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Marcus Johnson (00:00):

In marketing, everything must work seamlessly or efficiency, speed, and ROI all suffer. That's why Quad is obsessed, maybe too much, with making sure your marketing machine runs smoothly. With less friction and smart integration, better marketing is built on Quad. See how better gets done at quad.com/buildbetter.

(00:31):

Hey, gang, it's Friday, July 11th. Kathleen, Gadjo, and listeners, welcome to Behind the Numbers, an EMERKETER video podcast made possible by Quad. I'm Marcus, and joining me for today's conversation we have two people. Let's meet me. We start with our Senior Analyst covering everything AI and technology based in New York, it's Gadjo Sevilla.

Gadjo Sevilla (00:50):

Hey, thanks for having me back.

Marcus Johnson (00:51):

Yes, sir. Of course, of course. Kathleen, it sounds like we banned him for a while. Okay, that didn't happen. He's always been welcome. Gadjo, welcome back to the show. Professor and AI Advisor to the Deans at Royce Business School, and Founder and CEO of AI company DemystifAI based in Texas is Kathleen Perley joining the podcast. Welcome.

Kathleen Perley (01:14):

Thank you so much for having me, excited to be here.

Marcus Johnson (01:16):

Of course, of course, of course. Thank you for joining us. We start with a speed intro anytime we have an external guest on to get to know them a little bit better. So around 60 seconds on the clock, which we pay no attention to, let's do it.

Kathleen Perley (01:30):

So fun.

Marcus Johnson (01:31):

First question, what do you do in a sentence?

Kathleen Perley (01:36):

So I'm a linguist turned entrepreneur turned academic, and now I focus on making AI simple and real, and helping executives and entrepreneurs really navigate this new world and using it to solve problems, not as a magic wand, but really rethinking their strategy.

Marcus Johnson (01:55):

Okay. You said you are a linguist turned entrepreneur turned, and I thought you were going to say actor, and I was like, "Oh, no. Who did we book this for the show?"

Kathleen Perley (02:03):

Not with this face.

Marcus Johnson (02:06):

"These are all wrong questions." All right, academic, thank God. Second question, if you could master one skill overnight, what would it be?

Kathleen Perley (02:15):

So I actually thought about this long and hard, and I wanted to say something really profound like the art of storytelling or something.

Marcus Johnson (02:22):

Oh, no. Where are we going?

Kathleen Perley (02:24):

I will tell you it's actually doing hair.

Marcus Johnson (02:29):

Okay. Your own?

Kathleen Perley (02:31):

My daughter's hair.

Marcus Johnson (02:33):

Yes.

Kathleen Perley (02:34):

Any hair. The thought of having to use a blow dryer in one hand and a hair brush in the other hand at the same time is a foreign language to me. It's physics. I went to an all-girls Catholic school K through 12, so it was just not needed, and I never developed the skill. Went to college, literally would iron my hair with an ironing board.

Marcus Johnson (02:59):

Huh? That's no wonder.

Kathleen Perley (03:02):

And now I have girl who has beautiful curly hair and I still am at a loss.

Marcus Johnson (03:06):

Crew cuts, I think is what you're looking for.

Kathleen Perley (03:08):

Yeah.

Marcus Johnson (03:08):

Send them into the military.

Kathleen Perley (03:09):

Yeah.

Marcus Johnson (03:10):

That'll be fine. Gadjo, what about you?

Gadjo Sevilla (03:13):

Well, for me, I think I've always wanted to be an auto mechanic or a watchmaker.

Marcus Johnson (03:18):

Oh, nice.

Gadjo Sevilla (03:18):

So something that has to do with working with your hands, something AI isn't going to replace anytime soon. So yeah, something analog and just tools.

Marcus Johnson (03:33):

Fascinating.

Gadjo Sevilla (03:33):

And wrenches. Yeah.

Marcus Johnson (03:34):

Huh. Why? Was someone in your family that?

Gadjo Sevilla (03:39):

No, no one was. But then, I've always felt that it's an extra skill, really, that more people should be able to do to some extent.

Marcus Johnson (03:51):

Yeah.

Gadjo Sevilla (03:53):

They never taught it to us at school, not even as a basic course.

Kathleen Perley (03:59):

Could you change your oil or your tire?

Gadjo Sevilla (04:02):

Tires, yes, oil, it depends.

Kathleen Perley (04:05):

Okay.

Gadjo Sevilla (04:06):

Could do it. But I live in an apartment, so I need to find a place to be able to do that. But yeah, there's that limitation, right? You're like, "I wish I could fix this." Right?

Marcus Johnson (04:19):

Yeah. That might just be you, Gadjo. I'm renting a car right now based on where I live. And the change oil light came on, I took it back and asked them for another one.

Gadjo Sevilla (04:31):

That's one way to do it.

Marcus Johnson (04:33):

Yeah.

Gadjo Sevilla (04:34):

Change the whole car.

Marcus Johnson (04:35):

Yeah. Yes. The woman was not impressed.

Kathleen Perley (04:37):

I'm not a pro by any means, but after I had somewhat of a crisis, I actually got a 1972 International Scout that my husband and I have been fixing up in our garage.

Marcus Johnson (04:51):

Oh.

Kathleen Perley (04:52):

So we replaced the wiring harness. We've done transmission fluid, replaced the seats.

Marcus Johnson (05:02):

What kind of a crisis led you to buy a classic car?

Kathleen Perley (05:04):

It was kind of one of those same things. I had just sold and exited my company. I was just going to be a professor, and so I was like, "Oh man, I'm going to be the coolest professor if I roll up in a 1972 purple International Scout that's decked out. This is so fun. And it's been fun. It is been a money pit, but it's been fun.

Marcus Johnson (05:26):

Oh, okay. So you meant just like a moment crisis.

Kathleen Perley (05:30):

Yeah. It was more of those things like, "What am I going to do with my time?"

Marcus Johnson (05:32):

Oh, okay. Not like, "Oh, the house has burned down. Let's buy a classic car."

Gadjo Sevilla (05:36):

By the time you're done, you'll be an expert on that as well.

Marcus Johnson (05:39):

Yeah. Nice to get to know, I guess. A little bit better for today's episode, today's real topic, how to approach AI the right way. So talking AI again, and I want to start with this question because when me and Kathleen were speaking before this episode a week or two ago, one of the ways that she had described adopting AI was making it more approachable, which I thought was an interesting way. Because I've heard AI adoption a lot, but not heard the word approachable used much when it comes to artificial intelligence. So Kathleen, how do you make AI approachable?

Kathleen Perley (06:30):

So I think we talked about this a little bit, Marcus. So growing up I had dyslexia, right? So everything was complete noise to me, and I always had to figure out what was the pattern and identify that and develop analogies to help me understand things. And so, I've kind of taken that approach. And it's really about stripping away the smoke and mirrors so that people can start to feel comfortable and understanding it, because I think that is probably the biggest intimidation factor.

(06:55):

And since we've been talking about cars, I'll use the car analogy. I'm born and raised in Houston, Texas. So I come from a long line of actually New York transplants to Texas in oil and gas sector. And so, when I told my parents I wanted to be a linguist, they were not super happy, because they were like, "Your options are banking or oil and gas." And it was in November '22 and I finally realized, we had a family dinner. My dad turned to me and was like, "Have you heard this ChatGPT something?" And I was like, I pushed my sister who was like Wharton MBA, oil and gas private equity. I was like, "My time has arrived." We had Alien, that movie that came out where they brought in the linguist, and now I am somewhat relevant again, this is great. But it's really because there hasn't been a ton of advancements in AI from a technical standpoint since 2017. But the reason why I waited until 2022 was that we really saw a UX, UI expansion. It became easier for everyone to use.

(07:57):

And one of the analogies when I talk about making AI approachable is the idea of a car. So if I had to manipulate a car engine to get from my house to campus every day, I will tell you, most of my students don't think I would make it. Right? But we have a steering wheel, we have a gas pedal. We have gears that we can change based on the road conditions.

(08:19):

And the same is true with AI. You're working with an interface, the engine. And the powerful model is the models that are underlying it, like the O3-Pro model or something of that nature. But you're interacting with a steering wheel, so you're guiding it. You're able to utilize your gas and brake as almost like inference speed. So you want it to speed up because you need something quick and you don't need as much chain of thought reasoning. So you could want a fast, quick answer. Or do you let up on it so you would give the model time to think? And then do you switch gears? So do you think about it...

(08:51):

So I kind of try to use these analogies. When you think about it, it's just using that analogy and thinking about the fact that AI has become democratized, and so how do we make it approachable? And I think the first step is removing the smoke and mirrors, breaking down the language, and using examples that people can relate to.

Marcus Johnson (09:09):

Gadjo, how possible is that? Because there seems to be a growing knowledge gap between where people are and where the technology is going. And even though people are moving, you are seeing the technology move at such a faster speed that it seems like it's almost going to be impossible for them to catch up and for them to become AI literate, so to speak. So what would you make of making AI approachable? And do you think it's possible to get people up to speed or even close to where the technology is today, let alone in the future?

Gadjo Sevilla (09:40):

Well, you're right about that. It's super-fast. We're seeing innovations almost every week. And like what Kathleen was saying, it really is the user interface. That's really the only mode of control users have, so refining that part of it so that it's more intuitive. And I don't mean a drop-down of six different models. That doesn't really help a lot of people; but maybe demonstrating in simple terms that, "You choose this model, it could do A, B, and C really well. But it's going to burn more trees or use more electricity." Just giving the whole equation, I think, could help people find what they need.

Marcus Johnson (10:30):

Yeah.

Gadjo Sevilla (10:30):

And maybe that will let them sort of understand how to use it to their advantage.

Kathleen Perley (10:37):

Yeah. And I think it's not just about how you use it, but also understanding the basics. And if you talk to anyone I've ever worked with, I have an analogy for everything, which sometimes they're absolutely terrible. And not to say that this one won't be, but I find that even when I try to explain mixture of experts, I talk to them about like, "Hey, when you have an ailment, do you go to the hospital and they have a cardiologist, a neurologist, a general practitioner, a GI doctor? Does everyone show up to the room?" No, because really inefficient.

(11:11):

So when you think of an MOE architecture, it's very similar. You have a switch like a primary care that then identifies what is the best area to send you. And so, I think having... They don't need to understand neural networks at depth. They don't need to understand the ins and outs in terms of the technology nuance. But understanding how the cause, if A, then B, then C, and how all the levers are related are key. And I think whether it's the MOE example or I use another one, oftentimes being a native Texan, a margarita, right? Making a model is similar to making a cocktail. You have recipe cards, which are kind of like chain of thought. If you have bad limes or really bad tequila, you're going to have a really bad hangover. It's like data. Right? And so, it's understanding the different levers that go into place and providing analogy that people can relate to, and being able to understand it from a frame of reference to say, "Okay, if something goes wrong, I know where the potential breaking points are."

Marcus Johnson (12:14):

Yeah. What are some questions that you think are good for folks to ask themselves when they're adopting AI or approaching AI?

Kathleen Perley (12:28):

I think the real one is are you willing to make a shift or a change? Is this going beyond just experimentation? I think from a business perspective, are you going to stay in pilot purgatory forever or are you actually going to invest in restructuring your processes so you're not...

(12:52):

And it's interesting, because I think Marcus, you and I talked about this and I'd love to get, Gadjo, your thoughts on this too. It's like a chicken and egg. Because I've tried it both ways and I still haven't found the sweet spot. Ideally, when I work with companies or when I'm working at Rice and talking to businesses and Fortune to 500 executives that are coming up, I want them to think about AI and how do they readjust their processes as an organization to unlock the full potential of AI; not saying, "This is what we've always done. Now let's layer AI into that process." Right? They should rethink everything.

Marcus Johnson (13:27):

Yeah.

Kathleen Perley (13:28):

But I find that if I go that route first, it's often too intimidating to a lot of leaders or business because they just don't understand the full potential. And so, they kind of need to say, "Okay, what am I doing today? How can I use AI to create efficiency?"

Marcus Johnson (13:45):

Yeah.

Kathleen Perley (13:45):

But efficiency is like table stakes.

Marcus Johnson (13:48):

Right, right. I liked the way, in our conversation, Gadjo, Kathleen had said that the question being, "How can AI change what you offer and how you do it, as opposed to just improving on what you currently do?"

Gadjo Sevilla (14:03):

Right. Right.

Marcus Johnson (14:03):

And I feel like she was saying it is hard to get people to the more seismic changes that it could offer because people just want to tinker first.

Gadjo Sevilla (14:14):

Yeah. I think a lot of that is because companies all of a sudden have AI initiatives. They have budgets, right? And so yeah, they're all-in on adoption. But then, do they ask themselves really what problems are they trying to solve? Are they improving processes? Can they use the tools judiciously? And I don't think they spend maybe enough time evaluating before they jump in. Right? And that's when you find problems rolling it out. Because if you don't evaluate properly, you can't really inform your employees about the benefits, right?

Marcus Johnson (14:56):

Mm-hmm. Yeah. When we were speaking before, we talked about how do you implement it? Do you start with the workers and say, because you just mentioned, Gadjo, what's the problem you're trying to solve? To me, that seems easier at the worker level. As a worker, I know the pain points in my life, I know what I would like it to solve, and then you can kind of implement that. But Kathleen, you were saying actually, there are a lot of benefits to that top-down, to that CEO, that senior person having a vision for what they want to achieve and going about it that way. Tell us about some of the thinking there.

Kathleen Perley (15:35):

Yeah, I think just given the clients I've worked with and organizations I've worked with, I have a lot of companies who are doing more of an employee-up approach, which I think there's some value to letting them experiment, kind of get their feet wet. But what I'm seeing is those companies are really focusing on just that employee thinking about it from their day-to-day perspective. They're not thinking about how all the silos connect or they're not thinking about how you scale it or measure the success of the AI pilot.

(16:04):

I think there's a stat recently from McKenzie, 19% of organizations actually have KPIs for their AI initiatives. So there's about 80% of them that don't.

Gadjo Sevilla (16:15):

That don't. Yeah.

Kathleen Perley (16:16):

Which is just terrifying. And I think that's where it gets to the real need to have the CEO involved as a visionary. They're the ones that are looking at, "What does our future look like as an organization? Where are we going strategically?" And AI is a strategic play, not a tech play.

(16:35):

And I agree with Gadjo, and I have a framework in my book that I have that talks about what's the business challenge or opportunity? How do you quantify the value of it? Is it time-saving, more revenue generated, better quality? How do you measure that? What does that look like in year one? What does that look like in year three? What is the cost implication? What's the change impact implication? Right? And so, I think there's a real strategic way that every organization needs to go about it, but I think it's time that CEOs and C-suite start with getting really involved and not see this as a delegated down initiative.

Marcus Johnson (17:17):

Yeah. Yeah, I like that framing of particularly the what does this look like in year one, in year three? It seems like people want, even if they're experiments, they expect to reap all of the rewards tomorrow, as opposed to a little bit tomorrow and then maybe more in the future.

Gadjo Sevilla (17:35):

Yeah. That's why efficiency is usually the easiest one, right? They're like, "Oh, we've reduced headcount by X percent, and therefore..." And therefore what? You can do that, but what is the long-term effect, right?

Marcus Johnson (17:52):

Yeah. Yeah, we'll save this much time.

Gadjo Sevilla (17:55):

It could be a short-term boost, but is it strategic in the long-term? And do you really retain any value from that application?

Marcus Johnson (18:05):

Yeah.

Kathleen Perley (18:06):

And does it scale?

Gadjo Sevilla (18:07):

Yeah.

Kathleen Perley (18:09):

That company that creates efficiency cuts head count. Okay, they're going to be successful in the next two to three years. The company that's going to really kill it, the superstars are the companies who are thinking, "Okay, we've created this efficiency. What do we do with this extra? What can we do now? What's the next thing? What's the next market? What's the next challenge? What's the next value chain thing that we need to rethink?" Those are the companies that are going to be disruptive, because at a certain point, the efficiency game is going to become table stakes, and you're going to lose that competitive advantage.

Gadjo Sevilla (18:41):

Yeah. They need to be steps ahead, like you're saying, right? It's not just the immediate effects, but also the long-term goals on a number of levels.

Kathleen Perley (18:53):

And I will say most C-suite, and I'd love to hear if you have the same experience, most of the C-suite or CEOs that I work with, they have very much so a, "Oh, I use AI for pulling out a party trick or a poem randomly or something silly." And the McKinsey study that came out recently shows that we have a huge jump in terms of AI adoption, but I think one in five companies are seeing a 5% addition to their bottom line. So there's really low kind of-

Gadjo Sevilla (19:27):

ROI. Yeah.

Kathleen Perley (19:30):

ROI. And those companies that had the highest ROI, the biggest contributor to success was whether or not the CEO was involved in the AI vision and direction and the counseling of where the organization was going. And today, only about 28% of CEOs are involved in the AI strategy.

Marcus Johnson (19:48):

Wow. Wow. And I guess that involvement probably varies across the board, from barely to completely.

Kathleen Perley (19:56):

Yeah. And I will say I've worked with a wide range. I don't know if you know Marie Myers at Hewlett Packard Enterprises, she's the CFO there. She's fabulous. And she is hands-on and she is pushing her team. And she's thinking about what are the challenges the organization's facing, how can AI help integrate it, how they can do more with more to meet their future needs. And when I say she's in it, I worked with her over coffee recently on building her own AI voice agent to talk to while she was going to and from the car. And she didn't want me to build it for her. She was like, "No, no, I want you to show me how to build it." And I think those types of leaders are few and far between, but you need that.

Marcus Johnson (20:45):

Yeah. So you mentioned you have a new book, it came out this year, your book?

Kathleen Perley (20:51):

Yeah. It came now June 3rd or something like that.

Marcus Johnson (20:53):

It just came out, right? Yeah, hot of the press, called AI Made Simple. Results Made Real: An Executive's Guide to Partnering with the Future. And in that, so you have a chapter, I think it's five. I thought it was an interesting title of a chapter, because two questions came from it for me. One was, the title was called, Is Your Organization Ready for AI? And my first question was, how do you know? But then, my second question right after that was isn't everybody's?

Kathleen Perley (21:24):

Yes and no. So it's one of those things I feel like it's kind of the equivalent of kids. You're like, "Are you ever really ready for kids?" No.

Marcus Johnson (21:33):

Right.

Kathleen Perley (21:33):

Do you have a car that can put a car seat in it? Okay. Maybe that's... But you're never really ready. But there are certain aspects, and in the book I kind of have a quick little assessment. And I joke, I told some of my students this, it's like, do I want to have beach body ready by May? Yeah. But do I need to be Victoria's Secret model level or just not scaring children off when I walk on the beach? Right? And so, there's a balance between it.

(22:06):

And I think there are some foundational things. Do you have data governance? Do you have certain ways that you're thinking about how AI can be applied? Do you have people who can lean in and take the initiative with it and invest in it? Because I think there's a lot of times where I see a lot of start/stop; where they buy the enterprise license, they roll it out, kumbaya, and no one touches it. And you have a few star people across the board, but even then, I'm shocked when I talk to them, I'm like, "Oh, you guys are using deep research, obviously." And they're like, "I didn't know that was a thing." They didn't know they could switch the model. And it's because they've kind of been a, "Let's experiment" and bottom-up approach with no one kind of taking the initiative. So I think there is some basis, right? But you don't have to be perfect, right?

Marcus Johnson (23:00):

Right.

Kathleen Perley (23:00):

You don't have to have-

Marcus Johnson (23:02):

You can be a bit more... Yeah, you can be... Like if you're going to have kids, I'm sure having some savings, having family nearby to take care of the kids for you when you need, that probably helps. But you can be an amazing parent without that.

Kathleen Perley (23:17):

Exactly.

Marcus Johnson (23:17):

You can still get it done. Yeah. Yep. What was one of the most interesting findings from the book, in your opinion?

Kathleen Perley (23:26):

One is that AI is just like a SAP implementation. It's going to be painful. It's going to take a while before you see the results, but turn it over to IT and they'll get it done. And what I found is the leaders who are successful within organizations oftentimes don't have an IT background.

Marcus Johnson (23:45):

Interesting.

Kathleen Perley (23:46):

And so, that is probably one of my bigger insights.

Marcus Johnson (23:50):

Yeah. Yeah. So what do they have?

Kathleen Perley (23:54):

So they tend to be, and I say this as a former digital marketer, and so I know it sounds like I may be tooting my own horn. But there's some value in people who have played in the digital marketing space. I've written schema. I can kind of hack through some code. I'm used to rolling out a whole social strategy for an organization on Vine, and then have Vine disappear six months later and have to throw it all away. I'm used to measuring. I'm used to testing. I'm used to failing. I'm used to educating. That was a big part of my role as a digital marketer in the early 2010s. Because I also think part of AI implementation is marketing and advertising. Right?

Marcus Johnson (24:38):

Yeah.

Kathleen Perley (24:38):

How do you sell it internally? How do you sell it externally? You have to see the bigger picture. So I found the people that have been really successful in this role tend to be digital marketer backgrounds who have a little touch of tech, but not too much that they're hand-strapped in terms of they think that you have to have... They over-engineer it.

Gadjo Sevilla (25:02):

So they're not mired down by the technology. Right?

Kathleen Perley (25:07):

Yeah.

Gadjo Sevilla (25:08):

That's good. Kind of self-monitoring.

Kathleen Perley (25:12):

And I have a lovely friend. He's in London actually and he's going to kill me for saying this. He's a Cloudflare engineer and he's fabulous. But sometimes I'm like, "I know more than you do on some of this stuff." And it's because he's like, "I haven't played in this space. You were doing machine learning algorithms and natural language processing for years." And I've always been the type of person that hacked/duct taped stuff together with APIs to see if they were-

Marcus Johnson (25:43):

Figure out. Mm-hmm.

Kathleen Perley (25:45):

And I just got a lot more powerful over the last 18 months than I've ever been. But I think some of my engineering friends, they're fabulous, but they don't sometimes see the business opportunity or understand where the pain points are. They over-engineer it or they think it's more complicated than it is.

Marcus Johnson (26:06):

Yeah. Yeah. My last question was going to be, what should folks not do when it comes to AI adoption? Or what are some of the biggest mistakes made? You've given me a couple there. Did any more come to mind? Gadjo, any jump to mind to you?

Gadjo Sevilla (26:18):

I think the biggest mistake really is not to test and optimize AI for specific uses. So none of these models are really off-the-shelf solutions. They're pretty raw. You need to kind of forge them and see where they fit within your stack. And I think they need consistent training, and that goes both ways. People need consistent training on how to use them since they're constantly changing, they're constantly improving. So having that training, monitoring, evaluating these models, I think would really help address the needs of companies as they're looking at this ever-widening field of solutions, really.

Marcus Johnson (27:03):

Mm-hmm. Yeah. Yeah. The research on that, I remember one survey, and I think it was about half of folks said that their company offered training. But of those folks where it did offer training, it didn't say they offered consistent training. It might have just been a quick 20-minute, "This is what AI stands for." So yeah, I think that's a really important one. Anything else, Kathleen, to finish us off?

Kathleen Perley (27:28):

I would say on that end, one thing that I see time and time again, and I try to push back, because I do a lot of this for companies, is they'll be like, "Okay, we have 2,000 employees coming in. They range from this job role to this job role. We want you to do a four-hour AI intensive literacy in understanding how it can be used in their day-to-day." And I'm like, "How somebody in marketing is going to leverage AI is very different than how your data scientist or your developer is going to leverage AI. You have different guardrails for each, and potentially different models and use cases and documentation and prompting."

(28:03):

And so, I really try to have organizations start to think about it not even at the job level, but breaking that job into tasks. And of those tasks, where can AI be supplemented in? Because I think sometimes people think it's like a magic wand brush, and it's like, "If it can't do a hundred percent of it at a A+ level, then we're not doing it." Whereas you're like, "Okay. Well, can it do parts A, B, D, and F, and you do C and E?"

Marcus Johnson (28:03):

Yeah.

Kathleen Perley (28:36):

And you layer that in from that perspective. And I think changing that mindset in terms of that pattern recognition of breaking things down and then figuring out where the things overlap is, I think, huge. And it's one of the things I've been pushing at Rice is companies aren't just, they're not offering a lot of training. And so, I think it's a huge... That's what they're looking for schools and business schools to do, is to prep them so that they can think about this when they enter the workforce.

Marcus Johnson (29:05):

Mm-hmm. Fantastic note to end on. Thank you so much to both my guests for hanging out with me today. Thank you first, Kathleen.

Kathleen Perley (29:13):

Thank you. I'm very excited. This was so much fun.

Marcus Johnson (29:15):

Yes, indeed. Thank you to Gadjo.

Gadjo Sevilla (29:18):

Yes, this was great. Thank you so much.

Marcus Johnson (29:19):

Absolutely, absolutely. Thank you to the whole editing crew. And to everyone for listening in to Behind the Numbers: An EMARKETER Video Podcast made possible by Quad, please do subscribe and follow. Leave a rating and review if the mood takes you. We'll be back on Monday discussing how weight loss drugs became popular and the spaces they are reshaping. Happiest of weekends.