thirty years ago, Todd. I appreciate the call,

943
00:44:28,600 --> 00:44:31,360
Speaker 1: and there's a lot of stuff in between that for

944
00:44:31,440 --> 00:44:32,160
Speaker 1: that matter too.

945
00:44:32,480 --> 00:44:35,560
Speaker 3: Oh, this is nice. We got a balance here between.

946
00:44:35,320 --> 00:44:39,279
Speaker 1: Calls and people on social At Sterling Radio on X

947
00:44:39,360 --> 00:44:46,400
Speaker 1: this is nice, Marvin says, Kanye's My Beautiful, dark, Twisted Fantasy.

948
00:44:46,440 --> 00:44:48,680
Speaker 3: That is a great album. But Kanye, I mean, he's

949
00:44:48,719 --> 00:44:52,920
Speaker 3: had some issues.

950
00:44:53,120 --> 00:44:56,160
Speaker 1: Let's just say the music is amazing, but sometimes you

951
00:44:56,160 --> 00:44:58,200
Speaker 1: should just shut the hell up. But people have told

952
00:44:58,239 --> 00:45:00,520
Speaker 1: me that too, So you know what. I can't everything

953
00:45:00,560 --> 00:45:02,239
Speaker 1: against the guy, but some of what he said has

954
00:45:02,280 --> 00:45:04,920
Speaker 1: sort of taken me a bit personally. Let's get to

955
00:45:05,000 --> 00:45:06,840
Speaker 1: Mason and Brett. We got Rick and Jim. Where are

956
00:45:06,880 --> 00:45:08,960
Speaker 1: the women on this? I know your music lovers to

957
00:45:09,200 --> 00:45:11,040
Speaker 1: front to back best album, Brett, what do you have

958
00:45:11,160 --> 00:45:12,200
Speaker 1: with Sterling on the big one?

959
00:45:13,360 --> 00:45:15,839
Speaker 13: Well, I got something a little unique for you. So

960
00:45:16,280 --> 00:45:20,520
Speaker 13: it's an album and a movie. So if you listen

961
00:45:20,600 --> 00:45:24,360
Speaker 13: to Kink Floyd Dark Side of the Moon, which is

962
00:45:24,400 --> 00:45:27,120
Speaker 13: a great album on its own, all the way through, yes,

963
00:45:27,400 --> 00:45:30,200
Speaker 13: and you think it with Wizard of Oz it is

964
00:45:30,320 --> 00:45:34,560
Speaker 13: absolutely incredible. Now you run those boat all the way through.

965
00:45:34,400 --> 00:45:34,879
Speaker 2: To the end.

966
00:45:35,080 --> 00:45:37,800
Speaker 3: Here's my question, and take no offense to this. Please.

967
00:45:38,400 --> 00:45:41,719
Speaker 1: Is it better after visiting the dispensary or no?

968
00:45:43,120 --> 00:45:44,279
Speaker 13: Well that's a requirement.

969
00:45:44,400 --> 00:45:48,160
Speaker 3: Okay. I just wondered. Again, I'm not judging. I just wondered.

970
00:45:48,760 --> 00:45:52,480
Speaker 1: I remember them all right, fair enough, Brett. I appreciate

971
00:45:52,480 --> 00:45:54,080
Speaker 1: the call. Anything else before I let you bounce? I

972
00:45:54,080 --> 00:45:55,640
Speaker 1: had somebody just a message me said, I hung up

973
00:45:55,680 --> 00:45:57,239
Speaker 1: too soon, and then they came back at me with

974
00:45:57,280 --> 00:45:59,960
Speaker 1: another Johnny Cash record. Anything else before I let you go?

975
00:46:00,000 --> 00:46:00,040
Speaker 4: No?

976
00:46:01,640 --> 00:46:02,960
Speaker 6: No, I think I think that's it.

977
00:46:03,040 --> 00:46:05,839
Speaker 13: I think there's probably too many to mention. And I

978
00:46:05,840 --> 00:46:07,720
Speaker 13: do want to mention though, that when we were kids,

979
00:46:07,800 --> 00:46:09,880
Speaker 13: I think we listen to albums all the way through

980
00:46:10,320 --> 00:46:10,920
Speaker 13: more than we.

981
00:46:10,920 --> 00:46:12,719
Speaker 2: Do now, especially you know what I mean.

982
00:46:12,800 --> 00:46:14,759
Speaker 13: I think the quality of music was better, probably, and

983
00:46:15,320 --> 00:46:17,400
Speaker 13: we just there wasn't that many things to listen to,

984
00:46:17,520 --> 00:46:18,759
Speaker 13: so you really took the time to do it.

985
00:46:18,800 --> 00:46:21,000
Speaker 1: I think I wonder what it's like in that situation.

986
00:46:21,120 --> 00:46:23,200
Speaker 1: And I appreciate the call Brett as an artist, as

987
00:46:23,239 --> 00:46:27,399
Speaker 1: a creator, someone who is writing the music and orchestrating it,

988
00:46:27,560 --> 00:46:30,759
Speaker 1: and you know, coming together with these ideas and making them.

989
00:46:30,800 --> 00:46:33,480
Speaker 1: So I wonder in some fashion because a lot of

990
00:46:33,560 --> 00:46:36,839
Speaker 1: artists now just because of the way the businesses are

991
00:46:36,880 --> 00:46:39,279
Speaker 1: putting out, you know, one song at a time and

992
00:46:39,320 --> 00:46:41,960
Speaker 1: then maybe a collection of songs after that and touring

993
00:46:42,000 --> 00:46:46,560
Speaker 1: that way, because that's got to be different, just as

994
00:46:46,600 --> 00:46:49,200
Speaker 1: the way you approach it and going along with that.

995
00:46:49,880 --> 00:46:52,800
Speaker 1: But my friend's kids, and I'm talking like little kids,

996
00:46:52,840 --> 00:46:55,120
Speaker 1: middle school, you know, maybe not some of them, some

997
00:46:55,239 --> 00:46:57,680
Speaker 1: into high school. And then they seem to come back

998
00:46:57,719 --> 00:46:59,719
Speaker 1: around and they're telling me that they're finding all this

999
00:46:59,760 --> 00:47:01,600
Speaker 1: old stuff, a lot of the stuff I played when

1000
00:47:01,600 --> 00:47:04,080
Speaker 1: it was Channel Z and and still listening to EBN

1001
00:47:04,160 --> 00:47:06,720
Speaker 1: but embracing the album. So maybe, like a lot of stuff,

1002
00:47:06,719 --> 00:47:09,920
Speaker 1: it just keeps coming back around. We'll see how that goes.

1003
00:47:10,280 --> 00:47:11,880
Speaker 1: Let's get to Tim and then we got Randy and

1004
00:47:11,960 --> 00:47:14,480
Speaker 1: Rick and others with Stirling on seven hundred WLW.

1005
00:47:14,600 --> 00:47:14,960
Speaker 3: What do you have?

1006
00:47:15,040 --> 00:47:15,320
Speaker 6: Tim?

1007
00:47:16,840 --> 00:47:17,920
Speaker 5: Yes, sir, you got me?

1008
00:47:17,960 --> 00:47:19,120
Speaker 3: There, I gotcha, you got me?

1009
00:47:20,320 --> 00:47:20,560
Speaker 2: Yeah.

1010
00:47:20,680 --> 00:47:24,239
Speaker 14: Good Days of Future pass Days of Future passed.

1011
00:47:24,000 --> 00:47:28,239
Speaker 5: By the Moody BLUESO and I you know, be.

1012
00:47:28,280 --> 00:47:30,960
Speaker 14: A set that probably Dark Side of the Moon too,

1013
00:47:31,000 --> 00:47:34,480
Speaker 14: but that Day's of Future past. One song moves to

1014
00:47:34,520 --> 00:47:37,480
Speaker 14: the other so elegantly. You know, it's all fits together,

1015
00:47:38,239 --> 00:47:40,960
Speaker 14: and in fact, at my funeral, I planned to have

1016
00:47:41,080 --> 00:47:44,840
Speaker 14: this music played for the people who were waiting, you know,

1017
00:47:45,239 --> 00:47:45,960
Speaker 14: for the funeral.

1018
00:47:46,239 --> 00:47:50,760
Speaker 1: Ooh, okay, see, I hadn't really thought ahead about orchestrating,

1019
00:47:50,840 --> 00:47:54,480
Speaker 1: you know, the soundtrack to my passing and send off,

1020
00:47:54,480 --> 00:47:56,200
Speaker 1: But you're thinking ahead, Tim.

1021
00:47:56,239 --> 00:47:57,319
Speaker 3: That's tremendous. Thank you, man.

1022
00:47:57,360 --> 00:47:59,239
Speaker 1: I appreciate the call. It took me a minute. It

1023
00:47:59,280 --> 00:48:01,320
Speaker 1: sort of shook me. But I know others who have

1024
00:48:01,400 --> 00:48:03,040
Speaker 1: done that. I mean, I don't know how many times

1025
00:48:03,080 --> 00:48:07,640
Speaker 1: I've heard a Skinner's Freebird at somebody's wake or visitation

1026
00:48:07,840 --> 00:48:10,640
Speaker 1: or something along those lines, or some type of celebration

1027
00:48:10,719 --> 00:48:12,880
Speaker 1: of life scenarios. So I mean, I know that kind

1028
00:48:12,920 --> 00:48:15,120
Speaker 1: of thing happens, that is for sure.

1029
00:48:16,320 --> 00:48:16,480
Speaker 2: Man.

1030
00:48:16,520 --> 00:48:18,880
Speaker 1: What about like all the I mean, there's so many

1031
00:48:19,000 --> 00:48:22,640
Speaker 1: like James Brown songs and Bootsy Collins still making relevant

1032
00:48:22,719 --> 00:48:26,440
Speaker 1: music to this point, I mean, all the name is

1033
00:48:26,440 --> 00:48:31,680
Speaker 1: Bootsy Baby, I mean, seriously tremendous album that was. I

1034
00:48:31,800 --> 00:48:34,520
Speaker 1: was barely here at that point, but I've got a copy.

1035
00:48:34,560 --> 00:48:35,399
Speaker 1: That's one of those I bought.

1036
00:48:35,520 --> 00:48:35,800
Speaker 3: Used.

1037
00:48:35,960 --> 00:48:37,759
Speaker 1: I mean, there are a bunch of those, I mean,

1038
00:48:37,800 --> 00:48:41,960
Speaker 1: and Bootsy's done everything, just tremendous Randy again in the

1039
00:48:42,040 --> 00:48:44,759
Speaker 1: Hoosier Land, happy with your national champion football team.

1040
00:48:44,840 --> 00:48:45,560
Speaker 3: I don't listen.

1041
00:48:45,680 --> 00:48:47,480
Speaker 1: I know this is an aside and not why you

1042
00:48:47,560 --> 00:48:51,359
Speaker 1: called my whole life almost Indiana football was like a

1043
00:48:51,400 --> 00:48:54,880
Speaker 1: guaranteed win for anybody who played them. How in shock

1044
00:48:55,000 --> 00:48:56,960
Speaker 1: are you that the Hoosiers are national champs?

1045
00:48:58,400 --> 00:49:02,520
Speaker 4: Card shocks a Tennessee from Rockville.

1046
00:49:02,680 --> 00:49:03,960
Speaker 11: So I got you.

1047
00:49:04,840 --> 00:49:06,000
Speaker 6: You're talking about how good they are?

1048
00:49:06,120 --> 00:49:06,880
Speaker 3: Now got you?

1049
00:49:07,160 --> 00:49:07,359
Speaker 2: Yeah?

1050
00:49:07,440 --> 00:49:07,560
Speaker 5: Man?

1051
00:49:09,000 --> 00:49:11,240
Speaker 3: Are Tim mcgeeze like, thank you for loving the balls?

1052
00:49:11,320 --> 00:49:11,919
Speaker 2: Yeah? For sure?

1053
00:49:11,960 --> 00:49:12,320
Speaker 13: What do you have?

1054
00:49:12,440 --> 00:49:13,520
Speaker 3: I know that's not why you call it.

1055
00:49:14,320 --> 00:49:14,719
Speaker 2: I got it.

1056
00:49:14,760 --> 00:49:17,319
Speaker 6: Oh yeah, I appreciate you asking though, Thanks for taking

1057
00:49:17,360 --> 00:49:17,640
Speaker 6: my call.

1058
00:49:17,800 --> 00:49:18,120
Speaker 3: Ye man.

1059
00:49:18,480 --> 00:49:21,720
Speaker 6: I got a couple of not so well known artists.

1060
00:49:22,440 --> 00:49:25,480
Speaker 6: One of mine is Gary Moore. Oh sure, he's one

1061
00:49:25,480 --> 00:49:27,759
Speaker 6: of the greatest blues guitarists wherever was.

1062
00:49:28,120 --> 00:49:31,920
Speaker 3: Absolutely the album is called Are Still About the Blues?

1063
00:49:33,640 --> 00:49:37,560
Speaker 4: And then I've got another one. This lady is fantastic.

1064
00:49:37,680 --> 00:49:40,640
Speaker 4: If you've never seen her, report her name is Samantha Fish.

1065
00:49:40,880 --> 00:49:44,840
Speaker 6: Oh yeah, uh killer be kind her album. She's up

1066
00:49:44,880 --> 00:49:46,239
Speaker 6: for a Grammy Award tonight too.

1067
00:49:46,760 --> 00:49:49,120
Speaker 1: That's nice, very cool, Randy, Thank you, that's relevant. I

1068
00:49:49,200 --> 00:49:51,600
Speaker 1: mean and you think about Gary Moore, I mean thin Lizzie.

1069
00:49:51,640 --> 00:49:53,560
Speaker 1: Of course, how good were they in the skid Row?

1070
00:49:54,200 --> 00:49:57,080
Speaker 1: And of course the Gary Moore band tremendous, Jim and

1071
00:49:57,200 --> 00:49:59,640
Speaker 1: Tony and Rick others. Hang on, we'll keep this going

1072
00:49:59,680 --> 00:50:01,279
Speaker 1: a little there's more ground to cover.

1073
00:50:01,840 --> 00:50:01,960
Speaker 6: Uh.

1074
00:50:02,560 --> 00:50:04,560
Speaker 1: We got Kevin Carr a little bit later talking movie

1075
00:50:04,640 --> 00:50:08,200
Speaker 1: stuff and maybe some more artificial intelligence, because while my

1076
00:50:08,320 --> 00:50:11,919
Speaker 1: intelligence is limited and the artificial kind is what's gonna

1077
00:50:11,960 --> 00:50:13,560
Speaker 1: keep us going and move us to the next level,

1078
00:50:13,680 --> 00:50:16,160
Speaker 1: next level. Thank you Alex again making it sound good

1079
00:50:16,200 --> 00:50:18,040
Speaker 1: doing what you do. Sandy Collins got you one third

1080
00:50:18,080 --> 00:50:20,399
Speaker 1: of your ports straight away where the Reds play. Hey,

1081
00:50:20,920 --> 00:50:23,520
Speaker 1: we are just a little less than a month away,

1082
00:50:23,560 --> 00:50:27,360
Speaker 1: the twenty seventh of February. I'll be behind this microphone

1083
00:50:27,440 --> 00:50:30,279
Speaker 1: handing it off to Tommy Thraw and the Cowboy as

1084
00:50:30,360 --> 00:50:33,520
Speaker 1: Red's Baseball begins in the desert, and they'll be going

1085
00:50:33,600 --> 00:50:36,399
Speaker 1: to get to work in less than a month going

1086
00:50:36,440 --> 00:50:38,799
Speaker 1: at it obviously won't be long because we're the home

1087
00:50:38,840 --> 00:50:42,920
Speaker 1: of the Rets. News Radio seven hundred WLW, Cincinnati.

1088
00:50:46,120 --> 00:50:47,880
Speaker 3: Hears for fears. I think, weren't they just on.

1089
00:50:47,960 --> 00:50:50,719
Speaker 1: The road in the last couple of years they were yeah,

1090
00:50:50,960 --> 00:50:53,520
Speaker 1: Alex Eagan, of course producing this is one of your picks.

1091
00:50:53,560 --> 00:50:55,160
Speaker 1: This is one of your favorite albums. It is, I

1092
00:50:55,200 --> 00:50:57,120
Speaker 1: would say Songs from the Big Chair is Yeah, is

1093
00:50:57,160 --> 00:50:58,080
Speaker 1: a big one for me.

1094
00:50:58,480 --> 00:50:59,560
Speaker 3: Now, both others.

1095
00:50:59,640 --> 00:51:01,839
Speaker 1: You've seen those videos, right, and hey, how you doing

1096
00:51:01,920 --> 00:51:04,680
Speaker 1: At Sterling seven hundred WLW, we've been talking about the

1097
00:51:05,160 --> 00:51:08,000
Speaker 1: albums CDs front the back like just you don't turn

1098
00:51:08,080 --> 00:51:09,800
Speaker 1: them off, you don't hit skip. You're like, no, this

1099
00:51:10,000 --> 00:51:12,360
Speaker 1: is a perfect playlist as it is, as it was

1100
00:51:12,440 --> 00:51:15,080
Speaker 1: made by these artists. But the videos for that, I

1101
00:51:15,120 --> 00:51:17,720
Speaker 1: always like, Man, those guys have the biggest and brightest

1102
00:51:17,760 --> 00:51:20,600
Speaker 1: teeth I've ever seen. Yes, it's just one of those things.

1103
00:51:20,640 --> 00:51:22,160
Speaker 1: I'm like, holy crap, I got to get to the

1104
00:51:22,239 --> 00:51:26,200
Speaker 1: dentist now. It's kind of a weird thing. A bunch

1105
00:51:26,280 --> 00:51:29,440
Speaker 1: of people been getting interactive at Sterling Radio one X

1106
00:51:29,520 --> 00:51:33,120
Speaker 1: talking about this too, Kat King, I think it's Stuart

1107
00:51:33,200 --> 00:51:35,480
Speaker 1: not and some random numbers there on X. You can

1108
00:51:35,560 --> 00:51:39,080
Speaker 1: follow along he has or she I'm assuming it's a

1109
00:51:39,120 --> 00:51:41,959
Speaker 1: heats is Ted Nugent's Double Lot Gonzo. Yeah, that's huge.

1110
00:51:42,520 --> 00:51:44,400
Speaker 1: Dugent's still out there doing it. He's like one hundred

1111
00:51:44,400 --> 00:51:47,279
Speaker 1: and twenty six years old, still touring from time to time,

1112
00:51:47,320 --> 00:51:49,600
Speaker 1: a lot of state fares over the years, and still

1113
00:51:49,640 --> 00:51:50,239
Speaker 1: getting it done.

1114
00:51:50,800 --> 00:51:51,479
Speaker 3: Five three.

1115
00:51:53,000 --> 00:51:54,279
Speaker 1: Big, and we've done this for a little while, but

1116
00:51:54,360 --> 00:51:56,840
Speaker 1: people seem to be interested. Normally i'd turned the topic,

1117
00:51:56,920 --> 00:51:59,080
Speaker 1: so we will layer in other stuff. But as you

1118
00:51:59,320 --> 00:52:02,440
Speaker 1: call as, so we get in more interactivity. Let's just

1119
00:52:02,520 --> 00:52:06,120
Speaker 1: say you can mention your favorite album like that, and

1120
00:52:06,200 --> 00:52:08,520
Speaker 1: then we'll go on to other stuff too. Okay, five

1121
00:52:08,520 --> 00:52:11,120
Speaker 1: point three seven four nine, seven, eight hundred, the big one.

1122
00:52:11,160 --> 00:52:12,600
Speaker 1: You can pick up the phone, give it the finger,

1123
00:52:12,719 --> 00:52:14,759
Speaker 1: like my good friend who's no longer with this, mister

1124
00:52:14,840 --> 00:52:18,520
Speaker 1: K used to say, talk back the iHeartRadio app as well.

1125
00:52:19,280 --> 00:52:21,600
Speaker 1: A few people also getting interactive. I don't know if

1126
00:52:21,640 --> 00:52:24,040
Speaker 1: if Alex's screening phone calls. People are still going nuts

1127
00:52:24,080 --> 00:52:25,359
Speaker 1: for this, do you know what it is? I think

1128
00:52:25,400 --> 00:52:27,719
Speaker 1: we're all stir crazy because of the weather and the cold,

1129
00:52:27,800 --> 00:52:30,520
Speaker 1: and now it almost feels like summer and shorts weather.

1130
00:52:31,920 --> 00:52:33,920
Speaker 1: It's let me see, I'm going to refresh the Yeah,

1131
00:52:33,920 --> 00:52:38,200
Speaker 1: it's still twenty three, which still feels fantastic. Soundgarden's super Unknown.

1132
00:52:38,280 --> 00:52:40,440
Speaker 1: I had a couple of people mentioned that that's tremendous.

1133
00:52:40,840 --> 00:52:44,440
Speaker 1: I still like the Bad motor Finger album, Nirvana's in

1134
00:52:44,560 --> 00:52:47,800
Speaker 1: Utero still staying in the Seattle area overall, Alice and

1135
00:52:47,920 --> 00:52:51,480
Speaker 1: Change had some people mentioned that the Dirt record as well.

1136
00:52:51,600 --> 00:52:53,759
Speaker 1: So I mean, see, I'm showing my age when I

1137
00:52:53,840 --> 00:52:57,239
Speaker 1: say record, right, that's kind of odd, but it is

1138
00:52:57,320 --> 00:53:01,320
Speaker 1: true to Milford and Jim was sterling on seven hundred WLW,

1139
00:53:01,520 --> 00:53:01,960
Speaker 1: what's going on?

1140
00:53:02,040 --> 00:53:02,400
Speaker 15: What do you have?

1141
00:53:03,760 --> 00:53:06,080
Speaker 2: Where's all the women? I know?

1142
00:53:06,239 --> 00:53:06,920
Speaker 5: I don't know.

1143
00:53:07,200 --> 00:53:11,400
Speaker 6: I do know that it's all dominated by men calling up.

1144
00:53:11,560 --> 00:53:14,279
Speaker 3: I can't control who calls. I don't know. Find a

1145
00:53:14,320 --> 00:53:16,120
Speaker 3: woman in your life and have her call me.

1146
00:53:16,239 --> 00:53:16,680
Speaker 2: I I am.

1147
00:53:16,880 --> 00:53:19,440
Speaker 3: I cannot forcibly make anyone pick up the phone. I

1148
00:53:19,640 --> 00:53:21,200
Speaker 3: don't know. I feel that way too. I feel like

1149
00:53:21,280 --> 00:53:22,840
Speaker 3: they don't love us. Now it's hurtful.

1150
00:53:23,880 --> 00:53:25,720
Speaker 2: Do they not like listen to albums?

1151
00:53:25,800 --> 00:53:28,360
Speaker 6: Do they just interested in other? Thanks?

1152
00:53:28,400 --> 00:53:30,520
Speaker 1: That's not why I called thank you, But now I

1153
00:53:30,680 --> 00:53:33,000
Speaker 1: have to have that bouncing around in my head as well.

1154
00:53:33,280 --> 00:53:33,960
Speaker 3: Thank you, thank you?

1155
00:53:34,239 --> 00:53:34,359
Speaker 5: Right?

1156
00:53:35,080 --> 00:53:35,719
Speaker 3: What else do you have?

1157
00:53:35,920 --> 00:53:35,960
Speaker 2: You?

1158
00:53:37,280 --> 00:53:39,359
Speaker 6: Yes, there's nine on one two five.

1159
00:53:40,360 --> 00:53:40,520
Speaker 2: Yeah.

1160
00:53:40,640 --> 00:53:42,560
Speaker 9: In fact, I'd go to go on to say that

1161
00:53:42,840 --> 00:53:47,480
Speaker 9: leave It a cappella is like the greatest acapella song ever.

1162
00:53:47,760 --> 00:53:51,000
Speaker 1: Yeah, that's just about what from eighty four maybe eighty four,

1163
00:53:51,080 --> 00:53:55,080
Speaker 1: eighty four, eighty four, Yeah, yeah, yeah, that's true.

1164
00:53:55,400 --> 00:53:58,840
Speaker 3: One I have is Sergeant Pepper's hard to go that.

1165
00:53:59,320 --> 00:54:01,480
Speaker 1: Yeah, I mean it is just remember you know, as

1166
00:54:01,480 --> 00:54:04,280
Speaker 1: a kid, my mom played the Beatles constantly in the house,

1167
00:54:04,840 --> 00:54:08,719
Speaker 1: and uh, and I think it was a rebellion thing. Yeah, absolutely,

1168
00:54:09,120 --> 00:54:11,440
Speaker 1: but at the time it became annoying to me. So

1169
00:54:11,600 --> 00:54:14,239
Speaker 1: then I was searching for something else. I was like, yeah,

1170
00:54:14,239 --> 00:54:15,920
Speaker 1: I'll get it. The Beatles. They're great, but it was

1171
00:54:15,960 --> 00:54:18,319
Speaker 1: one of those I wanted to find my own thing,

1172
00:54:19,080 --> 00:54:21,320
Speaker 1: and so it was like, Okay, here's the Stones, here's this,

1173
00:54:21,560 --> 00:54:23,880
Speaker 1: here's that. And then I came back to the Beatles

1174
00:54:24,040 --> 00:54:26,920
Speaker 1: and was like, okay, Mom, you're absolutely right. They are tremendous.

1175
00:54:27,400 --> 00:54:29,480
Speaker 6: So what the women need to call in?

1176
00:54:29,680 --> 00:54:29,719
Speaker 5: What?

1177
00:54:30,000 --> 00:54:30,840
Speaker 2: What the heck's going on?

1178
00:54:31,640 --> 00:54:34,400
Speaker 1: Here's one now by the way, and I think I

1179
00:54:34,480 --> 00:54:39,760
Speaker 1: can't I can't pronounce it, but it is Elena or something.

1180
00:54:40,080 --> 00:54:44,520
Speaker 1: She says, Pete Jorn, So, uh, there any Pete Orn album?

1181
00:54:44,600 --> 00:54:46,719
Speaker 1: I guess I don't know all of them, So there

1182
00:54:46,800 --> 00:54:48,560
Speaker 1: you go, Jim. I appreciate the call. Yeah, I liked

1183
00:54:48,560 --> 00:54:51,319
Speaker 1: the band yas tremendous. Uh, and they were just here,

1184
00:54:51,560 --> 00:54:55,480
Speaker 1: weren't they? Did they play the Memorial Hall and OTR

1185
00:54:55,719 --> 00:54:57,520
Speaker 1: or something like that, And I was wanting to go,

1186
00:54:57,680 --> 00:54:59,560
Speaker 1: and I was sort of confused on the scheduling. I

1187
00:54:59,600 --> 00:55:01,640
Speaker 1: think they were doing a handful of like shows in

1188
00:55:01,719 --> 00:55:03,400
Speaker 1: the States and that was one of those places. If

1189
00:55:03,400 --> 00:55:05,839
Speaker 1: I'm not mistaken, Yeah, I wish I could have gone.

1190
00:55:05,840 --> 00:55:07,480
Speaker 1: I feel bad. I saw him at what was the

1191
00:55:07,800 --> 00:55:09,360
Speaker 1: Crown at the time, or maybe it was still the

1192
00:55:09,440 --> 00:55:13,279
Speaker 1: Coliseum back in the nineties. I remember my buddies and

1193
00:55:13,320 --> 00:55:17,040
Speaker 1: I we drove down here and they were just astounding.

1194
00:55:17,120 --> 00:55:20,040
Speaker 1: The band, Yes, was tremendous. To hebron and Tony was

1195
00:55:20,080 --> 00:55:22,399
Speaker 1: stirling on seven hundred w L double. You're talking about

1196
00:55:22,400 --> 00:55:23,799
Speaker 1: the greatest albums of all time.

1197
00:55:23,880 --> 00:55:25,960
Speaker 5: Hey man, Hey, how's it going.

1198
00:55:26,080 --> 00:55:27,799
Speaker 3: It's good, it's good. Where are the women, though? I'd

1199
00:55:27,840 --> 00:55:28,120
Speaker 3: like to know.

1200
00:55:28,160 --> 00:55:30,160
Speaker 1: I mean, nothing wrong with you, Tony or Alan or

1201
00:55:30,239 --> 00:55:32,759
Speaker 1: Don or Rick coming up, but I'm now starting to

1202
00:55:32,800 --> 00:55:33,960
Speaker 1: feel like there's a problem.

1203
00:55:34,000 --> 00:55:35,879
Speaker 3: I mean, I love you dudes, but where are the women?

1204
00:55:36,040 --> 00:55:38,360
Speaker 3: I mean serious, I'm with you, And you know what, I.

1205
00:55:38,400 --> 00:55:40,680
Speaker 2: Thought of a second one while while I was waiting

1206
00:55:40,880 --> 00:55:43,239
Speaker 2: and it's a woman singer. Maybe it' step in the

1207
00:55:43,280 --> 00:55:47,800
Speaker 2: right direction with with me. It's Shannis Joplin. Yeah, just

1208
00:55:47,920 --> 00:55:50,839
Speaker 2: like the Pearl. I mean, it's just cool. Like every

1209
00:55:50,960 --> 00:55:54,600
Speaker 2: song on Pearl is at least pretty good. I don't

1210
00:55:54,640 --> 00:55:56,880
Speaker 2: think I've ever hit skip when listening to it. I mean,

1211
00:55:57,560 --> 00:55:58,319
Speaker 2: so that's a good one.

1212
00:55:58,440 --> 00:55:59,560
Speaker 3: Yeah, that's impressed stepping.

1213
00:56:00,680 --> 00:56:02,759
Speaker 2: We did need some more women colors. But the first

1214
00:56:02,800 --> 00:56:05,400
Speaker 2: one I thought I was calling is Appetite for Destruction.

1215
00:56:05,600 --> 00:56:08,960
Speaker 1: Oh yeah, I mean that is one of those, you know,

1216
00:56:09,040 --> 00:56:11,240
Speaker 1: And what a great window of time that is between

1217
00:56:11,880 --> 00:56:15,520
Speaker 1: the Appetite for Destruction mechanical residents from Tesla. I mean

1218
00:56:15,600 --> 00:56:18,640
Speaker 1: that window of time right his stuff was shifting was just.

1219
00:56:18,920 --> 00:56:22,800
Speaker 5: Amazing, incredible, incredible, Yeah, it was, it was.

1220
00:56:23,000 --> 00:56:25,640
Speaker 2: It was the best eighties, that's h That's the reason

1221
00:56:25,680 --> 00:56:28,319
Speaker 2: I signed up for the Columbia Record House.

1222
00:56:28,680 --> 00:56:29,960
Speaker 3: Dude, I did that a bunch of times.

1223
00:56:30,800 --> 00:56:34,120
Speaker 1: And I remember heroll date me, Alex Egan is producing,

1224
00:56:34,200 --> 00:56:36,160
Speaker 1: is going to stroke out when I say this. I

1225
00:56:36,400 --> 00:56:39,880
Speaker 1: when they transitioned to CDs and I was still having cassettes,

1226
00:56:39,960 --> 00:56:42,279
Speaker 1: I joined all those record clubs and kept getting the

1227
00:56:42,320 --> 00:56:44,440
Speaker 1: introductory because I didn't want to buy a CD player

1228
00:56:44,520 --> 00:56:46,759
Speaker 1: with my Putt Putt Earning money and hair Arena money

1229
00:56:47,160 --> 00:56:48,279
Speaker 1: and not and not be.

1230
00:56:48,360 --> 00:56:49,160
Speaker 3: Able to play it.

1231
00:56:49,440 --> 00:56:51,160
Speaker 1: It was like, what So I bought a whole bunch

1232
00:56:51,200 --> 00:56:53,959
Speaker 1: of CDs before I got the Kenwood CD player, because

1233
00:56:53,960 --> 00:56:58,120
Speaker 1: I mean, what would be the point, you know, right? So, yeah,

1234
00:56:58,120 --> 00:57:00,840
Speaker 1: beautiful Tony. I appreciate the call me thank you. And

1235
00:57:00,920 --> 00:57:03,239
Speaker 1: then of course the CDs ended up at one time

1236
00:57:03,360 --> 00:57:06,080
Speaker 1: or another and people are embracing those again. They sort

1237
00:57:06,120 --> 00:57:08,839
Speaker 1: of turned into like a Christmas tree or Hanka bush,

1238
00:57:09,080 --> 00:57:11,760
Speaker 1: you know, ornament because they're nice and shining. Yeah, exactly,

1239
00:57:12,280 --> 00:57:15,279
Speaker 1: Alex knows to Western Hills and Rick, and then we

1240
00:57:15,360 --> 00:57:17,560
Speaker 1: got Don and Jim and Allen and still no women calling.

1241
00:57:17,600 --> 00:57:19,760
Speaker 1: I'm gonna have to put the kibosh on this topic

1242
00:57:20,080 --> 00:57:22,720
Speaker 1: because I feel like the women are We don't want

1243
00:57:22,760 --> 00:57:23,600
Speaker 1: to alienate the women.

1244
00:57:23,680 --> 00:57:25,400
Speaker 3: We want to bring the women into the fold. Rick,

1245
00:57:25,440 --> 00:57:25,880
Speaker 3: what do you have?

1246
00:57:26,680 --> 00:57:30,880
Speaker 2: Hi? Well, I like Neil Young Russ never sleeps for men.

1247
00:57:31,000 --> 00:57:32,240
Speaker 5: The song sail Away on there.

1248
00:57:32,280 --> 00:57:34,760
Speaker 2: I think it's great. And then I wasn't gonna say.

1249
00:57:34,680 --> 00:57:37,080
Speaker 15: Dark Side of the Moon, but I was thinking about it.

1250
00:57:37,480 --> 00:57:40,200
Speaker 15: Obscured by Clouds, the album that came out before Dark

1251
00:57:40,240 --> 00:57:40,840
Speaker 15: Side of the Moon.

1252
00:57:41,320 --> 00:57:42,120
Speaker 2: That's really good.

1253
00:57:42,200 --> 00:57:44,680
Speaker 15: That's that's got a bunch of really nice songs on it.

1254
00:57:44,760 --> 00:57:46,720
Speaker 15: And the last one was Bruce Springsteen, the one with

1255
00:57:46,800 --> 00:57:47,680
Speaker 15: thunder Road on there.

1256
00:57:47,800 --> 00:57:49,480
Speaker 2: Oh yeah, I like that one.

1257
00:57:50,080 --> 00:57:52,600
Speaker 1: Yeah, Springsteen's pretty amazing. And that's another one of those.

1258
00:57:52,800 --> 00:57:55,240
Speaker 1: And I may sound obnoxious about it, Rick, I appreciate

1259
00:57:55,280 --> 00:57:58,040
Speaker 1: the call. But as a kid, Springsteen everybody talked about

1260
00:57:58,080 --> 00:58:01,360
Speaker 1: as being and again, I think it's that anti establishment

1261
00:58:01,440 --> 00:58:04,480
Speaker 1: rebellion teenager kind of vibe. I knew a lot of

1262
00:58:04,520 --> 00:58:07,880
Speaker 1: people's parents were like, Springsteen's the Boss. You gotta love Springsteen.

1263
00:58:07,920 --> 00:58:10,520
Speaker 1: And I'm like, yeah, enough already, you know fine, I

1264
00:58:10,640 --> 00:58:13,840
Speaker 1: wanted something else and then I came back and was like, oh,

1265
00:58:13,920 --> 00:58:18,640
Speaker 1: these are really incredible songs. And so no disrespect, I

1266
00:58:18,720 --> 00:58:21,440
Speaker 1: mean all all the hats off to Springsteen doing what

1267
00:58:21,520 --> 00:58:26,160
Speaker 1: he does. What where's a Jim Jim here with Sterling

1268
00:58:26,240 --> 00:58:26,920
Speaker 1: on Metamora?

1269
00:58:27,000 --> 00:58:30,040
Speaker 3: There you go? Was Stirling on seven hundred WLW Jim.

1270
00:58:29,920 --> 00:58:30,480
Speaker 7: What do you have? Man?

1271
00:58:31,600 --> 00:58:31,760
Speaker 2: Yeah?

1272
00:58:31,920 --> 00:58:33,080
Speaker 7: Good anthnoon staring Hey.

1273
00:58:33,240 --> 00:58:36,560
Speaker 16: First off off, say at DC back in black Yes,

1274
00:58:36,760 --> 00:58:39,680
Speaker 16: and uh big fan got to see them live in

1275
00:58:39,840 --> 00:58:42,440
Speaker 16: the lat the Buddhicon in Tokyo, Japan.

1276
00:58:42,680 --> 00:58:43,400
Speaker 3: Oh, you're kidding.

1277
00:58:43,800 --> 00:58:46,720
Speaker 1: I've seen I've seen so many videos and heard so

1278
00:58:46,800 --> 00:58:48,800
Speaker 1: many albums from Cheap Tricks to on and on and

1279
00:58:48,920 --> 00:58:49,880
Speaker 1: on about the Budicon.

1280
00:58:50,000 --> 00:58:51,440
Speaker 3: What was it like to be in that building.

1281
00:58:52,800 --> 00:58:57,840
Speaker 16: It's very strange to be whether Japanese audience or predominantly

1282
00:58:58,040 --> 00:59:02,440
Speaker 16: Japanese because they're so quiet. And Angus gave him a

1283
00:59:02,440 --> 00:59:04,240
Speaker 16: good talking to and then he got a little rowdy,

1284
00:59:04,320 --> 00:59:06,320
Speaker 16: but great, great venue it was.

1285
00:59:07,040 --> 00:59:07,760
Speaker 11: It was quite a lot.

1286
00:59:07,880 --> 00:59:09,520
Speaker 2: I was in the Navy station over there.

1287
00:59:09,880 --> 00:59:12,480
Speaker 10: We had a great time, so loved that.

1288
00:59:12,960 --> 00:59:15,960
Speaker 16: The second album I would say is Dark Side of

1289
00:59:16,000 --> 00:59:19,880
Speaker 16: the Moon Pink Floyd. And then finally one that nobody

1290
00:59:20,000 --> 00:59:23,120
Speaker 16: has mentioned or a group anything by the Moody Blues.

1291
00:59:23,240 --> 00:59:25,600
Speaker 1: Oh yeah, Moody Glues Crank Pretty grow too. Yeah, that's

1292
00:59:25,600 --> 00:59:26,760
Speaker 1: pretty strong, Jim. I appreciate it.

1293
00:59:26,800 --> 00:59:27,160
Speaker 6: Call you know what.

1294
00:59:27,400 --> 00:59:29,680
Speaker 1: And I don't know now so much with the change

1295
00:59:29,720 --> 00:59:33,880
Speaker 1: and the way people are consuming entertainment in music in

1296
00:59:34,000 --> 00:59:38,320
Speaker 1: some cases, but for the longest time, every single year

1297
00:59:38,600 --> 00:59:42,040
Speaker 1: ac DC's Back in Black record was selling like a

1298
00:59:42,200 --> 00:59:45,400
Speaker 1: million copies. I don't know if people wore them out

1299
00:59:45,920 --> 00:59:47,760
Speaker 1: or what was happening, But I mean it was just

1300
00:59:47,880 --> 00:59:49,800
Speaker 1: a year after year they were just going thank you,

1301
00:59:50,400 --> 00:59:52,680
Speaker 1: thanks for the money check, thanks for the deposit. It

1302
00:59:52,800 --> 00:59:55,600
Speaker 1: was tremendous, Jim, I appreciate the call. And that sort

1303
00:59:55,640 --> 00:59:58,080
Speaker 1: of worked the same way with some of the skinnered

1304
00:59:58,120 --> 01:00:01,000
Speaker 1: stuff too. I mean, just million records, because you know

1305
01:00:01,080 --> 01:00:04,160
Speaker 1: what it is, it's about the songs. They are incredibly

1306
01:00:04,360 --> 01:00:08,960
Speaker 1: well written, relatable songs that an emotional visceral level and

1307
01:00:09,080 --> 01:00:12,200
Speaker 1: whether it's and this one's nice too, this is for Mark,

1308
01:00:12,480 --> 01:00:15,000
Speaker 1: and Oakley says, what about the Diary of Alicia Keys

1309
01:00:15,040 --> 01:00:17,800
Speaker 1: and that goes back to the early two thousands, and

1310
01:00:17,960 --> 01:00:20,440
Speaker 1: what an amazing record and what a talented woman she

1311
01:00:20,640 --> 01:00:22,400
Speaker 1: still is out there doing it too. It's it's just

1312
01:00:22,520 --> 01:00:25,760
Speaker 1: tremendous to Preble County, Don. And then Alan was Sterling

1313
01:00:25,800 --> 01:00:27,600
Speaker 1: on seven hundred WLW, what's going on?

1314
01:00:27,800 --> 01:00:28,000
Speaker 14: Don?

1315
01:00:28,840 --> 01:00:29,440
Speaker 6: Hey Sterling?

1316
01:00:29,640 --> 01:00:32,240
Speaker 17: Hey, well you guys been stealing my thunder on Pink Floyd.

1317
01:00:32,360 --> 01:00:35,040
Speaker 17: I definitely agree with that, but really a couple others,

1318
01:00:35,240 --> 01:00:38,320
Speaker 17: the Almond Brothers Live at the film More East. Sure

1319
01:00:39,320 --> 01:00:42,080
Speaker 17: you don't need any other album by the Almond Brothers

1320
01:00:42,120 --> 01:00:44,400
Speaker 17: if you have that, and the Door is La.

1321
01:00:44,360 --> 01:00:47,400
Speaker 3: Woman, Yeah, also nice man. I love the doors. That's

1322
01:00:47,440 --> 01:00:49,520
Speaker 3: tremendous stuff. It is amazing.

1323
01:00:49,680 --> 01:00:51,440
Speaker 1: Well do you remember the first record you bought her?

1324
01:00:51,440 --> 01:00:53,520
Speaker 1: Album or CD, cassette, whatever.

1325
01:00:55,200 --> 01:00:56,720
Speaker 5: Yeah, it was The Yardbirds.

1326
01:00:57,200 --> 01:01:00,280
Speaker 3: Okay, very cool, very first The Yardbirds.

1327
01:01:00,200 --> 01:01:02,720
Speaker 17: Man, it was pretty good. That's pretty good. Speaking of

1328
01:01:02,760 --> 01:01:06,480
Speaker 17: the Arbord, something else most people don't know, John Mayle

1329
01:01:08,400 --> 01:01:11,720
Speaker 17: an album called wake Up Call. He's been around, he's

1330
01:01:11,760 --> 01:01:14,560
Speaker 17: been around forever, but he's about Yeah.

1331
01:01:14,680 --> 01:01:17,160
Speaker 1: Absolutely, I agree with he's not quite that old. But no,

1332
01:01:17,240 --> 01:01:18,920
Speaker 1: I totally got you done. I appreciate the call Me

1333
01:01:19,040 --> 01:01:23,480
Speaker 1: My Male. It's fantastic. Yeah, that's great. And Allison's changed stuff.

1334
01:01:23,920 --> 01:01:26,320
Speaker 1: I mean, that whole vibe changed me. I mean the

1335
01:01:26,560 --> 01:01:31,280
Speaker 1: Dirt record unbelievable. In Marnie, I think I'm saying that correctly,

1336
01:01:31,400 --> 01:01:36,520
Speaker 1: Marne Marnee, yeah, uh says Alana's is jagged little pill, tremendous.

1337
01:01:36,560 --> 01:01:39,160
Speaker 1: And then that was again the middle nineties, right in

1338
01:01:39,240 --> 01:01:41,080
Speaker 1: that sweet spot with a lot of stuff, middle late

1339
01:01:41,120 --> 01:01:43,520
Speaker 1: and it was in ninety six, ninety seven, ninety eight,

1340
01:01:43,600 --> 01:01:47,800
Speaker 1: somewhere in there, a tremendous record, song after song after song,

1341
01:01:48,160 --> 01:01:50,760
Speaker 1: and I think, if I'm not mistaken, she started on

1342
01:01:50,920 --> 01:01:53,600
Speaker 1: like a kids show too. At some point to Brown

1343
01:01:53,680 --> 01:01:56,280
Speaker 1: County and Allen was Sterling on seven hundred WLW.

1344
01:01:57,520 --> 01:01:59,880
Speaker 7: He's Sterling and great show, great topic.

1345
01:02:00,080 --> 01:02:00,440
Speaker 3: Thank you man.

1346
01:02:00,480 --> 01:02:01,000
Speaker 2: I appreciate it.

1347
01:02:01,160 --> 01:02:04,800
Speaker 7: So I got h I got three kids, and once

1348
01:02:04,840 --> 01:02:07,920
Speaker 7: they hit about fourteen, they started, you know, kind of

1349
01:02:08,040 --> 01:02:11,200
Speaker 7: getting interested in dad's music, you know, riding around, playing

1350
01:02:11,200 --> 01:02:14,240
Speaker 7: stuff in the car and whatnot. And one one kid

1351
01:02:14,320 --> 01:02:17,760
Speaker 7: in particular, really got into it, started getting real curious.

1352
01:02:18,400 --> 01:02:21,080
Speaker 7: I said, look, this is like, uh, this is like

1353
01:02:21,600 --> 01:02:23,320
Speaker 7: hard rock one oh one.

1354
01:02:23,680 --> 01:02:24,480
Speaker 2: You got to listen to.

1355
01:02:24,560 --> 01:02:28,840
Speaker 7: Queen's Reich Operation mind Crime. Now it's not a fun

1356
01:02:29,280 --> 01:02:32,360
Speaker 7: it's it all flows like one long song because it's

1357
01:02:32,440 --> 01:02:36,000
Speaker 7: like a rock opera, correct, But uh, the entire thing

1358
01:02:36,080 --> 01:02:39,240
Speaker 7: from start to finish is must listen. And it's because

1359
01:02:39,280 --> 01:02:43,520
Speaker 7: of Jeff Tate. The vocalist is just clinically or you know,

1360
01:02:44,440 --> 01:02:48,000
Speaker 7: like just uh, just a whole different animal as far

1361
01:02:48,040 --> 01:02:49,120
Speaker 7: as vocals go.

1362
01:02:49,520 --> 01:02:50,480
Speaker 2: So that's my choice.

1363
01:02:50,960 --> 01:02:53,360
Speaker 1: That's that's tremendous. And Alan, I got to tell you,

1364
01:02:53,400 --> 01:02:56,080
Speaker 1: I appreciate the call. Jeff Tate actually is coming and

1365
01:02:56,160 --> 01:02:59,360
Speaker 1: he's doing that Operation mind Crime album so called Final

1366
01:02:59,480 --> 01:03:03,160
Speaker 1: Chapter back to back, and here's an amazing thing. And

1367
01:03:03,240 --> 01:03:07,280
Speaker 1: you'll be here at the Tap on the fifteenth of May,

1368
01:03:07,960 --> 01:03:10,960
Speaker 1: and then he'll drive up seventy five to seventy and

1369
01:03:11,080 --> 01:03:14,080
Speaker 1: go to Hubert Heights and just outside Dayton and he'll

1370
01:03:14,120 --> 01:03:16,840
Speaker 1: do it there at the Rose Music Center. So that's tremendous.

1371
01:03:17,160 --> 01:03:19,920
Speaker 1: I have an embarrassing story, Alan, I appreciate the call.

1372
01:03:22,160 --> 01:03:27,400
Speaker 1: It's going back to about ninety eight, I think somewhere

1373
01:03:27,440 --> 01:03:32,480
Speaker 1: in that window, Quaint Trayke was playing the Gardens and

1374
01:03:34,560 --> 01:03:36,720
Speaker 1: I had to take care of some business for the

1375
01:03:36,840 --> 01:03:39,280
Speaker 1: radio station and went and one I was going to

1376
01:03:39,360 --> 01:03:44,080
Speaker 1: the show as well, and I had to deal with

1377
01:03:44,160 --> 01:03:47,600
Speaker 1: the getting stuff signed for, like auction for EBN or whatever.

1378
01:03:48,240 --> 01:03:51,440
Speaker 1: And I'm hanging out there and I'm seeing the guys,

1379
01:03:51,480 --> 01:03:53,680
Speaker 1: and I'm there with a date. She's hanging out with me,

1380
01:03:54,040 --> 01:03:56,600
Speaker 1: and a buddy of mine goes hey. And a lot

1381
01:03:56,640 --> 01:03:58,640
Speaker 1: of times you get a chance and get spoiled. You

1382
01:03:58,680 --> 01:04:00,800
Speaker 1: can sometimes watch the show from the side of the

1383
01:04:00,840 --> 01:04:03,000
Speaker 1: stage or whatever else. I mean, you know, it's just

1384
01:04:03,080 --> 01:04:05,000
Speaker 1: one of those things, and they're okay, you just get

1385
01:04:05,040 --> 01:04:07,720
Speaker 1: over here. And then they rushed us onto the stage

1386
01:04:08,160 --> 01:04:10,840
Speaker 1: because that tour they had a bar that was set

1387
01:04:10,960 --> 01:04:13,160
Speaker 1: up on the stage as they were working and doing it,

1388
01:04:13,800 --> 01:04:16,360
Speaker 1: and it was the most embarrassing thing in the other

1389
01:04:16,440 --> 01:04:18,880
Speaker 1: They wanted us to get up and dance after sitting

1390
01:04:18,960 --> 01:04:21,080
Speaker 1: at the bar. I don't know if anybody it was

1391
01:04:21,120 --> 01:04:22,600
Speaker 1: a big crowd, so I know a lot of people

1392
01:04:22,640 --> 01:04:27,040
Speaker 1: were there, but she was so petrified and horrified of

1393
01:04:27,120 --> 01:04:29,280
Speaker 1: the circumstance. And I didn't want to be on stage.

1394
01:04:29,400 --> 01:04:30,160
Speaker 1: I'm not there.

1395
01:04:30,280 --> 01:04:32,680
Speaker 3: Nobody paid to see me. I just wanted to see

1396
01:04:32,760 --> 01:04:33,640
Speaker 3: Queen's Right work.

1397
01:04:34,400 --> 01:04:36,720
Speaker 1: So that is a memory that I will never forget

1398
01:04:36,800 --> 01:04:38,720
Speaker 1: in relation to the great band of Queen's Right and

1399
01:04:38,800 --> 01:04:40,480
Speaker 1: Jeff Take going to be in town come may.

1400
01:04:40,880 --> 01:04:41,760
Speaker 3: So thanks for the call.

1401
01:04:41,840 --> 01:04:44,080
Speaker 1: That's great, fantastic calls in this Normally I just do

1402
01:04:44,200 --> 01:04:46,200
Speaker 1: one segment, but it was overwhelming and.

1403
01:04:47,880 --> 01:04:49,280
Speaker 3: Not enough women getting interacted to.

1404
01:04:49,400 --> 01:04:53,800
Speaker 1: And here's from psy Coolnessness or since the coolestness on

1405
01:04:54,120 --> 01:04:58,160
Speaker 1: ex Jim Body says Sterling Lord's album Melodrama best album

1406
01:04:58,240 --> 01:05:01,720
Speaker 1: of my lifetime nominated, It was for a Grammy and

1407
01:05:01,760 --> 01:05:05,400
Speaker 1: should have won Graceland by Paul Simon also up there too. Yeah, Jim,

1408
01:05:05,480 --> 01:05:09,040
Speaker 1: I cannot disagree quite break. Come back your two o'clock

1409
01:05:09,120 --> 01:05:12,120
Speaker 1: report coming up sooner than later, and I'll ask you

1410
01:05:12,240 --> 01:05:16,000
Speaker 1: this too about the issue of artificial intelligence. Conversation I

1411
01:05:16,120 --> 01:05:18,840
Speaker 1: had with the director of AI Strategy and the Perry

1412
01:05:18,960 --> 01:05:22,280
Speaker 1: Professor at the University of Cincinnati's Lendinger College of Business

1413
01:05:22,640 --> 01:05:26,840
Speaker 1: gonna be on after the two thirty report, So hang out.

1414
01:05:26,840 --> 01:05:28,760
Speaker 1: In less than an hour, we'll talk about the future

1415
01:05:28,800 --> 01:05:31,440
Speaker 1: are of AI and how you and your kids and

1416
01:05:31,600 --> 01:05:35,440
Speaker 1: I can stay relevant, employable, earning and having purpose in

1417
01:05:35,560 --> 01:05:38,840
Speaker 1: life as II in a very short amount of time

1418
01:05:39,440 --> 01:05:42,440
Speaker 1: is going to drastically change the way we live in

1419
01:05:42,560 --> 01:05:45,560
Speaker 1: the world in which we observe and try to walk

1420
01:05:45,600 --> 01:05:46,600
Speaker 1: on a regular basis.

1421
01:05:46,800 --> 01:05:47,200
Speaker 6: Hang out.

1422
01:05:47,240 --> 01:05:51,280
Speaker 1: There's more sterling coming up on seven hundred WLW Sunday,

1423
01:05:51,320 --> 01:05:55,120
Speaker 1: isn't It is hard to keep track for some reason.

1424
01:05:55,200 --> 01:05:59,400
Speaker 1: Then Shawn's Mike Song, Sewan there is he's here now,

1425
01:06:00,800 --> 01:06:02,120
Speaker 1: Sean McMahon hanging out.

1426
01:06:02,360 --> 01:06:04,320
Speaker 3: Russ Jackson here producing as well.

1427
01:06:05,000 --> 01:06:08,520
Speaker 1: Later on conversation about the future and AI Director of

1428
01:06:08,560 --> 01:06:12,160
Speaker 1: AI Strategy, Perry Professor, University of Cincinnati's Lendard College of Business,

1429
01:06:12,240 --> 01:06:14,200
Speaker 1: Jeff Shaffer, going to talk about how we can skill

1430
01:06:14,360 --> 01:06:17,200
Speaker 1: up so we're not left behind and confused and with

1431
01:06:17,360 --> 01:06:19,760
Speaker 1: no purpose in life. In a matter of what they

1432
01:06:19,840 --> 01:06:22,800
Speaker 1: say within five years, the whole world will be drastically different.

1433
01:06:23,080 --> 01:06:26,040
Speaker 1: And speaking of you see, it will look drastically different.

1434
01:06:26,080 --> 01:06:27,680
Speaker 1: You see how I'm a professional. I just tied that

1435
01:06:27,800 --> 01:06:28,560
Speaker 1: right in the That was good.

1436
01:06:28,680 --> 01:06:29,040
Speaker 4: I like that.

1437
01:06:29,160 --> 01:06:32,320
Speaker 3: I went to school not for this but other stuff.

1438
01:06:32,760 --> 01:06:38,080
Speaker 1: Anyway, what's amazing is one of the most some would

1439
01:06:38,120 --> 01:06:42,280
Speaker 1: call it a brutalist bit of architecture on the University

1440
01:06:42,320 --> 01:06:46,240
Speaker 1: of Cincinnati campus. That Crossley Tower is to some been

1441
01:06:46,280 --> 01:06:48,600
Speaker 1: one of the most amazing things that they could ever see.

1442
01:06:48,920 --> 01:06:52,720
Speaker 1: It's something that they look at fondly. Others have from

1443
01:06:53,240 --> 01:06:56,720
Speaker 1: the very beginning disturbed by it, troubled by it, felt uncomfortable,

1444
01:06:56,960 --> 01:07:00,400
Speaker 1: and arguably the style of architecture is maybe hard to

1445
01:07:00,440 --> 01:07:03,680
Speaker 1: deal with just in general, hence the term brutalist. But

1446
01:07:03,840 --> 01:07:05,840
Speaker 1: that one of the things that's amazing to me, and

1447
01:07:05,880 --> 01:07:08,000
Speaker 1: it's coming down is why we're talking about it here

1448
01:07:08,040 --> 01:07:10,960
Speaker 1: for a minute, and about that change, as I understand,

1449
01:07:11,160 --> 01:07:14,200
Speaker 1: and Sean McMahon, who's the producer here but obviously a

1450
01:07:14,320 --> 01:07:16,800
Speaker 1: UC guy into work there, is going to school there,

1451
01:07:16,840 --> 01:07:19,040
Speaker 1: done a little of everything. The thing that blew me

1452
01:07:19,120 --> 01:07:21,960
Speaker 1: away about this it is the second largest, as it's

1453
01:07:22,000 --> 01:07:26,680
Speaker 1: been reported, single concrete poor thing ever done in the

1454
01:07:26,800 --> 01:07:29,120
Speaker 1: United States. As I understand it pretty amazing, right, that

1455
01:07:29,320 --> 01:07:33,240
Speaker 1: is hard to process, Yeah, from a building that people occupied. Yeah,

1456
01:07:33,520 --> 01:07:35,680
Speaker 1: so I mean that's a crazy thing that it is,

1457
01:07:35,840 --> 01:07:38,480
Speaker 1: just no pieces in parts, just keep bringing those trucks.

1458
01:07:38,600 --> 01:07:38,760
Speaker 2: Yeah.

1459
01:07:38,920 --> 01:07:43,680
Speaker 18: Yeah, eighteen straight continuous days they poured that building. That's crazy.

1460
01:07:43,720 --> 01:07:45,560
Speaker 18: I've never like, have you ever heard of construction like that.

1461
01:07:46,000 --> 01:07:48,000
Speaker 18: From what I read, apparent it was the same process

1462
01:07:48,040 --> 01:07:51,080
Speaker 18: they used to build like bunkers along the East Coast

1463
01:07:51,160 --> 01:07:53,920
Speaker 18: during the Second World War. Yep, So they just applied

1464
01:07:54,040 --> 01:07:57,960
Speaker 18: the same you know, techniques to this building on along

1465
01:07:58,120 --> 01:08:01,280
Speaker 18: MLK and it's it's it's funny because it was named

1466
01:08:01,320 --> 01:08:05,120
Speaker 18: one of America's like ugliest buildings on university campuses. Yeah,

1467
01:08:05,160 --> 01:08:07,000
Speaker 18: and like from I mean, I guess, I guess it

1468
01:08:07,080 --> 01:08:10,160
Speaker 18: always had a cult following, but like from that point forward,

1469
01:08:10,760 --> 01:08:13,160
Speaker 18: it developed an even deeper cult following, and I was

1470
01:08:13,240 --> 01:08:15,400
Speaker 18: proudly part of that. It was like the mid twenty

1471
01:08:15,480 --> 01:08:17,680
Speaker 18: teens or late twenty teens when that article came out,

1472
01:08:17,720 --> 01:08:19,639
Speaker 18: I want to say, it's like twenty seventeen or something

1473
01:08:19,760 --> 01:08:21,639
Speaker 18: like that, and I fell in love with the building

1474
01:08:21,720 --> 01:08:24,599
Speaker 18: because to me, yeah, it might be ugly, but it's

1475
01:08:24,640 --> 01:08:26,920
Speaker 18: a it's a fun kind of ugly, like it's it's

1476
01:08:26,960 --> 01:08:31,000
Speaker 18: a very distinct, unique kind of ugly that it's you

1477
01:08:31,080 --> 01:08:33,080
Speaker 18: can't find it anywhere else. And you mentioned it's the

1478
01:08:33,200 --> 01:08:37,120
Speaker 18: second largest continuoency pored structure in America or the world,

1479
01:08:37,200 --> 01:08:39,639
Speaker 18: I think, behind the Hoover Dam.

1480
01:08:39,880 --> 01:08:41,600
Speaker 3: Yeah, so literally a dam that.

1481
01:08:41,680 --> 01:08:45,120
Speaker 18: Holds back however, many tons of hundreds of thousands of

1482
01:08:45,160 --> 01:08:45,960
Speaker 18: tons of water, not.

1483
01:08:46,040 --> 01:08:47,599
Speaker 1: As much as it used to, not as much as

1484
01:08:47,600 --> 01:08:49,800
Speaker 1: you used to. See that bathroom going up there, You're like,

1485
01:08:49,840 --> 01:08:50,760
Speaker 1: oh my, where the water go?

1486
01:08:51,040 --> 01:08:51,519
Speaker 3: We drank it?

1487
01:08:51,640 --> 01:08:52,559
Speaker 5: Yeah, yeah we did.

1488
01:08:52,600 --> 01:08:53,400
Speaker 3: We got a little thirsty.

1489
01:08:53,520 --> 01:08:56,599
Speaker 18: Yeah yeah, we needed it. But it's it's such an

1490
01:08:56,840 --> 01:09:00,320
Speaker 18: iconic building. And even people start showing up to football games.

1491
01:09:00,320 --> 01:09:02,400
Speaker 18: There was always at least one guy you could see

1492
01:09:02,479 --> 01:09:04,400
Speaker 18: wearing a Crosley Tower hat.

1493
01:09:04,600 --> 01:09:06,840
Speaker 1: I've never even paid attention to notice that, and I've

1494
01:09:06,880 --> 01:09:10,200
Speaker 1: been i think two games, and that's right. Seriously, people

1495
01:09:10,240 --> 01:09:12,439
Speaker 1: show up where it's I mean, it's mainly I think

1496
01:09:12,479 --> 01:09:15,120
Speaker 1: it's just one guy or two guys, but architecture people,

1497
01:09:15,200 --> 01:09:18,479
Speaker 1: I'm guessing probably, or just you know, casual appreciators. It

1498
01:09:18,760 --> 01:09:20,040
Speaker 1: looked like a real I don't know what it was

1499
01:09:20,080 --> 01:09:22,160
Speaker 1: made of like paper mache or something, and it could

1500
01:09:22,160 --> 01:09:24,000
Speaker 1: have been too heavy because you know you're wearing it

1501
01:09:24,040 --> 01:09:25,560
Speaker 1: on your head, probably not concrete, you know what. You

1502
01:09:25,680 --> 01:09:27,840
Speaker 1: don't want to continue to support a concrete on your head, right,

1503
01:09:28,000 --> 01:09:30,760
Speaker 1: So yeah, I saw a guy wearing a hat at

1504
01:09:30,840 --> 01:09:32,800
Speaker 1: football games all the time, and it just it just

1505
01:09:32,920 --> 01:09:35,240
Speaker 1: became iconic. And it was sad because I think my

1506
01:09:35,280 --> 01:09:37,880
Speaker 1: freshman year of college was when they initially were like, Okay, yeah,

1507
01:09:37,920 --> 01:09:40,559
Speaker 1: we are we are definitely making plans to tear down.

1508
01:09:40,760 --> 01:09:43,639
Speaker 1: Is it falling down or or why are they getting

1509
01:09:43,720 --> 01:09:45,960
Speaker 1: rid of it? Because I know everything changes and it

1510
01:09:46,080 --> 01:09:48,040
Speaker 1: is an amazing campus. And one of the things that's

1511
01:09:48,080 --> 01:09:50,120
Speaker 1: cool about you see from my experience and you went

1512
01:09:50,160 --> 01:09:52,439
Speaker 1: to school there, Sewan, you live there, breath there or everything, uh,

1513
01:09:53,160 --> 01:09:57,479
Speaker 1: is that you can see these changes and experimentation and

1514
01:09:57,680 --> 01:10:00,200
Speaker 1: new kinds of looks of buildings that even sometimes don't

1515
01:10:00,200 --> 01:10:02,680
Speaker 1: look like they should somehow stand right right. But a

1516
01:10:02,800 --> 01:10:06,000
Speaker 1: lot of people, uh, I just don't understand why it's

1517
01:10:06,040 --> 01:10:06,439
Speaker 1: going away.

1518
01:10:06,760 --> 01:10:09,519
Speaker 18: So the reason that it's going away is because it's

1519
01:10:09,640 --> 01:10:12,559
Speaker 18: just structurally it's having so many issues. The biggest thing

1520
01:10:12,720 --> 01:10:16,200
Speaker 18: is that it's actually sinking into the dirt. So it's

1521
01:10:16,280 --> 01:10:19,439
Speaker 18: actually like the ground below it is actually moving and

1522
01:10:19,560 --> 01:10:22,479
Speaker 18: it's starting to kind of fall one direction, so it's

1523
01:10:22,560 --> 01:10:25,120
Speaker 18: just not stable. The concrete's kind of coming apart. Yeah,

1524
01:10:25,120 --> 01:10:27,439
Speaker 18: a little bit. The Lenning Tower of Sinsey. Yeah, you

1525
01:10:27,479 --> 01:10:29,360
Speaker 18: don't want that. You don't want that dangling over a

1526
01:10:29,479 --> 01:10:32,280
Speaker 18: major road, like Martin Luther King Junior. You know, like,

1527
01:10:32,360 --> 01:10:34,400
Speaker 18: you don't you don't want that. That's not good for drivers.

1528
01:10:34,439 --> 01:10:37,519
Speaker 18: But yeah, like it's sinking into the ground, it's having

1529
01:10:37,640 --> 01:10:42,439
Speaker 18: major plumbing issues. Basically, the cost of repair would far

1530
01:10:42,640 --> 01:10:45,280
Speaker 18: outweigh the cost of just let's just get rid of it.

1531
01:10:45,439 --> 01:10:48,000
Speaker 18: And from what I understand, UC did not want the

1532
01:10:48,040 --> 01:10:49,960
Speaker 18: building from the beginning. They saw it and they were like,

1533
01:10:50,960 --> 01:10:52,720
Speaker 18: oh man, I can't wait till that comes down. So

1534
01:10:52,760 --> 01:10:54,760
Speaker 18: they've been looking for an excuse for a long time

1535
01:10:54,880 --> 01:10:56,439
Speaker 18: to get rid of it. So they hated it the

1536
01:10:56,479 --> 01:10:58,320
Speaker 18: way a lot of other people hate her. Yeah, yeah,

1537
01:10:58,439 --> 01:11:00,720
Speaker 18: that's kind of odd that they would have it there

1538
01:11:01,040 --> 01:11:03,760
Speaker 18: and it became what it is and they build it.

1539
01:11:03,920 --> 01:11:07,120
Speaker 18: It was okay, it was cost was you know, accounting

1540
01:11:07,240 --> 01:11:10,080
Speaker 18: was done, they budgeted it, they build it, yet they

1541
01:11:10,160 --> 01:11:10,720
Speaker 18: didn't want it.

1542
01:11:10,880 --> 01:11:11,920
Speaker 3: Yeah, how does that happen.

1543
01:11:11,960 --> 01:11:12,280
Speaker 2: I don't know.

1544
01:11:12,439 --> 01:11:14,040
Speaker 3: They just I guess they saw the final result and

1545
01:11:14,080 --> 01:11:16,040
Speaker 3: they're like, you know, I don't know.

1546
01:11:16,160 --> 01:11:17,560
Speaker 18: It's like you get a new piece of furniture in

1547
01:11:17,600 --> 01:11:19,160
Speaker 18: your house and you're like, that looks awful, but I

1548
01:11:19,240 --> 01:11:21,600
Speaker 18: can't return it now, Like what am I supposed to do?

1549
01:11:21,840 --> 01:11:23,800
Speaker 1: Yeah, that's a bad place to be. I got to

1550
01:11:23,840 --> 01:11:25,400
Speaker 1: make the best of it. Easier to get rid of

1551
01:11:25,439 --> 01:11:29,439
Speaker 1: a chair than the massive building exactly exactly, So will

1552
01:11:29,479 --> 01:11:32,800
Speaker 1: people celebrate this? I mean, because I mean you think

1553
01:11:32,800 --> 01:11:34,680
Speaker 1: about it when they've had other buildings, if they're going

1554
01:11:34,800 --> 01:11:37,880
Speaker 1: are they going to do like an implosion kind of

1555
01:11:37,920 --> 01:11:39,720
Speaker 1: scenario or how are they going.

1556
01:11:39,720 --> 01:11:39,920
Speaker 2: To do it?

1557
01:11:40,040 --> 01:11:41,880
Speaker 18: So the way that they're doing it is they're actually

1558
01:11:41,960 --> 01:11:44,599
Speaker 18: taking it down floor by floor, so they're basically cutting

1559
01:11:44,640 --> 01:11:47,200
Speaker 18: it into sections, and then I guess they're just lowering

1560
01:11:47,280 --> 01:11:49,840
Speaker 18: it down to trucks below. Because if you were to

1561
01:11:49,880 --> 01:11:52,080
Speaker 18: blow up that much concrete, you would have a lot

1562
01:11:52,120 --> 01:11:53,360
Speaker 18: of issues. First of all, you need a lot of

1563
01:11:53,400 --> 01:11:55,719
Speaker 18: expl you'd need a lot of explosive you know, TNT.

1564
01:11:55,880 --> 01:11:58,439
Speaker 18: It's just kind of the fun part of you know,

1565
01:11:58,439 --> 01:12:01,160
Speaker 18: people who do the demo work tremendous. Yeah, I mean

1566
01:12:01,200 --> 01:12:03,640
Speaker 18: that's fun, right. The only issue is that you have

1567
01:12:03,840 --> 01:12:07,160
Speaker 18: so many buildings directly next to Crasley Tower. You got

1568
01:12:07,240 --> 01:12:09,479
Speaker 18: DAP right there. I think Vashaw Hall is right next

1569
01:12:09,479 --> 01:12:11,200
Speaker 18: to it. You got a parking garage attached to it.

1570
01:12:11,600 --> 01:12:13,400
Speaker 18: So you have to take it down in pieces again

1571
01:12:13,400 --> 01:12:16,640
Speaker 18: because it's so much concrete. Uh So in terms of

1572
01:12:17,360 --> 01:12:19,600
Speaker 18: like people, I don't know about celebrating it, Like I

1573
01:12:19,640 --> 01:12:22,599
Speaker 18: think people celebrate the quote unquote life of Crasley Tower

1574
01:12:22,640 --> 01:12:25,960
Speaker 18: because it's fifty seven years old this year. Wo first

1575
01:12:26,000 --> 01:12:30,280
Speaker 18: open in nineteen fifty seven, and it was home to

1576
01:12:30,439 --> 01:12:37,640
Speaker 18: many like science labs and you know, biology, chemistry labs, sociology.

1577
01:12:37,840 --> 01:12:40,240
Speaker 18: There were animals held in there like for experiment, like

1578
01:12:40,360 --> 01:12:42,200
Speaker 18: not like they were torturing this poor or anything like that,

1579
01:12:42,320 --> 01:12:44,400
Speaker 18: of course, but they had like they had like basically

1580
01:12:44,479 --> 01:12:46,400
Speaker 18: lab rats, you know, like they were doing important work,

1581
01:12:46,479 --> 01:12:49,640
Speaker 18: like it was at George Rivashaw, a chemist that you

1582
01:12:49,800 --> 01:12:52,439
Speaker 18: see who invented Benadryl was doing you did some of

1583
01:12:52,479 --> 01:12:54,240
Speaker 18: his work at Crosley Tower. Like there was a lot

1584
01:12:54,280 --> 01:12:57,000
Speaker 18: of important work done in that building. And I think

1585
01:12:57,080 --> 01:12:59,519
Speaker 18: still takes Benadrill. Oh really yeah, And you could think

1586
01:13:00,320 --> 01:13:02,519
Speaker 18: for that I did not know I learned something else.

1587
01:13:02,760 --> 01:13:05,760
Speaker 1: I know that obviously, you see had great connection Cincinnati

1588
01:13:05,840 --> 01:13:08,839
Speaker 1: had great connection to with issues with like polio research

1589
01:13:08,880 --> 01:13:11,000
Speaker 1: and everything else too, But I did not know about

1590
01:13:11,000 --> 01:13:12,519
Speaker 1: the diephen higermen.

1591
01:13:12,680 --> 01:13:13,920
Speaker 3: Yeah, so thank you, thank you. Yeah.

1592
01:13:14,479 --> 01:13:16,760
Speaker 18: And I think I think the public reaction, everybody was

1593
01:13:16,800 --> 01:13:18,479
Speaker 18: in agreement when it was first talked.

1594
01:13:18,320 --> 01:13:19,840
Speaker 3: About that it was coming down, that it was going

1595
01:13:19,880 --> 01:13:20,599
Speaker 3: to be a sad day.

1596
01:13:20,640 --> 01:13:23,719
Speaker 18: When it was officially announced that yeah, we have plans,

1597
01:13:23,880 --> 01:13:26,599
Speaker 18: here's when it's starting, everybody was going to be bummed.

1598
01:13:26,640 --> 01:13:28,960
Speaker 18: Even the people who were like that building's ugly. Not

1599
01:13:29,080 --> 01:13:33,599
Speaker 18: many people were like, you know, I'm happy it's coming down.

1600
01:13:33,880 --> 01:13:36,439
Speaker 18: That thing is ugly, it's terrible. We need to get

1601
01:13:36,520 --> 01:13:38,040
Speaker 18: rid of it. I think most people were like, this

1602
01:13:38,240 --> 01:13:40,479
Speaker 18: is gonna be sad because it's so iconic. It's such

1603
01:13:40,520 --> 01:13:43,759
Speaker 18: a a I mean, it's a massive fixture of UC's campus.

1604
01:13:43,800 --> 01:13:46,559
Speaker 18: You brought up the diversity of the architecture, which is fun.

1605
01:13:46,640 --> 01:13:48,080
Speaker 18: I remember the first time I came down with like,

1606
01:13:48,200 --> 01:13:50,679
Speaker 18: my this is going to sound weird, and I started

1607
01:13:50,720 --> 01:13:51,960
Speaker 18: to say it's so, I'm going to say it anyway.

1608
01:13:52,160 --> 01:13:54,360
Speaker 18: My grandmother's boyfriend, which is weird to say, but she

1609
01:13:54,520 --> 01:13:57,679
Speaker 18: was divorced. One of the early nineteen hundred stuff happens.

1610
01:13:57,720 --> 01:13:58,000
Speaker 2: Amen.

1611
01:13:58,640 --> 01:14:01,519
Speaker 1: So I would come down here with her from Dayton

1612
01:14:01,720 --> 01:14:03,720
Speaker 1: and her boyfriend at the time. I got to see

1613
01:14:03,760 --> 01:14:05,599
Speaker 1: some UC basketball, and I remember going around the campus

1614
01:14:05,640 --> 01:14:08,080
Speaker 1: seeing buildings, and I was just blown away about how

1615
01:14:08,160 --> 01:14:09,920
Speaker 1: different in everything walking around was.

1616
01:14:10,000 --> 01:14:11,200
Speaker 3: He may have actually gone to UC.

1617
01:14:11,360 --> 01:14:14,360
Speaker 1: I don't know, okay, but that was my introduction to

1618
01:14:14,439 --> 01:14:16,040
Speaker 1: campus and all that other stuff where I was just

1619
01:14:16,120 --> 01:14:18,879
Speaker 1: kind of like, oh wow, which is pretty interesting.

1620
01:14:19,040 --> 01:14:20,200
Speaker 3: Yeah, there's like.

1621
01:14:20,280 --> 01:14:23,400
Speaker 18: Even like you look at some of the public comments online, like,

1622
01:14:23,400 --> 01:14:25,280
Speaker 18: there's some Google reviews here that are really good. Some

1623
01:14:25,400 --> 01:14:27,840
Speaker 18: of them are just like heartfelt, some of them are

1624
01:14:27,840 --> 01:14:28,799
Speaker 18: a little bit more comical.

1625
01:14:28,920 --> 01:14:29,640
Speaker 3: So here's one for you.

1626
01:14:30,080 --> 01:14:32,639
Speaker 18: Can be scary at times, and it is poorly designed. However,

1627
01:14:32,760 --> 01:14:34,880
Speaker 18: it is a trademark of UC that can be seen

1628
01:14:34,960 --> 01:14:37,960
Speaker 18: from almost anywhere on campus. It would make me, along

1629
01:14:38,040 --> 01:14:40,720
Speaker 18: with many others, said, if it ever actually got torn down.

1630
01:14:40,880 --> 01:14:42,880
Speaker 1: So it sounds like that they're calling it a mistake,

1631
01:14:43,040 --> 01:14:44,559
Speaker 1: but it's a happy mistake.

1632
01:14:44,760 --> 01:14:44,920
Speaker 2: Yeah.

1633
01:14:45,520 --> 01:14:48,640
Speaker 18: The scariest single pore of concrete that I've ever been in.

1634
01:14:49,800 --> 01:14:53,040
Speaker 18: I love mister Crosley, though, final Boss of the University

1635
01:14:53,040 --> 01:14:55,400
Speaker 18: of Cincinnati will stare you down from anywhere where you

1636
01:14:55,479 --> 01:14:57,679
Speaker 18: are on campus. The Ultimate Building of the United States

1637
01:14:57,720 --> 01:14:59,839
Speaker 18: of America. And yet that was a three star review. Apparently,

1638
01:15:00,080 --> 01:15:02,439
Speaker 18: somehow the Ultimate Building of the United States of America

1639
01:15:02,520 --> 01:15:05,439
Speaker 18: gets a three star review. Someone's here, like, here's more

1640
01:15:05,479 --> 01:15:09,120
Speaker 18: comical stuff. Best building in town has the tastiest asbestos

1641
01:15:09,200 --> 01:15:12,840
Speaker 18: on campus. Yeah, by the way, don't eat asbestos. I

1642
01:15:12,960 --> 01:15:16,160
Speaker 18: want to make sure that I have the music not

1643
01:15:16,240 --> 01:15:18,880
Speaker 18: associated with any of this. Yes, I'm not either. That's

1644
01:15:18,920 --> 01:15:20,960
Speaker 18: a public statement that someone made on Google. I want

1645
01:15:21,000 --> 01:15:23,320
Speaker 18: to make it clear I'm not advocating for that whatsoever.

1646
01:15:23,400 --> 01:15:26,160
Speaker 18: That's a bad idea. Someone even wrote a poem here.

1647
01:15:26,400 --> 01:15:29,560
Speaker 18: Oh yes, it says, my eyes open from slumber. I

1648
01:15:29,640 --> 01:15:33,000
Speaker 18: quickly get dressed and rush outside toast in mouth and

1649
01:15:33,080 --> 01:15:34,920
Speaker 18: MacBook tucked neatly under my shoulder.

1650
01:15:35,240 --> 01:15:35,519
Speaker 3: Quote.

1651
01:15:35,600 --> 01:15:37,960
Speaker 18: I can't wait to see him rushing up the street.

1652
01:15:38,000 --> 01:15:40,400
Speaker 18: I begin to see his head, he knowing I'm coming.

1653
01:15:40,479 --> 01:15:42,720
Speaker 18: Big brother always knows. This is the best part of

1654
01:15:42,760 --> 01:15:45,040
Speaker 18: my morning, knowing that Crosley will see me once more

1655
01:15:45,520 --> 01:15:47,519
Speaker 18: after our bitter departure of the night before.

1656
01:15:47,880 --> 01:15:51,639
Speaker 3: Oh so long, oh so strong. The concrete and height everything.

1657
01:15:51,720 --> 01:15:54,360
Speaker 3: I like, you are where I belong now and forever.

1658
01:15:54,680 --> 01:15:54,840
Speaker 2: WOA.

1659
01:15:54,960 --> 01:15:58,000
Speaker 1: That was fair XXL, very emotional. I could feel that

1660
01:15:58,120 --> 01:16:00,479
Speaker 1: it was good. We are arts and craftsy some poetry,

1661
01:16:00,800 --> 01:16:01,080
Speaker 1: big one.

1662
01:16:01,120 --> 01:16:01,400
Speaker 3: That's good.

1663
01:16:01,520 --> 01:16:03,640
Speaker 18: And I was talking to Brian Thomas about it on

1664
01:16:03,720 --> 01:16:05,599
Speaker 18: Tuesday morning because I was filming in for Joe's Tracker

1665
01:16:05,640 --> 01:16:08,160
Speaker 18: on fifty five KRC, and he had brought it up

1666
01:16:08,160 --> 01:16:11,920
Speaker 18: because Tuesday was the day that deconstruction officially began. So

1667
01:16:12,040 --> 01:16:13,920
Speaker 18: then I went down the rabbit hole of all of

1668
01:16:13,960 --> 01:16:16,240
Speaker 18: these like articles that had been posted in the last

1669
01:16:16,720 --> 01:16:19,759
Speaker 18: twenty four hours, and there was all kinds of cool history,

1670
01:16:19,800 --> 01:16:22,920
Speaker 18: the photos of when it first opened, some basic facts

1671
01:16:22,960 --> 01:16:24,840
Speaker 18: and history about it that I didn't know before. I

1672
01:16:24,880 --> 01:16:27,040
Speaker 18: didn't have much of a personal connection to the building

1673
01:16:27,320 --> 01:16:29,280
Speaker 18: other than I just love how it looked at Iconic.

1674
01:16:29,360 --> 01:16:30,120
Speaker 3: I love the campus.

1675
01:16:30,200 --> 01:16:32,479
Speaker 18: The diversity of the architecture, you know, the way that

1676
01:16:32,920 --> 01:16:35,800
Speaker 18: you have old architecture mixed with new architecture, and the

1677
01:16:35,880 --> 01:16:38,599
Speaker 18: way that even some of the newer architecture wraps around

1678
01:16:38,680 --> 01:16:41,439
Speaker 18: the old architecture and the way that they merged together.

1679
01:16:41,880 --> 01:16:43,160
Speaker 3: That's what I love about the campus.

1680
01:16:43,280 --> 01:16:45,479
Speaker 18: And granted it's an you know, you can call it

1681
01:16:45,520 --> 01:16:47,720
Speaker 18: an ugly building, but it's unique and it adds to

1682
01:16:47,840 --> 01:16:51,640
Speaker 18: that sense of diversity on campus. So he was telling me,

1683
01:16:51,800 --> 01:16:54,040
Speaker 18: Brian was that, you know, he had had a lot

1684
01:16:54,080 --> 01:16:58,240
Speaker 18: of classes and classes in that building, so he had

1685
01:16:58,280 --> 01:17:00,360
Speaker 18: a really deep personal connection with it. And he's like,

1686
01:17:00,400 --> 01:17:03,240
Speaker 18: you know, it's funny because most of the sentimental value

1687
01:17:03,280 --> 01:17:05,400
Speaker 18: I have about going to UC is really linked to

1688
01:17:05,479 --> 01:17:07,920
Speaker 18: that building. And I'm like, see, that's interesting. I don't

1689
01:17:07,920 --> 01:17:10,560
Speaker 18: know many people that do, because I mean I was

1690
01:17:10,920 --> 01:17:12,640
Speaker 18: way late to the party. Like by the time I

1691
01:17:12,760 --> 01:17:14,320
Speaker 18: was in college, it was like, yeah, we're taking it

1692
01:17:14,400 --> 01:17:17,880
Speaker 18: down in the next ten years, so it was slowly

1693
01:17:18,080 --> 01:17:21,960
Speaker 18: being abandoned and just people were slowly moving out their offices.

1694
01:17:22,040 --> 01:17:24,960
Speaker 18: I think I went in there maybe twice, once for fun,

1695
01:17:25,360 --> 01:17:27,880
Speaker 18: because I made it a goal of mine before I

1696
01:17:27,960 --> 01:17:30,479
Speaker 18: even started going to UC, like months leading into my

1697
01:17:30,520 --> 01:17:32,479
Speaker 18: freshman year, that I was going to learn the name

1698
01:17:32,520 --> 01:17:34,720
Speaker 18: of every building on campus and I was going to

1699
01:17:34,800 --> 01:17:37,479
Speaker 18: go inside and check them out for myself because I

1700
01:17:37,600 --> 01:17:39,640
Speaker 18: just wanted to know what do they look like on

1701
01:17:39,680 --> 01:17:41,000
Speaker 18: the inside. I'm going to be here for the next

1702
01:17:41,080 --> 01:17:43,559
Speaker 18: four years. I'm gonna be curious about this stuff. They

1703
01:17:43,600 --> 01:17:46,280
Speaker 18: didn't do that for your tour. No, No, I gave

1704
01:17:46,320 --> 01:17:48,760
Speaker 18: myself a tour. I did, Yeah, I mean I got

1705
01:17:48,800 --> 01:17:50,360
Speaker 18: to see like the dorm I was living in and

1706
01:17:50,360 --> 01:17:52,000
Speaker 18: stuff like that, and I got to see the campus.

1707
01:17:52,080 --> 01:17:53,840
Speaker 3: But I mean I didn't really need a tour.

1708
01:17:53,880 --> 01:17:55,640
Speaker 18: I had been on campus so many times, and I

1709
01:17:55,680 --> 01:17:57,840
Speaker 18: went up by myself, like everybody's there with their parents,

1710
01:17:57,880 --> 01:17:59,840
Speaker 18: and I'm just like, hey, it's just me.

1711
01:18:00,760 --> 01:18:02,000
Speaker 3: I know what I'm doing.

1712
01:18:02,439 --> 01:18:02,560
Speaker 2: You know.

1713
01:18:02,880 --> 01:18:05,320
Speaker 18: You just give me the lecture on the on the

1714
01:18:06,080 --> 01:18:10,240
Speaker 18: material of UC and and the academic material.

1715
01:18:10,320 --> 01:18:12,840
Speaker 3: That's fine, that's all I need. I can give myself

1716
01:18:12,880 --> 01:18:14,960
Speaker 3: a tour. I know where I'm going. Yeah, and you

1717
01:18:15,040 --> 01:18:16,280
Speaker 3: try to figure it out. By the way.

1718
01:18:16,439 --> 01:18:19,240
Speaker 1: Sean McMahon, of course a producer here talking about this

1719
01:18:19,439 --> 01:18:21,880
Speaker 1: Crosley tower that's going to go bye bye eventually. It's

1720
01:18:21,880 --> 01:18:23,599
Speaker 1: going to take a little longer than some other buildings.

1721
01:18:23,880 --> 01:18:26,920
Speaker 1: February of next year. That's that's a long deconstruction. Yeah,

1722
01:18:26,920 --> 01:18:29,280
Speaker 1: that is what that is, and demolition that goes along

1723
01:18:29,320 --> 01:18:31,960
Speaker 1: with that, and then who knows what comes next. Was

1724
01:18:31,960 --> 01:18:33,800
Speaker 1: Sterling on the big one, you know. The other thing,

1725
01:18:33,840 --> 01:18:36,400
Speaker 1: of course, is the Crossley connection. Pal Crawsley, Junior of

1726
01:18:36,400 --> 01:18:39,280
Speaker 1: course going back to the early Roaring twenties, like the

1727
01:18:39,360 --> 01:18:42,360
Speaker 1: twenties two. Yeah, and of course, you know, in that

1728
01:18:42,439 --> 01:18:47,080
Speaker 1: window of time, they basically created seven hundredu w ulw

1729
01:18:47,320 --> 01:18:50,400
Speaker 1: in that window, we just celebrated one hundred and four

1730
01:18:50,520 --> 01:18:52,639
Speaker 1: more years at one hundred and four years, I guess, yeah,

1731
01:18:52,760 --> 01:18:58,840
Speaker 1: which is astounding amazing. And there you've got we have radio.

1732
01:18:59,200 --> 01:19:01,280
Speaker 1: We need a state or a station and then radio

1733
01:19:01,320 --> 01:19:02,760
Speaker 1: and they made it all happen. I mean, it was

1734
01:19:02,800 --> 01:19:05,640
Speaker 1: a pretty amazing thought process to go with it. I mean,

1735
01:19:05,760 --> 01:19:08,040
Speaker 1: I'm just trying to get the dog outside, and they're

1736
01:19:08,240 --> 01:19:10,560
Speaker 1: you know, we have broadcast technology and then here we

1737
01:19:10,640 --> 01:19:11,599
Speaker 1: are the nation station.

1738
01:19:12,320 --> 01:19:13,439
Speaker 3: Of all the other buildings.

1739
01:19:13,680 --> 01:19:17,960
Speaker 1: I'm wondering because I think most campuses a lot of cities,

1740
01:19:18,680 --> 01:19:21,400
Speaker 1: whether it's bridges when we think about Brent Spence Bridge,

1741
01:19:21,439 --> 01:19:23,800
Speaker 1: and a lot of people love that mess that's going

1742
01:19:23,880 --> 01:19:26,240
Speaker 1: to you know, obviously have a companion bridge sooner than

1743
01:19:26,320 --> 01:19:29,160
Speaker 1: later with seventy one seventy five of stuff that used

1744
01:19:29,200 --> 01:19:31,720
Speaker 1: to be that isn't anymore, or stuff that you love

1745
01:19:31,840 --> 01:19:35,080
Speaker 1: that you can't imagine ever going away. I mean, there's

1746
01:19:35,120 --> 01:19:37,599
Speaker 1: a lot of stuff in the skyline here in other

1747
01:19:37,680 --> 01:19:39,639
Speaker 1: places that go with it. Is there any other building

1748
01:19:39,680 --> 01:19:41,759
Speaker 1: that you have a connection to that you can remember,

1749
01:19:42,040 --> 01:19:44,519
Speaker 1: like as a kid, for me growing up in Dayton

1750
01:19:44,560 --> 01:19:46,720
Speaker 1: before you know, coming here the first time, and all

1751
01:19:47,120 --> 01:19:50,000
Speaker 1: I can remember leaving Dayton and whether it was coming

1752
01:19:50,000 --> 01:19:52,960
Speaker 1: to Cincinnati for the Reds games or whether going to Florida,

1753
01:19:53,000 --> 01:19:55,360
Speaker 1: which really sticks out as a kid too. Yeah, you'd

1754
01:19:55,400 --> 01:19:57,760
Speaker 1: pass like what was at that point Dayton Power and Lights,

1755
01:19:57,800 --> 01:19:59,920
Speaker 1: big building near ud Okay, and I was like, okay,

1756
01:20:00,120 --> 01:20:02,040
Speaker 1: leave in town. Then you go through a Moraine and

1757
01:20:02,120 --> 01:20:04,720
Speaker 1: you go I smell what now is like Mount Roumky Yeah,

1758
01:20:04,960 --> 01:20:07,720
Speaker 1: and then sorry, Moraine. I have relatives in the area.

1759
01:20:07,760 --> 01:20:09,479
Speaker 1: I'm not hating on you and the people are hating

1760
01:20:09,520 --> 01:20:12,280
Speaker 1: me now and then driving through, but there's certain stuff

1761
01:20:12,320 --> 01:20:14,320
Speaker 1: that sort of sticks out. It's like, oh, this is

1762
01:20:14,400 --> 01:20:16,880
Speaker 1: that town. This is that Like if they ever took

1763
01:20:17,000 --> 01:20:19,880
Speaker 1: down the arch, the gateway to the West on the

1764
01:20:19,920 --> 01:20:22,559
Speaker 1: West Bank near Bush Stadium in Saint Louis, Yeah, I mean,

1765
01:20:22,600 --> 01:20:24,919
Speaker 1: would you recognize anything in Saint Louis.

1766
01:20:24,720 --> 01:20:26,640
Speaker 18: Otherwise I don't know that I would, honestly. I mean,

1767
01:20:26,680 --> 01:20:28,400
Speaker 18: what else do you know Saint Louis for other than

1768
01:20:28,400 --> 01:20:31,600
Speaker 18: the Cardinals and formerly the Rams right. Yeah, yeah, the

1769
01:20:31,720 --> 01:20:34,960
Speaker 18: arch is there, that's their icon, right. I think maybe

1770
01:20:35,160 --> 01:20:38,200
Speaker 18: for me that's a good question. I would say the

1771
01:20:38,240 --> 01:20:39,240
Speaker 18: Big Mack Bridge.

1772
01:20:39,280 --> 01:20:40,920
Speaker 3: Yeah, that's true. I mean it's monument.

1773
01:20:41,120 --> 01:20:43,800
Speaker 18: It looks exactly like Pittsburgh's bridge, it does, but it

1774
01:20:43,920 --> 01:20:46,519
Speaker 18: stands out you know, it's it's big, it's yellow, it's

1775
01:20:46,640 --> 01:20:48,960
Speaker 18: it's got a unique design. Even the suspension bridge, like

1776
01:20:49,000 --> 01:20:52,800
Speaker 18: Russ just mentioned, Yeah, that the suspension bridge. I really

1777
01:20:52,840 --> 01:20:55,320
Speaker 18: started learning about about the suspension bridge much later on

1778
01:20:55,600 --> 01:20:57,920
Speaker 18: how it was. Yeah, it was a it was a

1779
01:20:58,160 --> 01:21:00,559
Speaker 18: precursor essentially the one in New York.

1780
01:21:01,040 --> 01:21:03,880
Speaker 1: Cincinnati, take it over the Ohio and then go to

1781
01:21:04,160 --> 01:21:06,400
Speaker 1: you know, much bigger stuff in New York, which is

1782
01:21:06,720 --> 01:21:07,840
Speaker 1: that's an amazing thing.

1783
01:21:08,000 --> 01:21:08,360
Speaker 3: Yeah it is.

1784
01:21:08,560 --> 01:21:11,280
Speaker 1: I I just it's hard for me to imagine the

1785
01:21:11,439 --> 01:21:15,400
Speaker 1: brain power that goes into the vision, aside from the engineering.

1786
01:21:15,520 --> 01:21:17,759
Speaker 1: Ye just to say okay, we're going to solve this problem.

1787
01:21:18,479 --> 01:21:21,360
Speaker 1: AI will help me get there, right. These people did

1788
01:21:21,439 --> 01:21:24,280
Speaker 1: not have that, which is also a wild thing.

1789
01:21:24,479 --> 01:21:27,880
Speaker 18: Incredible, man. Engineering is amazing, especially when you look at

1790
01:21:27,880 --> 01:21:29,760
Speaker 18: how it was done in this city. Now, it's a

1791
01:21:29,800 --> 01:21:32,880
Speaker 18: shame that, like some of these our old infrastructure is

1792
01:21:33,000 --> 01:21:35,840
Speaker 18: literally collapsing before our eyes. You know, they've been doing

1793
01:21:35,920 --> 01:21:37,720
Speaker 18: tons of projects to work on the Roebling Bridge, and

1794
01:21:37,800 --> 01:21:40,040
Speaker 18: unfortunately there's nothing they can do for Crosley Tower.

1795
01:21:40,320 --> 01:21:42,120
Speaker 3: I wanted to give a couple more facts here.

1796
01:21:43,240 --> 01:21:45,759
Speaker 18: There was actually a UC student who made a type

1797
01:21:45,840 --> 01:21:50,000
Speaker 18: font that was inspired by the design language.

1798
01:21:50,560 --> 01:21:52,519
Speaker 1: When you look at the font a little bit. Yeah,

1799
01:21:52,680 --> 01:21:54,599
Speaker 1: but I almost always good for people's reading.

1800
01:21:54,880 --> 01:21:55,599
Speaker 3: We'll read more.

1801
01:21:55,720 --> 01:21:56,559
Speaker 15: My eyes hurt.

1802
01:21:56,680 --> 01:21:57,160
Speaker 5: It hurts.

1803
01:21:57,240 --> 01:21:59,600
Speaker 18: I almost kind of want my email signature to be

1804
01:22:00,600 --> 01:22:03,439
Speaker 18: that funt because it's so cool. I don't know how

1805
01:22:03,479 --> 01:22:05,719
Speaker 18: I can get it. It's I wonder if it's publicly accessed.

1806
01:22:05,920 --> 01:22:07,679
Speaker 18: They call it brutalist, or they call it Crossley.

1807
01:22:07,680 --> 01:22:09,479
Speaker 3: What do they call it? You know, that's a good question, somebody.

1808
01:22:09,920 --> 01:22:11,559
Speaker 3: You should have had that ready, yes, And I should

1809
01:22:11,560 --> 01:22:12,720
Speaker 3: have had the name of the student ready.

1810
01:22:12,720 --> 01:22:14,320
Speaker 1: And I'm sorry. I know that's all right. You knew

1811
01:22:14,320 --> 01:22:16,200
Speaker 1: I was going to ask the most like thing that

1812
01:22:16,400 --> 01:22:17,960
Speaker 1: is going to put you in in the weeds. I

1813
01:22:18,000 --> 01:22:19,439
Speaker 1: didn't mean to do it, but I'm good at doing that.

1814
01:22:19,520 --> 01:22:21,439
Speaker 18: But apparently the shape of the building was meant to

1815
01:22:21,520 --> 01:22:24,280
Speaker 18: help exhaust the science lab fumes from the building. The

1816
01:22:24,680 --> 01:22:26,920
Speaker 18: way that it's like to let it get out, yeah, exactly,

1817
01:22:27,080 --> 01:22:31,240
Speaker 18: to get it away from the building and through the neighborhood. Yeah, yeah, right, precisely. Yeah,

1818
01:22:31,479 --> 01:22:34,519
Speaker 18: send it through the houses of the neighborhood. Fine, kids

1819
01:22:34,560 --> 01:22:38,400
Speaker 18: are fine. There's there's some other really funny public reviews

1820
01:22:38,400 --> 01:22:40,439
Speaker 18: here is like there's a lot of people talking about ghosts.

1821
01:22:40,720 --> 01:22:43,280
Speaker 18: There's one that says, minus the shadow people whispering in

1822
01:22:43,360 --> 01:22:45,440
Speaker 18: the halls, the vibes are immaculate.

1823
01:22:45,760 --> 01:22:47,800
Speaker 1: Now, these are college students probably coming up with this

1824
01:22:47,880 --> 01:22:50,960
Speaker 1: other may some other type of assistance to have that.

1825
01:22:51,160 --> 01:22:53,000
Speaker 3: Do you think there's anything haunted there? I mean it's

1826
01:22:53,000 --> 01:22:54,400
Speaker 3: an old campus. There's gotta be.

1827
01:22:54,439 --> 01:22:56,840
Speaker 18: There if you believe thing right, I don't know what

1828
01:22:57,040 --> 01:22:59,640
Speaker 18: building would be Like Crosley, of all of them, kind

1829
01:22:59,680 --> 01:23:02,000
Speaker 18: of makes the most sense because people like to hate

1830
01:23:02,040 --> 01:23:04,560
Speaker 18: on it the most more than any other building. Like

1831
01:23:04,600 --> 01:23:07,679
Speaker 18: you wouldn't look at the Athletic build million, Yeah it's haunted.

1832
01:23:07,800 --> 01:23:09,560
Speaker 18: Like that doesn't really make any sense. But like you

1833
01:23:09,600 --> 01:23:12,000
Speaker 18: look across and you tell someone that's haunted, Like you

1834
01:23:12,040 --> 01:23:14,360
Speaker 18: could convince a few people who are toring the campus like,

1835
01:23:14,439 --> 01:23:16,080
Speaker 18: oh wow, that's a haunted building.

1836
01:23:16,200 --> 01:23:17,840
Speaker 3: Yeah, you know, you look at the athletic facility. You're like,

1837
01:23:18,080 --> 01:23:19,840
Speaker 3: there's no way that's haunted. That doesn't make any sense.

1838
01:23:20,240 --> 01:23:22,240
Speaker 3: So you know, it's sad.

1839
01:23:22,400 --> 01:23:25,839
Speaker 18: It's a sad day to know that it's days are numbered,

1840
01:23:25,880 --> 01:23:28,320
Speaker 18: it's on borrowed time, and by this time next year

1841
01:23:28,400 --> 01:23:31,200
Speaker 18: it'll be it'll be gone. And I think by February

1842
01:23:31,240 --> 01:23:35,360
Speaker 18: of twenty twenty nine they'll have a new science, Technology, engineering,

1843
01:23:35,479 --> 01:23:39,080
Speaker 18: and math building in that exact print, Yeah, in that

1844
01:23:39,200 --> 01:23:41,880
Speaker 18: footprint right there, So they'll have a new building as

1845
01:23:41,920 --> 01:23:44,400
Speaker 18: they do like every year. You know, there's always a

1846
01:23:44,479 --> 01:23:46,880
Speaker 18: new building popping up on that campus because you see

1847
01:23:47,000 --> 01:23:48,679
Speaker 18: under constructions to that old jokes.

1848
01:23:48,800 --> 01:23:50,680
Speaker 1: And that's the beauty of it really because it is

1849
01:23:50,760 --> 01:23:54,479
Speaker 1: constantly changing, you bringing new minds and new ideas and

1850
01:23:54,640 --> 01:23:57,519
Speaker 1: new things. So I mean it's just ever changing and

1851
01:23:57,560 --> 01:23:59,439
Speaker 1: always Thanks for coming in and doing this. You did

1852
01:23:59,439 --> 01:23:59,960
Speaker 1: a great job.

1853
01:24:00,120 --> 01:24:00,680
Speaker 3: Thank you, Thank you.

1854
01:24:00,760 --> 01:24:04,160
Speaker 18: I'm going to miss that concrete monolith that someone described

1855
01:24:04,200 --> 01:24:07,679
Speaker 18: as a sentinel looking over Clifton and the greater Cincinnati area.

1856
01:24:07,880 --> 01:24:09,720
Speaker 1: I don't know how it works with licensing, but I

1857
01:24:09,760 --> 01:24:12,240
Speaker 1: could see Sincy shirts or somebody else getting some shirts

1858
01:24:12,240 --> 01:24:15,040
Speaker 1: together that yeah, I will buy one right now if

1859
01:24:15,080 --> 01:24:17,840
Speaker 1: they make one, I will buy five of them. I

1860
01:24:17,880 --> 01:24:19,599
Speaker 1: don't want to tell anybody how to handle their money,

1861
01:24:19,640 --> 01:24:21,360
Speaker 1: but we're all good at it with sports teams. Everybody

1862
01:24:21,479 --> 01:24:23,200
Speaker 1: likes to tell the Bengals how to handle their business

1863
01:24:23,280 --> 01:24:25,400
Speaker 1: or the Reds, right exactly. I don't want to interfere

1864
01:24:25,439 --> 01:24:28,160
Speaker 1: with the University of Cincinnati. There's a lot of a

1865
01:24:28,200 --> 01:24:30,760
Speaker 1: lot of weight there, powerful people, right right, But that's

1866
01:24:30,760 --> 01:24:32,040
Speaker 1: one of those where'd be like, yeah, I get I

1867
01:24:32,120 --> 01:24:33,679
Speaker 1: get a little of that. So if they could find

1868
01:24:33,720 --> 01:24:35,200
Speaker 1: a way to do it, that might be way to

1869
01:24:35,320 --> 01:24:38,120
Speaker 1: maybe raise some money for charitable stuff or something along

1870
01:24:38,160 --> 01:24:40,080
Speaker 1: those lines too. We also we got a bounce, we

1871
01:24:40,120 --> 01:24:42,200
Speaker 1: got other stuff to speaking in the University of Cincinnati

1872
01:24:42,280 --> 01:24:45,679
Speaker 1: and the future director of AI Strategy Perry Professor University

1873
01:24:45,760 --> 01:24:48,920
Speaker 1: of Cincinnati's Lender College of Business, Jeff Schaeffer, going to

1874
01:24:49,000 --> 01:24:51,599
Speaker 1: join me conversation I had with him about the future.

1875
01:24:51,920 --> 01:24:54,720
Speaker 1: Five years from now, life as we know it will

1876
01:24:54,760 --> 01:24:57,600
Speaker 1: not be. It will be something completely different. How do

1877
01:24:57,680 --> 01:24:59,880
Speaker 1: you stay relevant, how do you stay vital? How do

1878
01:24:59,920 --> 01:25:02,280
Speaker 1: you you have a future in this or your kids?

1879
01:25:02,360 --> 01:25:04,720
Speaker 1: We'll talk about that after your two third report. Right here,

1880
01:25:04,960 --> 01:25:08,599
Speaker 1: Sterling and a Sean McMahon and Russ Jackson and Sandy Collins.

1881
01:25:08,600 --> 01:25:10,280
Speaker 1: I think with those or somebody else's coming in. I

1882
01:25:10,280 --> 01:25:13,439
Speaker 1: don't know who, maybe Matt. Seven hundred double LW.

1883
01:25:23,840 --> 01:25:24,519
Speaker 14: The same.

1884
01:25:24,760 --> 01:25:28,520
Speaker 1: The longest time is coming for us, all artificial intelligence,

1885
01:25:28,600 --> 01:25:32,400
Speaker 1: the robots taking our jobs. The question, of course, what

1886
01:25:32,640 --> 01:25:35,120
Speaker 1: is it that you and your kids and I and

1887
01:25:35,400 --> 01:25:38,479
Speaker 1: Alex Egano's producing or Sandy Collins can do or are

1888
01:25:38,560 --> 01:25:42,000
Speaker 1: willie to skill up around what we already do to

1889
01:25:42,160 --> 01:25:45,000
Speaker 1: embrace AI and have a future where we're not going

1890
01:25:45,600 --> 01:25:46,960
Speaker 1: I have no idea what I'm going to do with

1891
01:25:47,080 --> 01:25:50,599
Speaker 1: my life. A guy who knows all about these things.

1892
01:25:51,360 --> 01:25:55,400
Speaker 1: The director of AI Strategy and Perry Professor the University

1893
01:25:55,439 --> 01:25:59,160
Speaker 1: of Cincinnati Lendner College of Business, Jeff Shaeffer, Welcome back

1894
01:25:59,160 --> 01:26:01,120
Speaker 1: to seven hundred double You well w with Sterling.

1895
01:26:01,160 --> 01:26:02,360
Speaker 3: How are you this fine Sunday?

1896
01:26:03,520 --> 01:26:03,920
Speaker 5: I'm good.

1897
01:26:04,000 --> 01:26:04,960
Speaker 2: Thanks for having me back.

1898
01:26:05,080 --> 01:26:06,360
Speaker 3: I appreciate you making time.

1899
01:26:06,560 --> 01:26:09,680
Speaker 1: So what is the first thing someone should look at

1900
01:26:09,880 --> 01:26:13,600
Speaker 1: is they assess their current situation in looking ahead in

1901
01:26:13,760 --> 01:26:17,400
Speaker 1: whatever industry, whatever trade, whatever it is that they do

1902
01:26:17,760 --> 01:26:20,360
Speaker 1: to make a living or have a passion that they

1903
01:26:20,400 --> 01:26:23,360
Speaker 1: should be looking to improve upon to be able to

1904
01:26:23,479 --> 01:26:27,000
Speaker 1: stick around and keep doing something anything and have a future.

1905
01:26:28,800 --> 01:26:31,760
Speaker 2: Well, these are all great questions and questions that I think,

1906
01:26:31,920 --> 01:26:34,920
Speaker 2: you know, we're pondering in this age of AI. Here, right,

1907
01:26:35,560 --> 01:26:38,800
Speaker 2: we have six thousand students as an example, at the

1908
01:26:38,880 --> 01:26:42,559
Speaker 2: Lennar College of Business, you see, and we're talking about

1909
01:26:42,600 --> 01:26:45,800
Speaker 2: these same things. So one of the things that we've

1910
01:26:45,880 --> 01:26:48,680
Speaker 2: done is kind of put these into i'll call them

1911
01:26:48,760 --> 01:26:52,960
Speaker 2: buckets of competencies. Right, and so you have some basic

1912
01:26:53,040 --> 01:26:57,200
Speaker 2: things about career readiness, professionalism, you know, things that before

1913
01:26:57,240 --> 01:26:59,800
Speaker 2: AI we'd still talk about, right, showing up to work

1914
01:27:00,080 --> 01:27:04,160
Speaker 2: and prepare, you know, accountability, respect all all of those things.

1915
01:27:04,479 --> 01:27:07,680
Speaker 2: What's really changed now is the tech side of it, right,

1916
01:27:07,880 --> 01:27:11,920
Speaker 2: tech savviness and trying to be fluent in that, in

1917
01:27:12,040 --> 01:27:14,840
Speaker 2: that in those tools and in those those language and

1918
01:27:15,600 --> 01:27:18,600
Speaker 2: the other thing that probably doesn't get enough talk. And

1919
01:27:18,840 --> 01:27:21,840
Speaker 2: when we're all talking about technology, are we skilled up

1920
01:27:21,880 --> 01:27:26,160
Speaker 2: in the AI uh space with these tools and uh uh,

1921
01:27:26,360 --> 01:27:31,519
Speaker 2: these these new things right that that potentially make us better, faster, smarter. Uh,

1922
01:27:31,720 --> 01:27:34,200
Speaker 2: we don't lose the human element. The human element may

1923
01:27:34,439 --> 01:27:37,200
Speaker 2: in fact become more important than the tech element in

1924
01:27:37,320 --> 01:27:39,680
Speaker 2: all this. So when you ask that question, I think

1925
01:27:39,720 --> 01:27:42,200
Speaker 2: the first thing is don't be afraid of it. I

1926
01:27:42,240 --> 01:27:45,160
Speaker 2: think you got to lean into the technology. At Pandora's

1927
01:27:45,240 --> 01:27:48,280
Speaker 2: boxes open. You know, everything is AI and it's been

1928
01:27:48,320 --> 01:27:50,360
Speaker 2: around for a long time. It's like you say, oh,

1929
01:27:50,400 --> 01:27:53,160
Speaker 2: I don't want to use AI. Well, you're you're watching

1930
01:27:53,240 --> 01:27:56,080
Speaker 2: Netflix tonight right that that that has AI in it,

1931
01:27:56,320 --> 01:27:59,160
Speaker 2: So you know everything has AI built into these things.

1932
01:27:59,240 --> 01:28:02,760
Speaker 2: It's just getting more ingrained into the things that we do.

1933
01:28:03,760 --> 01:28:06,759
Speaker 1: Talking to Jeff Schaeffer, he is the director of AI Strategy,

1934
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Speaker 1: Perry Professor, University of Cincinnati's Lendner College of Business.

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Speaker 3: With Sterling on the Big One.

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Speaker 1: So as we look at the future of jobs and

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Speaker 1: what is coming next, you mentioned about being more tech

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Speaker 1: savvy for someone who's using some tech but not a

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Speaker 1: lot of tech. Talking to friends, I've had other people

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Speaker 1: that are supposedly experts about this. They sort of say,

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Speaker 1: a lot of the trades are probably less likely to

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Speaker 1: have a major hit to their future earning possibilities by AI,

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Speaker 1: but there is some AI that can be utilized to

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Speaker 1: help them and what they're doing regardless of that. So

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Speaker 1: this is already sort of getting into every aspect of

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Speaker 1: our lives. Anyway, whether it's the algorithms for stuff we're streaming,

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Speaker 1: or even your car that gives you that you know,

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Speaker 1: oh you got you're out of your lane, or you're

1949
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Speaker 1: you're too close to somebody, let's hit that brake for you.

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Speaker 1: I mean, these are all elements that over the last

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Speaker 1: decade have sorely, you know, sort of like bounced into

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Speaker 1: our everyday lives.

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Speaker 5: Correct, Yeah, that's exactly right.

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Speaker 11: Now.

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Speaker 2: If you look, if you want to talk about trades,

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Speaker 2: certainly the we call this the world model, you know,

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Speaker 2: the human elements. Again, AI doesn't have the concept of

1958
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Speaker 2: physics and gravity and things that are in our natural world,

1959
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Speaker 2: so it's a lot harder and people are chasing that problem.

1960
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Speaker 2: So Elon Musk is building you know, millions of robots.

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Speaker 2: We'll get there and there will be people that are

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Speaker 2: replaced from human tasks, just like you know in a factory.

1963
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Speaker 2: But that doesn't mean everything is going to get replaced, right,

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Speaker 2: And so when you go back to any of this,

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Speaker 2: I think we got to look at what that is.

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Speaker 2: So from a technical standpoint, let's talk about that learning

1967
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Speaker 2: what AI is good at and what AI cannot do,

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Speaker 2: at least in the short term. In the midterm and

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Speaker 2: following that and staying on top of it. We had

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Speaker 2: an alumni in this week and he said something to

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Speaker 2: us that we need to teach the students to learn

1972
01:29:52,000 --> 01:29:54,320
Speaker 2: how to learn faster. Right. They need to be able

1973
01:29:54,360 --> 01:29:56,680
Speaker 2: to pick up these things quicker. They need to be

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01:29:56,720 --> 01:30:00,639
Speaker 2: able to adapt to change faster than need to apply

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Speaker 2: those human things that I talked about, like question framing,

1976
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Speaker 2: how do you frame the question? Like that's more important

1977
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Speaker 2: than the technical side. Being technical in this day and

1978
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Speaker 2: age is going to get wiped out before the critical

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Speaker 2: thinking right, interpreting the generation because understanding is this thing

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Speaker 2: that just generated? Is it right? Or is it wrong?

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Speaker 2: How do I know that right? And that comes from

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Speaker 2: experience business context, right, being able to put it in

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Speaker 2: a business context. I give you an example, like for

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Speaker 2: automating an invoice flow. You know, every business has an invoice,

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Speaker 2: Every invoice has a date on it, and every invoice

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Speaker 2: has an amount on it. And so that sounds like

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01:30:36,880 --> 01:30:39,960
Speaker 2: something that could be easily automated. And then you say, okay,

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Speaker 2: well what about medical invoicing? Now just think about the

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Speaker 2: complexities I just introduced, right with insurance companies and doctors

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Speaker 2: and why it's being invoiced and all those things, and

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Speaker 2: so you have to have a domain expertise, and then

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Speaker 2: you have to be able to apply that domain expertise

1993
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Speaker 2: to AI to get results that are worthwhile, because otherwise

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Speaker 2: you're just real eying on a generation, right, I mean

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Speaker 2: word generation that's going to give you answers. How do

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Speaker 2: you know it's right? How do you know that workflow

1997
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Speaker 2: is right? How do you know that what it produced

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Speaker 2: is accurate? And so being able to understand the technical

1999
01:31:13,160 --> 01:31:16,400
Speaker 2: side enough, the domain side enough, and then being able

2000
01:31:16,439 --> 01:31:19,560
Speaker 2: to apply i'll call it the personal traits right, the

2001
01:31:19,680 --> 01:31:24,400
Speaker 2: human elements, the critical thinking, the judgment, the ethical reasoning, accountability,

2002
01:31:24,880 --> 01:31:25,719
Speaker 2: all of those things.

2003
01:31:27,920 --> 01:31:30,479
Speaker 1: Is there still a need for people to do coding

2004
01:31:30,840 --> 01:31:35,040
Speaker 1: and learn different programming languages? Because it was less than

2005
01:31:35,280 --> 01:31:38,639
Speaker 1: a decade to fifteen years ago, hanging out with friends

2006
01:31:38,800 --> 01:31:40,840
Speaker 1: and their kids and talking about the future and what

2007
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Speaker 1: they need. We need coders, we need coders, we need coders.

2008
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Speaker 1: A decade later, it seems like a lot of that

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01:31:45,840 --> 01:31:47,519
Speaker 1: stuff is sort of fallen to the wayside.

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Speaker 3: Is that the case? Or am I misunderstanding where we

2011
01:31:49,920 --> 01:31:50,760
Speaker 3: are in what's coming?

2012
01:31:52,200 --> 01:31:55,160
Speaker 2: No, there is an absolute risk there of that I

2013
01:31:55,680 --> 01:31:58,400
Speaker 2: was Vibe coding this week, which means that I'm not

2014
01:31:58,479 --> 01:32:01,759
Speaker 2: writing any code myself. I'm giving directions to the computer

2015
01:32:01,960 --> 01:32:03,920
Speaker 2: to generate the code. And it was in a language

2016
01:32:03,960 --> 01:32:07,439
Speaker 2: called Python and I and I also was doing some

2017
01:32:07,600 --> 01:32:11,160
Speaker 2: web page stuff in HTML, and so these sound technical.

2018
01:32:11,240 --> 01:32:13,320
Speaker 2: Then you say, okay, well, how did I know what

2019
01:32:13,479 --> 01:32:17,400
Speaker 2: question to ask? How did I know enough about HTML

2020
01:32:17,560 --> 01:32:20,400
Speaker 2: or Python, these languages to ask the question to get

2021
01:32:20,439 --> 01:32:22,360
Speaker 2: what I want? Because I got exactly what I want

2022
01:32:22,439 --> 01:32:24,040
Speaker 2: and I didn't have to write a line of code.

2023
01:32:24,600 --> 01:32:28,360
Speaker 2: So then put that in perspective to students. You may

2024
01:32:28,439 --> 01:32:30,320
Speaker 2: not need to know all the details of how to

2025
01:32:30,400 --> 01:32:32,640
Speaker 2: do it, because I you know, with code complete, and

2026
01:32:33,600 --> 01:32:36,720
Speaker 2: you know generation of code now it's a GitHub copilot

2027
01:32:36,760 --> 01:32:39,280
Speaker 2: and tools like that, it'll generate the code for me.

2028
01:32:39,640 --> 01:32:41,360
Speaker 2: So I go back to how do I know that

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Speaker 2: code is correct? How do I put that code in production?

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Speaker 2: How do I monitor that code is doing the right thing?

2031
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Speaker 2: How do I make sure that code is compliant legally?

2032
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Speaker 2: If it's making a business decision, what kind of decision

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Speaker 2: is it making. Is it making a high risk decision

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Speaker 2: or is it making a low risk decision? And so,

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Speaker 2: while there's lots of tasks out there. If it's folding

2036
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Speaker 2: my laundry like a you know, think about a robot

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Speaker 2: for an example, that's a low risk task and if

2038
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Speaker 2: it makes a mistake, no problem. But if it's making

2039
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Speaker 2: a medical decision, that could be a high risk, right.

2040
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Speaker 2: And so the short answer, yes, I think humans are

2041
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Speaker 2: critical in this in some form or fashion, but also

2042
01:33:16,479 --> 01:33:19,680
Speaker 2: acknowledging people make mistakes too, right, And so I think

2043
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Speaker 2: the collaboration of the technology and the people is what's

2044
01:33:22,800 --> 01:33:23,920
Speaker 2: key getting that balance.

2045
01:33:24,479 --> 01:33:27,800
Speaker 1: Director of AI Strategy and PERI Professor University of Cincinnati's

2046
01:33:27,840 --> 01:33:30,360
Speaker 1: Lender College of Business, Jeff Schaeffer was sterling on the

2047
01:33:30,400 --> 01:33:32,400
Speaker 1: Big One talking about the future of jobs and how

2048
01:33:32,439 --> 01:33:37,040
Speaker 1: we can better position ourselves moving ahead. These skills related

2049
01:33:37,080 --> 01:33:39,760
Speaker 1: to artificial intelligence, and I don't know what you don't

2050
01:33:39,800 --> 01:33:42,080
Speaker 1: call it the right term here, whether it's future proofing

2051
01:33:42,200 --> 01:33:44,719
Speaker 1: whatever else. I just want to place in the future regard.

2052
01:33:44,800 --> 01:33:46,760
Speaker 1: You know, whether you're driving on seventy four now or

2053
01:33:46,840 --> 01:33:49,800
Speaker 1: two seventy five, whatever, and you're thinking like what's next.

2054
01:33:50,000 --> 01:33:52,519
Speaker 1: You know, you're thirty five years old, You've got a family,

2055
01:33:52,560 --> 01:33:54,320
Speaker 1: You're thinking how to how do I make sure that

2056
01:33:54,439 --> 01:33:58,160
Speaker 1: I'm safe? And you think about this protection issue. You

2057
01:33:58,280 --> 01:34:01,720
Speaker 1: talk about data interpretation, the learning, machine learning stuff and

2058
01:34:02,600 --> 01:34:05,680
Speaker 1: problems solving. How do you know is there if I

2059
01:34:05,760 --> 01:34:08,040
Speaker 1: go to UC or if I go to somewhere else,

2060
01:34:08,080 --> 01:34:11,880
Speaker 1: are there online tools that you can sort of take

2061
01:34:12,000 --> 01:34:14,559
Speaker 1: some type of tutorial or something to sort of get

2062
01:34:14,600 --> 01:34:16,880
Speaker 1: a firm grip on it. Because if you're already you know,

2063
01:34:17,040 --> 01:34:19,360
Speaker 1: doing what you do and making your way through the world,

2064
01:34:19,640 --> 01:34:22,520
Speaker 1: sometimes it's hard to make time to do the continuing

2065
01:34:22,680 --> 01:34:25,559
Speaker 1: education that's necessary to stay relevant.

2066
01:34:27,120 --> 01:34:29,800
Speaker 2: Loads of resources out there, right, there's all kinds of

2067
01:34:30,800 --> 01:34:33,920
Speaker 2: YouTube free resources that are available. But if you want

2068
01:34:33,920 --> 01:34:37,160
Speaker 2: to dive deeper into those things, yes, a program like

2069
01:34:37,240 --> 01:34:39,639
Speaker 2: you see. We offer workshops for people who are busy

2070
01:34:39,720 --> 01:34:41,720
Speaker 2: professionals and just want to come learn a little bit.

2071
01:34:42,080 --> 01:34:44,519
Speaker 2: We have certificate programs if you want to dive deeper.

2072
01:34:44,840 --> 01:34:46,760
Speaker 2: We have degree programs, right, if you want to get

2073
01:34:46,760 --> 01:34:49,240
Speaker 2: a degree in this. So if I'm out there thinking

2074
01:34:49,280 --> 01:34:51,360
Speaker 2: about it, I'm gonna not be scared of it. I'm

2075
01:34:51,360 --> 01:34:53,400
Speaker 2: going to try to embrace it in some sense. Even

2076
01:34:53,439 --> 01:34:55,639
Speaker 2: if you're even if you put yourself in the anti

2077
01:34:55,840 --> 01:34:59,560
Speaker 2: AI category, just recognize it's like saying I'm anti automobile

2078
01:34:59,640 --> 01:35:05,680
Speaker 2: right everywhere. And yeah, so so you know you have

2079
01:35:05,840 --> 01:35:08,719
Speaker 2: to figure out how it's going to incorporate into those things.

2080
01:35:08,840 --> 01:35:13,439
Speaker 2: From a standpoint of changing careers or advancing or trying

2081
01:35:13,520 --> 01:35:16,200
Speaker 2: to do something else, I'd probably be thinking about, Okay, well,

2082
01:35:16,240 --> 01:35:19,120
Speaker 2: how can I leverage AI to make me better and

2083
01:35:19,240 --> 01:35:21,800
Speaker 2: faster at doing the things I want to do? Think

2084
01:35:21,840 --> 01:35:23,680
Speaker 2: about if you ever had an idea and you want

2085
01:35:23,720 --> 01:35:27,160
Speaker 2: to build a business, well, gosh, it's probably never been

2086
01:35:27,200 --> 01:35:29,320
Speaker 2: a better time. Think about you know, years ago, how

2087
01:35:29,320 --> 01:35:31,120
Speaker 2: would you get a website? Well, you'd have to hire

2088
01:35:31,200 --> 01:35:34,040
Speaker 2: somebody to go build a website for you. Right today,

2089
01:35:34,080 --> 01:35:35,880
Speaker 2: you don't have to do that. There's loads of places

2090
01:35:35,960 --> 01:35:38,040
Speaker 2: to go and you could spin up a website in

2091
01:35:38,160 --> 01:35:40,280
Speaker 2: a few hours and your you know, your website would

2092
01:35:40,280 --> 01:35:42,240
Speaker 2: be ready to go. And that goes for every single

2093
01:35:42,400 --> 01:35:45,000
Speaker 2: task of what you're doing in the business. You could

2094
01:35:45,520 --> 01:35:48,479
Speaker 2: have ideas and you could brainstorm faster, you can create

2095
01:35:48,560 --> 01:35:52,040
Speaker 2: marketing products faster, you can create content faster, and so

2096
01:35:52,640 --> 01:35:56,320
Speaker 2: it really leverages anybody to be able to kind of

2097
01:35:56,360 --> 01:35:59,360
Speaker 2: level the playing field, if you will. Anybody can level

2098
01:35:59,479 --> 01:36:02,200
Speaker 2: up to do what they need to do in that space.

2099
01:36:02,320 --> 01:36:04,960
Speaker 2: And so yes, go get some training find out how

2100
01:36:05,000 --> 01:36:06,600
Speaker 2: to do some of these things and see if you

2101
01:36:06,640 --> 01:36:09,040
Speaker 2: can leverage them in whether it's your daily work at

2102
01:36:09,080 --> 01:36:11,800
Speaker 2: your office, or whether it's something new you want to try.

2103
01:36:12,479 --> 01:36:13,400
Speaker 3: You know, It's funny.

2104
01:36:13,439 --> 01:36:16,120
Speaker 1: When I was coming up, it was always sterling as

2105
01:36:16,160 --> 01:36:18,439
Speaker 1: it possible you could fix the VCR, so it's not

2106
01:36:18,520 --> 01:36:19,840
Speaker 1: blinking twelve for my mom.

2107
01:36:21,520 --> 01:36:22,759
Speaker 3: And now I get the I don't.

2108
01:36:22,640 --> 01:36:24,840
Speaker 1: Understand why I can't get from YouTube back to the

2109
01:36:24,920 --> 01:36:27,519
Speaker 1: iHeartRadio app and then back to Netflix. Why can't I

2110
01:36:27,600 --> 01:36:29,680
Speaker 1: just just I'm like, look, it's a different device. It's

2111
01:36:29,680 --> 01:36:32,840
Speaker 1: a whole nother scenario. There's always this gap as we

2112
01:36:32,960 --> 01:36:35,519
Speaker 1: sit here now. If you have kids and they're coming up,

2113
01:36:35,640 --> 01:36:37,120
Speaker 1: and I've seen it with my friends and some of

2114
01:36:37,200 --> 01:36:41,160
Speaker 1: my relatives kids, they're born with the device in their hand.

2115
01:36:41,200 --> 01:36:43,920
Speaker 1: They're already aware of what this is, or maybe don't

2116
01:36:44,000 --> 01:36:46,320
Speaker 1: have an idea is to giving it a name, to

2117
01:36:46,360 --> 01:36:50,360
Speaker 1: say it's artificial intelligence. But they're born in living in it,

2118
01:36:50,439 --> 01:36:53,120
Speaker 1: in a wash in it. Are they feeling stressed? Should

2119
01:36:53,160 --> 01:36:55,720
Speaker 1: they feel stressed? Or is this just as simple and

2120
01:36:55,840 --> 01:37:00,600
Speaker 1: easy for adaptation as it was maybe for coming up.

2121
01:37:02,240 --> 01:37:03,400
Speaker 5: I think that's a real.

2122
01:37:03,360 --> 01:37:05,960
Speaker 2: Challenge for parents and that's that's not just an AI thing.

2123
01:37:06,000 --> 01:37:07,800
Speaker 2: I think that was a result of you know, our

2124
01:37:07,880 --> 01:37:12,560
Speaker 2: social media generation, as content got shorter and faster and

2125
01:37:12,840 --> 01:37:16,719
Speaker 2: in your face, right through reels, tiktoks even now LinkedIn

2126
01:37:16,960 --> 01:37:20,360
Speaker 2: and Instagram and you know, all these channels and it's

2127
01:37:20,400 --> 01:37:23,559
Speaker 2: it's just kind of flowing, and that makes it hard

2128
01:37:23,640 --> 01:37:25,680
Speaker 2: because there's a lot more noise out there. And then

2129
01:37:25,760 --> 01:37:29,880
Speaker 2: throw the AI component on that weaponized you know, social media,

2130
01:37:30,479 --> 01:37:32,800
Speaker 2: and now you have content you don't know whether it's real,

2131
01:37:32,920 --> 01:37:35,880
Speaker 2: you don't know whether it's fake, you don't know you know,

2132
01:37:35,920 --> 01:37:38,880
Speaker 2: why we're so polarized. All those things could probably come

2133
01:37:38,960 --> 01:37:41,360
Speaker 2: back to those those elements, So that that is a

2134
01:37:41,439 --> 01:37:44,479
Speaker 2: hard challenge to counteract that. I would say, you know,

2135
01:37:44,600 --> 01:37:46,880
Speaker 2: we have to slow down a little bit, you know,

2136
01:37:47,040 --> 01:37:49,840
Speaker 2: work fast with technology, but slow down when it comes

2137
01:37:49,880 --> 01:37:54,360
Speaker 2: to the critical thinking, the discernment, those again, those human elements,

2138
01:37:54,479 --> 01:37:58,000
Speaker 2: because that's that's how we're going to have to implement

2139
01:37:58,080 --> 01:37:59,519
Speaker 2: and apply these these tools.

2140
01:38:00,360 --> 01:38:03,320
Speaker 1: Jeff Safer is the director of Artificial Intelligence Strategy, the

2141
01:38:03,400 --> 01:38:07,759
Speaker 1: Perry Parry Professor, University of Cincinnati Lindner College of Business.

2142
01:38:07,760 --> 01:38:09,479
Speaker 1: AI should help me be able to slow down and

2143
01:38:09,640 --> 01:38:15,360
Speaker 1: enunciate that would help. What about the issue of understanding

2144
01:38:15,520 --> 01:38:18,759
Speaker 1: what people are going through in the eighties to the nineties,

2145
01:38:18,880 --> 01:38:23,920
Speaker 1: huge technological change, industry change, russ Belt issues people, you know,

2146
01:38:24,200 --> 01:38:27,800
Speaker 1: the despondency in all the things. As time moves on,

2147
01:38:28,040 --> 01:38:32,040
Speaker 1: we saw people left behind, multi generations dealing with emotional stress,

2148
01:38:32,439 --> 01:38:35,759
Speaker 1: not necessarily balancing back and taking a couple of decades

2149
01:38:35,800 --> 01:38:38,240
Speaker 1: really to even navigate that. In some cases where we

2150
01:38:38,360 --> 01:38:41,280
Speaker 1: sit right now in twenty twenty six, I'm curious about

2151
01:38:41,320 --> 01:38:45,479
Speaker 1: the issue of ethics and understanding what is coming because,

2152
01:38:46,160 --> 01:38:49,360
Speaker 1: as you've described it and many others have, what's ahead

2153
01:38:49,400 --> 01:38:51,120
Speaker 1: of us is supposed to be in what we're in

2154
01:38:51,160 --> 01:38:54,120
Speaker 1: the middle of or in the early stages, is supposed

2155
01:38:54,120 --> 01:38:57,960
Speaker 1: to be what the most dramatic change in human history

2156
01:38:58,439 --> 01:39:02,000
Speaker 1: and advancement of technology. And that's a lot to deal with,

2157
01:39:02,280 --> 01:39:06,560
Speaker 1: not just hey, an auto industry or something was outsourced overseas,

2158
01:39:06,680 --> 01:39:09,040
Speaker 1: and now how do I have purpose. There's a lot

2159
01:39:09,120 --> 01:39:09,960
Speaker 1: of layers to that.

2160
01:39:11,800 --> 01:39:14,559
Speaker 2: It's complicated and it's a lot to figure out. There

2161
01:39:14,600 --> 01:39:17,360
Speaker 2: are things that we certainly should should be scared of

2162
01:39:17,720 --> 01:39:21,479
Speaker 2: in that realm, but there's there's so many unknowns, right,

2163
01:39:21,800 --> 01:39:23,720
Speaker 2: you know, we talked, I think on the last time

2164
01:39:24,400 --> 01:39:28,280
Speaker 2: about h You know, lower level jobs are getting replaced, right,

2165
01:39:28,360 --> 01:39:31,400
Speaker 2: you know, they're they're they're only keeping the higher level,

2166
01:39:31,479 --> 01:39:34,240
Speaker 2: more skilled people. And the problem with that is is

2167
01:39:34,320 --> 01:39:37,000
Speaker 2: where do those highly skilled people come from? Right? They

2168
01:39:37,320 --> 01:39:40,320
Speaker 2: come from lower level jobs that are trained up. And

2169
01:39:40,560 --> 01:39:42,559
Speaker 2: so we have to think through that. You know, it's

2170
01:39:42,560 --> 01:39:44,280
Speaker 2: the same thing with coding. You could say, oh, well,

2171
01:39:44,360 --> 01:39:46,439
Speaker 2: don't be a coder because we don't know anybody, you know,

2172
01:39:46,520 --> 01:39:48,599
Speaker 2: we're not going to need to code. Well, where did

2173
01:39:48,600 --> 01:39:50,680
Speaker 2: the computer learn to code? Right? It didn't learn to

2174
01:39:50,760 --> 01:39:52,679
Speaker 2: code on its own. It learned to code from best

2175
01:39:52,760 --> 01:39:56,439
Speaker 2: practices of people doing it for decades. And so we

2176
01:39:56,640 --> 01:39:59,960
Speaker 2: are the engine that feeds that knowledge and we shouldn't

2177
01:40:00,040 --> 01:40:03,400
Speaker 2: forget that, right, And so yeah, where do we fit

2178
01:40:03,520 --> 01:40:07,080
Speaker 2: in the picture? This is akin to back in the

2179
01:40:07,200 --> 01:40:11,160
Speaker 2: day we had automation that replaced muscles. You know. Now

2180
01:40:11,280 --> 01:40:15,479
Speaker 2: we're at automation trying to replace cognition. And it's not there,

2181
01:40:15,760 --> 01:40:17,960
Speaker 2: you know, it's it's getting there, and it's moving, and

2182
01:40:18,000 --> 01:40:20,840
Speaker 2: it's moving very fast. It can do things that are

2183
01:40:21,240 --> 01:40:25,760
Speaker 2: either very domain specific that humans can't do, or it

2184
01:40:25,840 --> 01:40:29,280
Speaker 2: can do generalized things not as well as humans can do.

2185
01:40:29,439 --> 01:40:32,000
Speaker 2: And so that's where we are at the moment. Where

2186
01:40:32,040 --> 01:40:34,639
Speaker 2: we go in the future, you know, people are chasing

2187
01:40:34,720 --> 01:40:36,880
Speaker 2: the idea that a computer will be better at us

2188
01:40:37,000 --> 01:40:41,160
Speaker 2: than everything at some point agi artificial general intelligence. I

2189
01:40:41,360 --> 01:40:44,760
Speaker 2: don't know if we'll get there anytime soon, but that

2190
01:40:45,240 --> 01:40:47,720
Speaker 2: is the that is the goal, right, And so in

2191
01:40:47,800 --> 01:40:50,080
Speaker 2: that world, then you really have to think about where

2192
01:40:50,160 --> 01:40:52,960
Speaker 2: we are. That's that's a human race sort of question, right.

2193
01:40:53,040 --> 01:40:56,240
Speaker 2: But I think today we have to think about keeping

2194
01:40:56,360 --> 01:40:59,920
Speaker 2: that human in the loop in some form or fashion,

2195
01:41:00,840 --> 01:41:04,040
Speaker 2: and at least for all of those things I outlined right,

2196
01:41:04,360 --> 01:41:08,160
Speaker 2: just to make sure that we are providing that critical thinking,

2197
01:41:08,280 --> 01:41:12,840
Speaker 2: the problem solving those things that today are very very

2198
01:41:12,840 --> 01:41:14,240
Speaker 2: important in those processes.

2199
01:41:15,200 --> 01:41:17,880
Speaker 1: Before we let you go, and I really appreciate you

2200
01:41:18,000 --> 01:41:20,639
Speaker 1: you making time, and I hope you'll come back. Jeff Schaeffer,

2201
01:41:20,680 --> 01:41:22,640
Speaker 1: by the way, is the director of AI Strategy and

2202
01:41:22,680 --> 01:41:26,160
Speaker 1: Peri Professor University of Cincinnati's Lendard College of Business. In

2203
01:41:26,280 --> 01:41:29,479
Speaker 1: about a minute or so, is someone's driving around right

2204
01:41:29,560 --> 01:41:31,240
Speaker 1: now that got the kids in the car coming back

2205
01:41:31,240 --> 01:41:33,320
Speaker 1: from brunch church whatever else it is that they got

2206
01:41:33,360 --> 01:41:35,080
Speaker 1: going on in this beautiful day in the Troy State.

2207
01:41:35,080 --> 01:41:37,720
Speaker 1: If you like cold weather especially and you're trying to

2208
01:41:37,720 --> 01:41:39,800
Speaker 1: think of Okay, if I want to get a plan

2209
01:41:39,920 --> 01:41:42,120
Speaker 1: together and I want to start working this, you don't

2210
01:41:42,120 --> 01:41:44,000
Speaker 1: want to stress out. You don't want to be overwhelmed.

2211
01:41:44,200 --> 01:41:47,800
Speaker 1: What's the first thing someone should do if they're looking

2212
01:41:47,880 --> 01:41:49,360
Speaker 1: to try to find a way to get to the

2213
01:41:49,439 --> 01:41:49,960
Speaker 1: next level?

2214
01:41:51,400 --> 01:41:53,719
Speaker 2: How about a fun exercise? How about we use AI

2215
01:41:53,880 --> 01:41:56,479
Speaker 2: to help us answer that question? Go out to many

2216
01:41:56,520 --> 01:42:01,000
Speaker 2: many free resources, Google, Gemini, Chat, GPT, Claude. You can

2217
01:42:01,120 --> 01:42:03,840
Speaker 2: use their free tool and use AI and try it

2218
01:42:04,000 --> 01:42:06,320
Speaker 2: and ask those questions and see if you can formulate

2219
01:42:06,400 --> 01:42:08,920
Speaker 2: a plan. Spend ten or fifteen minutes going back and

2220
01:42:09,040 --> 01:42:12,600
Speaker 2: forth prompting and asking questions like that, what could I do?

2221
01:42:12,760 --> 01:42:15,040
Speaker 2: Where could I go? What about researching? And just give

2222
01:42:15,080 --> 01:42:16,920
Speaker 2: it a try and see what it finds for you,

2223
01:42:17,360 --> 01:42:20,000
Speaker 2: because you'll, I think most people who aren't familiar with

2224
01:42:20,120 --> 01:42:22,280
Speaker 2: that will be amazed as what you could get done

2225
01:42:22,320 --> 01:42:24,800
Speaker 2: and what you could do in fifteen minutes or twenty

2226
01:42:24,840 --> 01:42:27,880
Speaker 2: minutes or thirty minutes. And that would be a way

2227
01:42:28,000 --> 01:42:30,560
Speaker 2: to kind of get your feet wet into something like

2228
01:42:30,640 --> 01:42:33,040
Speaker 2: that and then maybe go explore some training on this,

2229
01:42:33,200 --> 01:42:35,480
Speaker 2: whether it's a U see at the University of Cincinnati

2230
01:42:35,640 --> 01:42:38,160
Speaker 2: or somewhere else. Go out and look for some classes

2231
01:42:38,200 --> 01:42:39,760
Speaker 2: and say, hey, you know what, I'm gonna sit through

2232
01:42:39,800 --> 01:42:42,400
Speaker 2: a few hours of this and see what I can learn,

2233
01:42:42,479 --> 01:42:44,320
Speaker 2: see what I can pick up from that, and then

2234
01:42:44,400 --> 01:42:46,439
Speaker 2: see what I can apply pack it at my job.

2235
01:42:46,840 --> 01:42:48,879
Speaker 3: Give me a solution for my future.

2236
01:42:48,960 --> 01:42:51,720
Speaker 1: It's the right question, prompted the right way to the

2237
01:42:51,880 --> 01:42:55,400
Speaker 1: right AI could perhaps be the difference in everything. It's

2238
01:42:55,439 --> 01:42:57,000
Speaker 1: good to talk to you. I appreciate you making time.

2239
01:42:57,040 --> 01:42:58,920
Speaker 1: I hope you enjoyed the rest of your weekend. Jeff

2240
01:42:58,960 --> 01:43:02,200
Speaker 1: Schaeffer's the direct actor of AI Strategy. Perry Professor, University

2241
01:43:02,280 --> 01:43:04,880
Speaker 1: of Cincinnati, Linder College of Business. Thank you for making time.

2242
01:43:04,920 --> 01:43:06,680
Speaker 1: Take care of yourself, Jeff. We'll check it in soon.

2243
01:43:07,680 --> 01:43:08,640
Speaker 2: Always great talking with you.

2244
01:43:08,800 --> 01:43:09,240
Speaker 3: Take care of.

2245
01:43:09,240 --> 01:43:12,200
Speaker 1: Yourself straight away. The news more Sterling Home of the

2246
01:43:12,240 --> 01:43:13,880
Speaker 1: Red seven hundred WLW