Latest / Auto Collabs / Listening To 4000 AI Phone Calls with Monik Pamecha

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Listening To 4000 AI Phone Calls with Monik Pamecha · Auto Collabs

• 0:00
  Any of you guys know a good riddle? No. [upbeat music] This is Auto Collabs. Remember the riddles you would try to solve in, like, middle school?
• 0:14
  My dad, my dad literally, and this is, uh, one time, he came to our house, and we were just sitting on the couches, hanging out, talking. And he just started telling riddles to my kids to, like, discover.
• 0:27
  And it was an endless trevert- treasure trove of riddles over and over and over, and I was like, "Where do you store all of these?" Right. Right. Where's all this coming from? It was wild. I grew...
• 0:37
  I feel like I grew up in a riddle. You know what I mean? It's like- [laughs] Like, like, my dad comes to me and says- I get it now. I get the whole thing.
• 0:45
  [laughs] Yeah, it's like dad walks in and says, "You have four sisters," and that's the... That's it. It's like, "Figure that out." [laughs] Well, then, well, then, then you're like, "But I only know three of them.
• 0:57
  Wait a minute." [laughs] All right, here, I have a riddle for you. Okay. I'm built in lines but sold in rows. I carry people where they go. My heart is fire, my breath is air. I sleep in lots but roam everywhere.
• 1:11
  With four feet turning, I dash with might. What am I, shining in the light? This is Michael Cirillo at 3:00 AM with ferocious heartburn. [laughs] It's a car. It's a car. Yeah. I don't know. That was pretty lame, GPT.
• 1:27
  How's that, how's that- Did you... I was gonna say, you asked GPT for that. Well, well- Yeah, it's built in assembly lines and sold in rows. I sold in rows. Yeah, yeah, and yeah. Yeah. It was real easy.
• 1:36
  It's, it wasn't that... It definitely wasn't hard. It's, yeah. It definitely wasn't. And we talked about cars. I know. I know. Well, today we're get, get to meet, uh, a new person, Manick from Toma.
• 1:45
  Um, talk about solving riddles. What do you do with so much information, especially with phone calls and all the data being produced? How do you make something useful out of it?
• 1:55
  I mean, more useful than the riddle I just read. We hope you enjoy this conversation we have. [laughs] Was you gonna say something? Who two said something? No. I thought I heard that. My chair.
• 2:05
  I, oh, was that what it was? [laughs] Well, we'll blame it on Kyle's chair. Either way, [laughs] we hope you enjoy this interview with Manick. [upbeat music] Manick, thank you so much for joining us today.
• 2:17
  It's so good to be here with you. Hey, Paul. It's my pleasure. All right. So, um, one of the favorite things that we love about this particular show is that we don't just talk tech and talk strategy.
• 2:28
  We like to talk about the people side of the business, and one question that I personally love asking is, how did you actually get into the auto industry? Uh, well, serendipity. Um- [laughs]...
• 2:42
  I feel like anybody else I've spoken to who's been in the auto business always told me that, "Hey, the car business chooses you." [laughs] And, like, you don't, you don't get to choose. And I think it's been true.
• 2:53
  It's true. It's true. Um, I just somehow landed in there. But, um, it's been a fun journey. Um, y- you know, my background is, like, actually engineering, building things, selling things.
• 3:04
  Like, that had nothing to do with cars for the most part, uh, just software. And then we, we just...
• 3:12
  We were working on a project with my co-founder for a while, and we did a lot of different things, a lot of random things. Like, we built stuff for, uh, diet recommendation tools for patients with chronic conditions.
• 3:25
  Uh, my co-founder actually sold nipple covers for a while on Amazon. Like- Okay. [laughs] All right. Necessary.
• 3:32
  [laughs] Uh, a lot of learnings, but eventually we started building something that we had a f- we had one dealer that really liked what we did. And, uh, so we were building a h- a voice AI platform for them.
• 3:47
  And, uh, they then went and, uh, you know, spoke to their 20 Group, and they told them that, "Hey, this thing has been kind of working." It was horrible at the first, when we made the first version.
• 3:59
  [laughs] It was awful. You're like, "Don't tell anyone, please." [laughs] Well, isn't, isn't that what they say, anything worth doing is worth doing terribly at first? Yeah. Right. And it was so funny. Like,
• 4:11
  if I start talking about the bugs and the issues we had, you'd laugh.
• 4:14
  Like, we made an error where somewhere we left a comment saying, "Act like a cowboy," and we forgot to remove it, and the next thing is the call goes in, and it's like, "Howdy, partner."
• 4:25
  Like, "Would you like to book an oil change?" And the person actually goes and books the oil change. [laughs] No. [laughs] That's, that's really good. But it, it... Like, I have the actual call recording.
• 4:34
  It's really funny. And the general manager then emailed me and saying, "Hey, the AI is acting like a cowboy." And I'm like, "Yeah, [laughs] 'cause that's what we asked it to do."
• 4:43
  It's a, it's a cowboy who can close is the only thing you need to worry about, right? Apparently. But it was all funny like that, right? Like, sometimes it'd take, like...
• 4:51
  It was like talking to a walkie-talkie, you know? You'd get a response in, like, 10 seconds. And, and people were patient. Whatever, right? Like, we just started with that.
• 5:00
  We had one dealer that kind of, that, that, you know, was okay with all the problems that we had, still liked it, and told their 20 Group.
• 5:08
  And then more folks came into us, and they're like, "Looks like, you know, what you have is, you know, something that solves our problems, so maybe you should, you know, help us, too." And then we said, "Why not?
• 5:18
  Let's try it." At that point, we had no idea about, like, why dealerships want this. Like, we, we... 'Cause we were working with hospitals, with, like, um, banks, and everybody was moving so slowly.
• 5:31
  But dealers were like, "Hey, work." Let's go. Let's go. Yeah. Let's see what happens. Wait a second. Okay. We'll test this sucker tomorrow, right? Let's do it.
• 5:37
  Say that again because I think, well, I think, you know, de- dealers are a lot of times seen from other industries, and, like, retail auto is seen as, like, the slow movers. As, like, old industry, yeah.
• 5:48
  Like, oh, they're streaming behind. But you're like, "No, no, no, no, no." Well, and it, it sounded as though you were working with a lot of regulated industries- Yeah... which are al- already somewhat slow.
• 5:57
  But of those, at least, it seemed like the dealers were kinda, like, ready to throw in a little bit quicker. Yeah. Yeah.
• 6:03
  Like, 'cause a bank gave me a questionnaire, and they said, "How can you guarantee that this AI is not going to, like, uh, you know, be informal?"And I was like- Uh... That's a tough one- I can't. [laughs]...
• 6:18
  'cause how can you guarantee if your agents are not being informal and at the call center, right? Like, that's a really tough question. That's a great, that's a great co- Like, how do you guarantee it?
• 6:27
  It's like, "Well, we monitor them." It's like, "Yep, so do we." [laughs] And, and I mean, like, how, how much formality do you need in, like, home loans?
• 6:36
  I mean, I'm sure there's, like, you know, obviously there are regulations. Different countries there are different rules. Yeah, howdy, howdy partner might not do it, right? Yeah. Right.
• 6:43
  Um- Give me my social security number. [laughs] But, um, obviously in its infancy, like, you know, the technology's new. There are a lot of risks associated with it.
• 6:52
  It has matured a lot over the last year, so these questions don't even come up anymore. But, you know, a year and a half ago, that was, like, that was how it was, and dealers were willing to take risks.
• 7:02
  And personally, I love that so much. 'Cause if you're building something new and you really wanted to break new ground and, like, you know, you wanna explore a new territory, you'll fail, of course, right?
• 7:12
  You'll make mistakes. But you need to have that space for failure. We don't have any of that with other industries 'cause they're, you know, a little slow-moving, right? For a lot of reasons. That's why we loved it.
• 7:23
  And when we step into the dealership, like, we walk around and we talk. We spoke to, like, every person, right?
• 7:29
  From the person who's the, uh, lot porter to, like, the general manager, owners, every person in the chain, and we're like...
• 7:38
  Like, everybody had, like, all of these ideas on, like, you know, how this should be different. And sometimes it's conflicting with, you know, what other people are saying in the store, too, right?
• 7:46
  Which is pretty natural. That's when we started seeing, like, you know, a common thread. Like, okay, there is a lot of opportunity. And it's very underserved. Despite there being so many, like, tools,
• 7:59
  I don't think they're really hitting the mark and, like, solving all the pain points. Some of them do a really good job but, like, overall, there's still a lot of scope.
• 8:07
  So all in all, we're like, we're building something. There's some traction. There's so much more, you know, that we can do. Put it all together, we're like, "Okay, we're going all in."
• 8:17
  Like, forget the banks, forget the hospitals. Like, I don't know how many decades it's gonna take to make it something really exceptional over there, but here we're just gonna dive in and, and do our job. I love that.
• 8:29
  And you're never, and you're never going back. Nope. Never going... It's not possible. Just so you know, you can't get out now. Even if you wanna try. You're locked in. [laughs] That's the thing, Paul, right?
• 8:36
  Like, it doesn't... It chooses you, and then you're in. You... So we were talking, uh, uh, that you're a soccer fan- Yeah... before this. Here we go. And so I coach so- I know. I know, Paul. Sorry.
• 8:47
  Just hang with us for a minute, Paul. Hang with us. [laughs] So unfortunately, uh, Manik is, he's also a Real Madrid fan, which is a problem, but we'll get over it for a second. Okay. What, what's the problem? Tell me.
• 8:58
  [laughs] I'm a City fan. Uh... [laughs] All right. I have no problem with either of you. That's your problem. [laughs] Uh, all right. So, so I'm a soccer coach.
• 9:09
  I coach kids, and I've been watching a lot of, like, soccer coaching videos. I'm going somewhere with this.
• 9:13
  But I was watching this soccer coaching video, and this coach was coaching probably, like, 12-year-old boys and girls, and they kept saying sorry, right?
• 9:24
  Like, and it, it's actually, if you know soccer, if you've been around soccer, there, there's this very, like... It's one of those words, right? Uh, like unlucky, that people will say. People will be like, "Oh, unlucky."
• 9:34
  You know? "Oh, so sorry. Missed..." Like, if you missed the pass or something like that. And he said, he said, "Never.
• 9:40
  Never again are you allowed to say sorry, because all that, all that y- happened when you missed the pass when you were, when you were unsuccessful is an unsuccessful attempt at greatness." Yeah. Right?
• 9:55
  And so, like, keep attempting that. Because, like, if you, if you were attempting that pass into the open field and the person could've run onto it but you just barely missed, it...
• 10:06
  You, you don't have to say sorry for messing up because you didn't do anything wrong. Sorry is wrong, right? Unsuccessful attempts at greatness are just unsuccessful.
• 10:15
  And I think what you were saying there, and I think this is so key, especially when we think about, like, dealers trying new things or tech partners trying new things.
• 10:25
  A lot of times we just want solutions that are silver bullets that always work 100% of the time, and they never fail, and we always know that they provide value.
• 10:33
  But sometimes there's unsuccessful attempts at greatness that if you stop those, if you don't give yourself the opportunity to, to make those, then you never make the cool thing next. Mm.
• 10:46
  Is there somewhere that, like, point back to something where you're like, "We've really tried this, and it was an unsuccessful attempt at greatness, but we know that, like, on the other side there's probably something really cool"?
• 10:57
  Uh, so just to, um, I guess to, to back up that point that you said, right? Uh, about, like, trying things and things not working out. And same thing with, like, soccer, right?
• 11:08
  Like, I also that people saying sorry, I think that's, like, a more Europe... You know, it's, like, Europe saying sorry that's not- It is, for sure... how we do things. [laughs] Or, or Canadian. Yeah. Little of both.
• 11:21
  Um, but, um, I think, like, when was the last time a, you know, something in your life went according to plan? Like, everything just went according to plan. When was that one time that happened, you know?
• 11:34
  I, I don't remember any single time in my life, like, whether it's in business or outside of it. Like, so I mean, that doesn't mean you don't make plans, right? Of course you'll have plans and you have some goals.
• 11:44
  So we've always, like, with our product or, like, you know, many things in the past as well as I've built, like, you know, small pro- We... I used to build a lot of, like, social networking websites, like, in 2007.
• 11:55
  That, that was when I was in high school, and we were trying to build all these, like, products for users. And we would build these amazing features.
• 12:02
  We'd add, like, oh, if you're in Central Park, you can make these groups based on sports, and people will come in and you'll, like, all know when you're gonna play. And all that sounds so great, right?
• 12:11
  It, it sound- Wait, what was that app called? It was called Active Life NYC. So this was- Love it... I was in high school. Um, I think I was, like-I don't know, maybe eight...
• 12:22
  16 years old or 15 years old, and I found a customer, uh, he was a instructor in Central Park, like, um, I think a fitness instructor, and he was like, "Let's make a social network.
• 12:32
  Let's get everybody to, like, come here and then, you know, have these common interests, and then they'll put videos on there, photos on here. It'll be like a mini Facebook of, like, people who just play sports," right?
• 12:43
  And on paper it's like, it's great, you know. Of course you wanna have all these communities, you wanna know who you play with, you wanna come and do all these things.
• 12:51
  We put all the features in there, every single thing. Like, I spent like a year building it, and I think only four people used it. Wow. Um- Wow. And it was, like, rough, 'cause- [laughs]...
• 13:05
  I spent so much time building it, and I thought- Yeah... every single feature. I was like, "This would be amazing.
• 13:11
  This would be blah, blah, blah," and you, you're trying to do all that but, you know, people just didn't want it.
• 13:15
  They just rejected it, and they're like, "Screw it, I'll just post the picture on Facebook," for example, right? Um, so I think throughout, like even with our current product, we've tried, like...
• 13:26
  I- if we have five features that work for that, we have like 20 features that didn't work. Mm. Mm. So every single thing, like I would say we've, like, done that again and again.
• 13:36
  We try to think ahead and get there, but I think failure is just a natural part. And, uh, think we're even tired to say sorry. We're like, you know, "Whatever." It always happens, right? It's accepted.
• 13:46
  So screw it, let's do the next thing.
• 13:48
  So there's so much, there's so much, um, I think there's such a variety of opinions and thoughts on what AI actually is and how it can actually be used inside the dealership to, to cut down on repetitive tasks or to increas- increase, uh, lead quality, close rates, all of these things.
• 14:05
  Um, you have a unique position in that you get to look inside all of these things, formulate a product around them.
• 14:12
  What do you see as, we'll call it the lowest hanging fruit, of auto dealers who are thinking like, "How do I implement AI into my operations in some way?"
• 14:23
  What do you think is the, the lowest hanging fruit that you would encourage dealers to say, "Hey, dip your toe in the water here"?
• 14:30
  I think if there is any specific thing that you do that does not require creativity, it should be automated.
• 14:38
  So for example, right, like booking appointments for, like, specific things, like in comes like whatever, you know, my whatever I need to request for, and then out goes a specific static appointment, automated.
• 14:51
  But if there's like something that's more, requires more creativity, right, where you're constructing like some kind of an offer, you're trying to like appeal to someone, that's better handled by humans.
• 15:00
  So I think it's like first step is distinction between what should be automated and what should be not. And then whatever has to be automated, you automate that part.
• 15:10
  So I'll tell you because I've been listening, I've listened to over 4,000 calls, like maybe on 3X, 4X speed, over the last year, year and a half, and a majority of the calls are actually repetitive.
• 15:24
  Like, they have these elements of, you know, like even within appointment bookings when you're dealing with specific loan or cars, like there's always like this rule in mind, you know, like, "Oh, if this, then that.
• 15:34
  If this, then don't do that," right? So a lot of it what we...
• 15:38
  Even things that we don't think are, you know, repetitive, they actually are, 'cause when you look at like, you know, 4,000 or 5000 calls- You realize how much it actually happens. Mm. That's the same thing.
• 15:46
  You're just doing like if and else, right? So a lot of that can be automated.
• 15:50
  I think best use of AI is actually doing whatever can be done multiple times, like, you know, giving it to the AI, and only taking the stuff, like, that's really, really requires you to involve yourself and have that creativity.
• 16:05
  And also like, you know, the customer expects that, you know... 'Cause, I mean, what personal touch do you want when you're booking an oil change, right? I mean, of course there is to some degree you want that.
• 16:14
  But like, wouldn't you better serve that time like, you know, giving, you know, pre-qualifying somebody for a used car, you know, things like that, right? Mm-hmm. So it's understanding what to automate, what not.
• 16:25
  And once you've decided on what to automate, going all out on that end to end, and then taking the rest and then giving it to someone who's like, you know, trained for that purpose, right? A human. Yeah.
• 16:36
  Do you get, do you tinker with automation outside of like the work thing, or do you get tired of it in, in building a product? [laughs] He's, he's f- his whole real life is full manual, right?
• 16:47
  Like even has manual windows. Yeah. Exactly. [laughs] I mean, uh, let's see. Uh, I think the Waymo. So has some degree of automation outside, you know, where- Oh, okay. It's, it's actually really funny.
• 17:02
  Uh, but yeah, W- Waymos are cool. Like at least in San Francisco you can take them now and then. Initially it's like a little scary, but then once you get in it and you're like, "Okay."
• 17:10
  Like, if it says three minutes, it'll be there in three minutes.
• 17:13
  And you know, one thing is that if I'm late, I'm always very nervous 'cause, you know, if I'm like a minute or two minutes late, the Uber driver is, like, just getting annoyed.
• 17:22
  But with Waymo I'm like, "Whatever," you know? Oh. [laughs] That's like- I didn't think about that. Um, and also if you're driving, you can always cut Waymo off. Like, I mean, you know, it's, it's- [laughs]...
• 17:35
  it's regular. So I have that aspect too if you're on the other side. Um- I never thought about that, but yeah, like- Oh, that's hilarious... people just like, "Oh yeah, it's a robot." Yeah.
• 17:43
  Waymo's just like, "Oh, it's fine." Yeah. It, it- "I'll readjust"... yeah. Waymo on the other side, "Hell yeah, turn left." You know, like, whatever. [laughs] Oh, that's hilarious. That's amazing.
• 17:53
  But yeah, not, not too much. I mean, um, just with work, like you have all these email automations and like, you know, summaries of meetings that you can get after that.
• 18:01
  So you could do a lot of, like, small things that I've been doing. They're pretty good. It'll just make a to-do list after every meeting, so you never have to like think of like, "Oh, I need to do this or that."
• 18:11
  But, uh, yeah, like randomizing what to eat. I mean, uh, some people love, you know, choosing what to eat. For me, it's like I'm just... I keep getting confused, you know?
• 18:21
  So I have this extension, I'll justRandomize this thing on DoorDash, and we'll just pick, like, all right, this- No way... item, and then just get it, you know? Uh. [laughs] That's amazing.
• 18:32
  You're like, "I'm sick of making decisions. I'm sick of it." Yeah. And I'll eat it. You know, I'm like, whatever, right? It is what it is. Um. [laughs] That's... I... But, but that's a great, like...
• 18:42
  That's, that's actually similar, but it's the same type of thing as, like, Steve Jobs only wears black shirts, right?
• 18:50
  But to reduce the decision-making ne- necessary so that he can make the decisions on the hard things, right? And it's, it's the same thing. It's like deciding what food today, put that on the robot.
• 19:01
  Like, th- it'll just pick something different for me. I'll do the hard thing, which is listening to 4,000 calls to see if- Oh my goodness...
• 19:09
  you know, to see if we've m- made the if/then statements correct across that many calls. That's the hard thing. That's what needs the human thing.
• 19:18
  What are you most excited about in your product as you're, like, building toward the next things?
• 19:22
  I think a lot of people are starting to get familiar with, "Oh, we can have some level of AI or machine learning kind of, like, take and capture," especially service calls because they're high volume, they're repetitive.
• 19:35
  But what are you excited about as the evolution of, of, like, voice AI, um, when it comes to consumer interaction? So I think it's really voice AI is, like, just a channel.
• 19:46
  It could be text, it could be email, it could be any channel, right? It, it's really the work that gets done behind the receiver.
• 19:52
  Like, that's complicated, and what has happened over time is that, you know, initially, maybe 20 years ago, you had an operator, then you had a phone tree, right?
• 20:02
  Which is like press one, press two, press three for this, right? This is the next generation, like next evolution of that, which is like, "Hey, what do you need help with?" Right?
• 20:11
  And then you can, like, figure out and do things, you know, automatically. A lot of times you'll transfer in cases where you cannot help them.
• 20:19
  What is happening is that you're tr- the, the things that you have to transfer for will keep shrinking over time. So, you know- Right...
• 20:26
  let's say in the past you'd be able to handle only 2% of the calls, then it became 10%, then it became 15%, then it became 40%, right?
• 20:35
  So I think we'll see that number go, you know, like, like how much you can handle will keep going up because there's a tail end of all these complex things like, "Hey, is my, um, you know, like, is my license plate ready?"
• 20:47
  You know, like number plate ready, like can I pick it up?
• 20:50
  Or is m- you know, some question around warranty that only a human would've been able to answer, or you have some question about a minder code that came up and you don't know what to do with it, right?
• 21:00
  Maybe two years ago you couldn't respond to that, but now you can.
• 21:03
  And then you can actually tell them that this is the diagnosis, you know, maybe you should come in, and this is how long it would take and how much I- it would cost.
• 21:11
  Maybe you can also add this additional service, and hey, I also see that your car is like four years old and maybe you wanna check out your trade-in valuation 'cause I just pulled it out from Black Book- Right...
• 21:20
  and here is your number, right? Yeah. And I think it also depends on the amount of data sources available, right? Yeah.
• 21:25
  If a data source becomes available that can tell you whether or not the license plate is ready, then the AI can ingest that, right?
• 21:32
  And, and more and more data sources are coming online every single day, and that's, that's where I think we get that growing percentage because it's all just data, and whether it's stored in someone's head, a spreadsheet, or in an accessible thing online, like the more data that can be consumed, it can then be shared, right?
• 21:52
  'Cause the natural language is, is, is already there. Yeah. And, and you know, like, even like in the past, like you have all these like data, uh, silos, right?
• 22:01
  They're locked up in like this interface and that browser and this login. But you know, I don't know if you've seen any of those demos where the AI is like opening a browser and like, you know, logging- As an agent.
• 22:12
  Yeah... human things, like as an agent. Um, so they will make integrations even easier.
• 22:18
  So imagine it gets easier to access data, it gets easier to ingest it, and it gets very easy to like even express it to the customer, like through voice.
• 22:28
  Like all these three things happen at the same time, just imagine the possibilities, right? It's crazy.
• 22:34
  Um, I could pull anything from anywhere and also like these LLMs and these, you know, large language models, as you call them,
• 22:41
  they know more than any- anybody can know about physics, about chemistry, about your OEM manuals- All of it... about how the engine works. All of it.
• 22:49
  So what are the odds, like we can beat, you know, the model at talking about a certain problem? 'Cause it can see from all the perspectives possible to mankind, right? Yeah. So
• 23:01
  put all of it together, it's gonna be a blast. But still, I think the challenge is the tools always exist, but I always like to tell my team that we're plumbers, you know?
• 23:11
  We're, we're taking all these different things and plumbing it together to make the best customer experience. Mm-hmm. What that experience is gonna be like, no one knows, right?
• 23:20
  That is the part you have to experiment and find out. So- That's the fun part. Yeah. Well, awesome.
• 23:25
  Manik, I, I'm-- It was fun chatting with you all the way through, you know, pushing your chips in and, and, and soccer and, and just imagining what can be, uh, with the product that you're building, that anybody's building with AI right now.
• 23:39
  We can't wait to see you at ASOTU Con, uh, and hang out and learn a little bit more. Kudos to all you're doing, and thanks for joining us today on Auto Collab. Well, yeah. Thanks for having me.
• 23:48
  It's gonna be a blast, uh, at, at ASOTU Con and, uh, you know, we have our, we have a racing rig set up over there. Yes. That's gonna be the thing. It's gonna be fun. Like, come race with us.
• 23:59
  [upbeat music] Any-- I don't care if it's 3x, 5x, 1x. If you're gonna listen to- I can't listen to 3x... 4,000 calls, you're dedicated to building a product that doesn't miss, right? I cannot listen to things at 3x.
• 24:14
  The fact that he listened to 4,000- There's no way... what happens in your brain at that point? Like, I feel like you start to absorb another layer of conversational pattern that you probably wouldn't otherwise. Mm.
• 24:25
  Right? Like I bet you can start to hear things- You know what you can probably hear actually a little bit quicker is voice inflectionBecause listening fast actually, like, you can hear up and down as a pattern across.
• 24:37
  And so I- that's actually an interesting pattern to think about, because there's so much when it comes to, like, conversational AI that has to understand not just what the words that were said or requested, but the intent or, um, you know, the, the, the, like,
• 24:54
  what's going on in that person's head when they say those words. Mm-hmm. Um, and, and their level of frustration or energy or joy. You know, golly, it, it, it's a complex thing.
• 25:05
  But I know that, that people like To- Tomar are obviously trying to figure out, like, how do we f- how do we fix this? I listen to three calls and I'm instantly enraged. So I don't know- [laughs] Oh, boy.
• 25:18
  I've been there. I don't know, 4,000 calls. I mean, like, you're telling me. That is dedication, and also an insane amount of discipline. 'Cause if I listen to 4,000 calls, I would be rage eating.
• 25:32
  I'd be- Fam- familyless... I'd be 400 pounds. [laughs] I mean, it, it- Why did I just finish this much ice cream? Oh, one of, one of my favorite things from that conversation was the soccer metaphor.
• 25:42
  When his, his coach said, you know, "It's not, an unsuccessful pass isn't a failure. It's an unsuccessful attempt at greatness." Whoa. That, that reframe was pretty cool. It's strong. Right? It's strong.
• 25:53
  Real- And look, well, you know, I'm not gonna go listen to 4,000 calls, but we don't have to, which is the good news. [laughs] Right. You get to- The great news... you get to listen to Auto Collabs and get the recap.
• 26:05
  Even better. I don't know if there's a recap of the calls, but regardless, uh, thank you so much to our guests today. And as always,
• 26:12
  on behalf of Kyle Mounts here, Michael Cirillo, and myself, thank you for joining us on Auto Collabs at 1X.
• 26:19
  Sign up for our free and fun-to-read daily email for a free shot of relevant news in automotive, retail, media, and pop culture. You can get it now at asotu.com. That's A-S-O-T-U.com.
• 26:32
  If you love this podcast, please leave us a review and share it with a friend. Thanks again for listening. We'll see you next time.
• 26:39
  [upbeat music] Welcome to Auto Collabs. Welcome to Auto Collabs. [laughs] Why are we recording? Are we rolling yet?
• 26:57
  Not yet.