The AI Gold Rush Problem: Big Investment, Unclear ROI with Naomi Liu and Mike Rizzo

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Hello everyone, welcome to another episode of Opscast brought to you by well, you know who, marketingops.com and all the MoPros out there.
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This is gonna be an interesting one.
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Uh we see this is a we've got special guests, like my two co-hosts, Amy Lou, Mike Rizzo.
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We don't get to do this right often.
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I always feel very special.
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It's uh special.
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You're our you're you are special, yeah.
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That's the best way to say that.
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Tell me more.
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Tell me more.
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Tell me how special I am today.
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Yeah, I don't know I want to feel special today.
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That's what I want to feel.
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Yeah.
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No, I was thinking about it.
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For all you listeners, you're special, just so you're watchers, you know.
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For watchers, right, because we do the video now.
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Yeah.
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Right.
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Um, now I was thinking about like I I I think I think by by just happenstance not really planned, we end up doing this about once a quarter.
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It feels like, or once every six months.
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I think it would be good to like let's do this like that, like make sure we do it.
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Yeah, we need to be a little bit more intentional with that.
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The use of the three amigos time on the pod and just reflecting on things.
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I agree.
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I agree.
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We we need to have some of the uh you know, three amigos, the what was it was the was the movie three amigos, right?
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I think so.
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I think there's also a tequila.
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We could do that too, right?
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We could do that too.
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So so we're recording this the first day of quarter two, so that would be April Fool's Day, but we're not gonna we have to share no jokes to share.
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I only know a handful of really bad dad jokes anyway, so likewise.
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Yeah.
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Um, but a couple days ago we saw the announcement about open AI getting massive funding.
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Like, I don't know, yeah, like crazy.
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And then like you had recently posted some stuff about asking people if they wanted to hear about you know what others are getting in terms of ROI from AI initiatives, and you thought we could start there, like which I guess those two things kind of coming together, it's like it looked like there's a massive interest in seeing like what what are other people getting out of AI, which makes me think, are others not getting that?
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Are they just not sure how to measure it?
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Yeah, yeah, for sure.
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Yeah, let's start.
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Let's start with the news for those of you who may have missed it.
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Uh OpenAI announced an incredible amount of funding, I don't think.
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I honestly don't know if it's ever been as high.
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Uh it's crazy, as high as as high as it has been for these folks.
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So they they announced 122 billion raised in their round on an 852 billion with a B folks, yeah uh valuation.
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And um and so the I I only grabbed the first part of the message to send it to a few of my my friends, and I was just like, this is insanity.
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And and the the opening of the post from uh I think their CFO or whoever posted on LinkedIn, yeah, said hey OpenAI was the fastest technology platform to reach 10 million users, the fastest to a hundred million users, and soon the fastest to one billion weekly, weekly folks active users, a billion weekly active users.
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That's insanity.
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And then they said within a year of launching Chat GPT, we reached one billion dollars in revenue.
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And by the end of 2024, they were generating a billion per quarter, and they are currently, as of the post that went live yesterday, which would have been the 31st of March of 26, they are now generating 2 billion in revenue per month.
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That's insane, crazy, and valuation is uh approaching a trillion dollars.
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Like, I mean, I I was like my initial gut response to that was to try to figure out like as a percentage of GDP of the just US, like what where are they at relative?
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Like, this is nuts.
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Like, that's the scale of revenue that these folks are having, and then you know what, like I said this to somebody else yesterday or earlier today, I don't know, too.
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And they're like, I think that the person's response was like, we're all screwed.
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I was like, wait, what does that mean?
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What do you mean by that?
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Why why are we all screwed?
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Um, but their burn rate is insane.
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Like, I don't know exactly what it's all on, but I I know that they're um I guess like one of my colleagues was saying that um between 26, the year of 2026 to the year of 2029, their expected burn is to be over a hundred billion dollars.
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So like that's an insane, like in three years they're gonna spend a hundred billion dollars um on whatever infrastructure, people, marketing.
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Like I'm sure the tech itself costs pretty decent amounts of money.
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The compute power of AI is like getting better, but it's still a lot.
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Well, I mean, so it's interesting.
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I I I have a friend who uh uh works in HR at a company that builds out data centers.
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And he said I can't remember the number of people he said, but they have to hire they're gonna have to hire an extraordinary number of people over the next uh few years to help.
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And it's like what's interesting is it's it's uh it's jobs like electricians, plumbers, uh, construction workers, right?
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It's not high tech we would think, right?
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Because that's what they have to do.
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They have to build the the infrastructure and get it set up and running and all that.
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Um which is like now it makes sense, but like I it makes me think like there's this capital that's had nowhere else to go.
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Well, that was the yeah, that was the other thing that like not not that like I'm you know I don't know, but like I'm not proposing that there's just like this secret money that sits somewhere or whatever, but it's just it's it's a little alarming.
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Yeah, yeah, yeah, exactly.
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I don't I don't mean to insinuate anything like that, but it is just like eye-opening when you look at it from like sort of this macro point of view, like holy cow, there's somehow that many billions of dollars just like able to be pulled together and invested just like somewhere out into the world that is accessible, you know.
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Like 122 billion bucks.
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I wonder how they're gonna utilize that money, like where they're gonna where they're gonna put infrastructure, right?
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Yeah, yeah.
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I I imagine it'll be probably data centers and server farms and all that kind of stuff, and then I'm sure some asinine amount of marketing spend and that kind of stuff too, but they have to pay for a lot of electricity, yeah.
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Well, infrastructure is their bottleneck, right?
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And that's just yeah something that they that takes a lot of money, so yeah.
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But it's crazy though.
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It's just yeah, but it's crazy.
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Like there's 122 billion dollars out there that they were able to find from all these wherever companies, investment funds, whatever.
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I was like, you know, I'll just take like 0.0001% of that, please.
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Yeah, I'm still pretty sure that's still a decent amount of money.
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Yeah.
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I didn't do the math on it.
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Some math whiz is like, no, that's not that no, you mean like 0.001.
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Got it.
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Yeah.
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Thanks.
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But I also feel like having that amount of investment and that amount of money, it doesn't like on one hand, it like validates that yes, AI is here to stay, right?
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It's not just this, you know, novelty product that people are really into right now that's just gonna disappear.
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It's like mass habit, right?
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Um, but I do think that it also intensifies or like makes scrutiny much more apparent, right?
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Because the bigger this company gets, the more scrutiny it's gonna face around things like competition, security, you know, data breaches, yeah, the thing that happened with anthropic, right?
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Like, you know, dependence on server farms, compute stack, compute providers, things like that, right?
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So like I don't know, I just I think it's like a two a double-edged sword, right?
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Well, and I think especially validates it, but also between the three of us, right?
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When we were talking chatting in Slack about this, so like my reaction was like, and this is a company that started as open source, like the intent was to make it available to everybody.
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And here they are.
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So it's like, you know, I don't know what that says.
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I I do think that's gonna force more scrutiny on them too.
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Yeah.
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Yeah.
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Yeah, I think there's definitely some eyeballs that need to be paying attention to them.
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I think if I'm the other products in the market that you know, I I find this like fascinating as we as we sort of like transition so some of this conversation into the poll that you mentioned at the top of the top of the episode.
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Um here here's open AI like raising all this money.
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If you if you were if you go, there's a company called Augment um that does some.
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You guys are like, where are you going right now?
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So there's this company called Augment that does training or whatever.
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They they like help you learn from experts in the field.
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Okay.
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Um and one of the instructors is uh, I believe it's actually the one of the original like founders uh or executives of OpenAI.
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And um, there's a brief backstory that he gives of like how the Chat GPT product came to market.
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And so um if I could if I could truncate it a little bit, and someone could fact check me on this if they go find it, um, but I'm pretty sure I remember this accurately.
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And and from what I recall from the lesson that he taught in the backstory about ChatGPT is that it actually was never intended to be the initial app.
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What they were doing is they were struggling to get these companies to adopt AI.
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So they were saying, hey, we have these capabilities for you.
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We want you to ingest them into your products, we want you to start using our AI to enable your companies to do more, whatever it is, whether you're a software company or whatever.
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And they weren't getting the adoption and they went and turned on this thing that was more like, hey, here's this self-service way to sort of interact with the AI, right?
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With our with our um LLM.
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Yeah.
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And that is what was the it was like it was this like almost accidental catalyst that was never intended to be a like for the general public.
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Like they they had intended to build sort of behind the scenes and like go like work with these companies and be this infrastructure layer, right?
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And now, juxtaposition, like you know, fast forward a handful of years later, like the broader market starts adopting it, it forces the conversation across the board, including at businesses, and they're trying to establish these technology partnerships.
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I'm sure they have many of them.
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I've heard of these things happening, I don't know where.
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And you look at like what our community, just from what I've observed and the conversations that I've had, both with like you two and with others in the community, there's this like really big shift from yeah, ChatGPT was like great, but cloud is like just run away with so much of our audience's like attention.
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And I've personally like shifted all of my workflow to be cloud-based at this point.
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And yet here's OpenAI saying, yeah, we just raised you know, an asinine amount of money, and and most of our market, our practitioners are like signaling, no, no, no, Claude is the place where I'm spending my time right now.
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And so if I'm Claude looking at that capital raise, I'm like, either I'm worried or I'm like, oh yeah, we're about to announce something very similar.
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It feels like they keep leap-rogging each other.
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Yeah.
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Yeah.
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Right.
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And then you get a but I do think it's a right behind.
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Yeah, but I think it also sort of begs the question like, you know, who who are your end consumers, right?
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If OpenAI's sort of like initial foray into the market was meant to be more on the B2B transaction side and not the B2C sort of piece, um, then then perhaps like that's really where a lot of this investment is going to end up going, is like back into that direction.
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And I and I don't know who Cloud's sort of core customers are, but we're seeing uh a big shift from them to be like, hey, like get your enterprise on here and or start orchestrating across your team, not just the end user, but it'll just be like fascinating to see what happens, which is part of the reason why I asked the question that I did on LinkedIn, which was all about getting ROI out of your AI.
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Like, is anybody getting it?
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And would you like to see you know what others are doing?
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Like, I don't know.
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Naomi, like, I think I it was actually prompted by Naomi's question to me.
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She was like, Do you I feel like I remember you and I were talking about this?
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You were like, hey, I feel like there's a bunch of hype that I see, but I like, does anybody actually get ROI out of this stuff?
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I was like, I don't know, let's see what people say.
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Like, and the result was there were two people that wanted to show off what they were getting out of the soft, soft like measurement too, right?
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Like time saved, you know, and you put a how you put dollar value on your time, right?
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And it's just kind of like an extension of your day-to-day now, you know, but like in terms of like actual ROI dollars, I'm curious what people are doing.
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Yeah, right.
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Like great if you know you're saving 30 minutes per email that you're building in Marketo, or like you're shaving down some time, pulling together reports for senior leadership.
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But like you're accelerating lead scoring and routing.
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Exactly.
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But like actual ROI around revenue.
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I'm curious about that.
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I want to see that.
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Yeah.
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And I think it is part of your curiosity, other is it self-driven?
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Is it your own sort of teams or your peers are asking you similar questions?
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Like where is that curiosity?
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I think that's just a I think it's a bit of all of it, right?
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I think a lot of us who work in this industry, we will often get, you know, a lot of the tools that you know you have in your tech stack have now at this point AI components to them, right?
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Um, there are tools that are all AI and you can't, you just can't escape it, right?
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Even and even if you wanted to.
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So part of that is okay, so if I'm adopting something into the organization or I'm adopting it into my personal workflow, how does that help me either by saving time or can I prove out that it's actually giving back some kind of ROI in terms of real actual hard dollars to the business?
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Um, and yeah, like part of it to answer your question, part of that is personal curiosity, because that's how I like to learn is from other people, right?
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That's why things like mops are great and things like other conferences are great because you get to see what other people are doing.
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So you can potentially have takeaways to take back into your own, you know, use case.
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Um but also just, you know, wanting to be on top of what other people are doing, because if we get that question, then you kind of want to be known.
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I think people who work in this at work in our industry, like we tend to generally be regarded as the subject matter experts in all things tech and digital.
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So we kind of want to be able to speak eloquently about these things and these topics when it changes constantly all the time.
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So yeah.
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Like, do you expect to get um and it's different because it feels like it feel felt like what sick it feels like it was only six months ago where clay, like everybody was talking about clay, right?
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Maybe it was nine months ago, but it wasn't that long ago, and it I just don't hear about it anymore.
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So um, but I like I played around with it and I've played around with some other like I never used N8N, that was big one.
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I've played around with relay.
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Um, and what I figured out was like, oh, there's these hidden costs that are part of it too, right?
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All those calls to the LLMs or you know, uh AI platforms have these costs, and it's the pricing was pretty opaque, right?
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I never knew what it was gonna cost for any particular call.
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And and I think that's part of what's making it hard.
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There's like so many like the fragmentation in Martec has been pretty big for a long time, and now you throw all these things in there, it's like people are creating stuff all over the place that pricing's all over, and you it's really hard to quantify like how much are you actually spending on it, and then it makes it really hard to go like okay, well, then how do you go, like, okay, we spent X amount uh last quarter, last month, whatever.
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How do I know?
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Yeah, do I need to get the benefit of that in the short same time horizon, right?
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Even if our sales cycle of a big company is 12 months, 18 months.
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Yeah, like it's like, yeah, how do you like I don't know how you come up with the model for that?
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And I mean, for like working with a big company right now, like they're really behind using AI for I think they would call it time dividend.
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I don't know how you measure that though, right?
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Because the level of use by any one person across the like there are gonna be some people who are like off the charts, right?
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They're using it every day, all the time.
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I'm gonna come up with creative stuff, and other people who are kind of in and out, and then some people are just like well, that's all you know, it's all hype, right?
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It's not gonna really affect what I'm doing.
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And uh so like it's really I think it's a challenge, right?
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So I think it's probably why in your poll, like what was it?
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Well over half of the people were like, Yes, I want to know.
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Yeah, if people are getting ROI because like I Yeah, they're all like, Yeah, I definitely want to see what people are doing.
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And the second option on the poll for those of you that are sort of listening and watching was was basically like, Yes, I want to see, but I'm also willing to demo.
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And there were just two people that said that they were willing to sort of show it off.
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Um and and while the question wasn't directly like, are you willing to show it off?
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Um I think those who are excited and confident about it uh would have like sort of like selected that.
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And there's there's two individuals, two of whom both of whom are in our community, and I've heard and watched them talk about you know the things that they're doing.
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Um, so it's it wasn't surprising to me that they were like willing to show it off.
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But I I think it's very telling of the situation that we're in right now, which is just echoing everything that that you just suggested, Hartman and and you as well, Naomi, is just that like how do you measure this stuff?
00:21:04.970 --> 00:21:06.009
It's really hard.
00:21:06.009 --> 00:21:20.410
The the hidden costs of all these things, the little mini products that you're building, like all of them have these like intricacies that that are just fundamentally different in an already like uh fragmented landscape of technology.
00:21:20.410 --> 00:21:39.289
Um and I think for me, the the latest observation uh that I've sort of like been trying trying to pay attention to, at least, and is really just it boils down to like this efficiency gain thing, right?
00:21:39.289 --> 00:21:56.649
I think for years we've been tasked as marketing operations professionals in general with the idea of like, how do you measure the investment of a Marketo or a HubSpot or whatever?
00:21:56.649 --> 00:22:00.490
How is that impacting our ability to generate revenue?
00:22:00.490 --> 00:22:04.410
Um, and there's been threads about this in Slack before.
00:22:04.410 --> 00:22:15.529
There's been conversations, probably on this very podcast, if I recall, about like literally doing the math around how much time does it take to build an email, send an email, and what is the cost per email?
00:22:15.529 --> 00:22:18.250
And then does that email generate a lead or whatever?
00:22:18.250 --> 00:22:18.649
Right.
00:22:18.649 --> 00:22:29.369
Like there's formulas that people have like built out for themselves to try to say, hey, like By using this tool, we've been able to drive some level of dollars.
00:22:29.369 --> 00:22:43.049
Um, and like that's kind of our natural instinct as like like people who manage this tech, but also the the folks that we report into, they're sort of always trying to measure it that way.
00:22:43.289 --> 00:22:43.690
Yeah.
00:22:43.930 --> 00:22:58.089
And I think while it's a worthwhile venture to pursue that level of measurement of any piece of technology you invest in, I do think that it goes without saying that there are many that you just can't do that with, right?
00:22:58.089 --> 00:23:02.569
Like, um what is the ROI of my phone bill?
00:23:02.569 --> 00:23:12.569
If every time I pick up the phone and I call someone that is a potential prospect, like is it that by paying for Verizon versus ATT that like I made the right choice?
00:23:12.569 --> 00:23:20.329
Like there's just things that like there are the costs of doing business, and some aspects of your investments just sort of fall into that category.
00:23:20.329 --> 00:23:32.569
Um, and I think AI is for me lately has just been like, yeah, this is just a really good enablement solution to like let people do things way more effectively than they used to.
00:23:32.569 --> 00:23:48.809
And in some cases, you're gonna go build out a very hyper-specific product use case that unlocks your ability to do something new or in a novel way, the same thing in a novel way that is that is just better than it was before.
00:23:48.809 --> 00:23:52.649
And and if that costs you an extra hundred bucks a month, like great.
00:23:52.649 --> 00:23:55.450
But it's like the same motion, just better.
00:23:55.450 --> 00:24:16.649
Um, and but a lot of it is like what what Justin Norris posted on LinkedIn the other day was like, hey, I am forcing my team to prove that they interacted with our agent that we built for them, yeah, to stop asking us like, or to stop putting tickets in our queue that they could find the answers to.
00:24:16.649 --> 00:24:18.730
And they have to literally drop the link.
00:24:18.730 --> 00:24:19.930
This is a like this is a great.
00:24:19.930 --> 00:24:24.809
If if all you did was stop listening after this part of the episode, this is probably the best takeaway.
00:24:24.809 --> 00:24:32.329
Justin does this post, he says, I built a system, it's an AI agent, it knows all these things about our business.
00:24:32.329 --> 00:24:40.170
If someone wants to open a ticket with us, they have to prove to me that they asked the AI first by sharing the link of the conversation.
00:24:40.170 --> 00:24:44.490
Because if they didn't, then I'd I'd just reject the ticket.
00:24:44.490 --> 00:24:56.009
And the result of that was a 33% reduction in effectively what he calls like paper cuts, like wasted time tickets that could be solved with a system where knowledge already exists.
00:24:56.009 --> 00:24:58.490
You just need to spend time to go look it up.
00:24:58.490 --> 00:25:00.170
And I was like, wow, that's amazing.
00:25:00.170 --> 00:25:03.609
My first thought was Justin's working himself out of a job.
00:25:04.009 --> 00:25:07.210
I was like, uh oh, dude, don't get rid of all the tickets.
00:25:07.450 --> 00:25:08.730
I don't think that's true though.
00:25:08.970 --> 00:25:09.690
No, it's not.
00:25:09.690 --> 00:25:12.809
My second thought quickly following that was like, that's not true.
00:25:12.809 --> 00:25:22.089
In fact, it's actually unlocking him and his peep his team's time uh to go spend more energy on things that really move the needle for the business, right?
00:25:22.089 --> 00:25:26.730
Which is uh which is why I really appreciated his comment about the tickets being paper cuts.
00:25:26.730 --> 00:25:29.930
Like they're painful little moments that you just shouldn't have to deal with.
00:25:30.170 --> 00:25:36.250
What so what really caught my eye on that is like there's this this may be part of what I think people struggle with AI.
00:25:36.250 --> 00:25:38.170
I'd love to do your take, mammy, because I don't know.
00:25:38.170 --> 00:25:43.129
I mean, I guess you've implemented some AI tools where you are, especially with your broader scope now.
00:25:43.129 --> 00:25:51.849
Um, is that like I think people have thought, well, there's not really we don't really need to do much for change management, right?
00:25:51.849 --> 00:26:10.009
And I remember doing some research on change management like 12 months ago, and the best paper I found about it was written like a Harvard Business Review article that was written originally like I think the 70s, maybe early 80s, because it's still like it still applies because it was all about human nature, right?
00:26:10.009 --> 00:26:14.410
Resisting change and you need to communicate and then you need to reinforce it, right?
00:26:14.410 --> 00:26:19.529
So to me, what Justin did is like people always I get people all the time.
00:26:19.529 --> 00:26:22.410
I'm like, how do we get people to do this new behavior?
00:26:22.410 --> 00:26:28.490
I'm like if you they try to do the old behavior, you tell them go do the new behavior, right?
00:26:28.490 --> 00:26:32.649
It's not really that hard, except for then you feel bad.
00:26:32.649 --> 00:26:38.730
If you're the kind of person who likes to solve problems and help people and like the problems really with you, not with the people, right?
00:26:38.730 --> 00:26:41.129
They're gonna do what is most effective for them.
00:26:41.129 --> 00:26:45.849
And if you enable it, then who's that who's that with?
00:26:45.849 --> 00:26:50.649
But yeah, have you have you gone through that, Naomi, with like AI tools internally?
00:26:50.649 --> 00:26:52.089
Have you had struggles with that?
00:26:52.410 --> 00:27:03.690
Yeah, I think um, and I actually was having a conversation with this uh about this with a friend the other day when we're catching up about similar things around AI in our companies, and you know, and just in general, right?
00:27:03.690 --> 00:27:11.129
People that we talk to, um, I think that you can group it into like three core gaps, right?
00:27:11.129 --> 00:27:13.930
Belief gap, skill gap, expectation gap, right?
00:27:13.930 --> 00:27:16.250
So belief gap.
00:27:16.250 --> 00:27:30.889
Uh, you know, people think AI is impressive, it's great, it's this like amazing new thing, but they don't actually believe that it will materially help them in their actual job, their workflow, or the company that they work for, right?
00:27:30.889 --> 00:27:34.250
Second, skill gap, right?
00:27:34.250 --> 00:27:38.649
Uh, you know, I know what I don't know.
00:27:38.649 --> 00:27:40.329
And there's a lot about AI that I don't know.
00:27:40.329 --> 00:27:44.490
There's a lot that I do know, there's a lot that I don't know, and I there's the gap in between is huge, right?
00:27:44.490 --> 00:27:55.849
And I think even when people want to use AI, it's still like really surprisingly hard to eke out value, right?
00:27:55.849 --> 00:28:05.769
You need to be an amazing prompter, you need better process design, you need like really good data, you need to, you know, be very specific in how you're doing and how you're aligning something.
00:28:05.769 --> 00:28:13.369
Like just the other day, I was asking um AI to create this like image for me because I'm trying to like redo something in my house.
00:28:13.369 --> 00:28:18.410
And I I was asking, create this visual image because I just want to see what it looks like, right?
00:28:18.410 --> 00:28:21.289
And it was putting this one thing in the wrong place.
00:28:21.289 --> 00:28:23.289
And I said, no, put it on the other side.
00:28:23.289 --> 00:28:27.369
And it kept not doing that, putting it everywhere except that.
00:28:27.369 --> 00:28:28.410
I said, no.
00:28:28.410 --> 00:28:34.250
And I literally would like draw out a picture and then circle it and I would say, put it right here.
00:28:34.250 --> 00:28:35.690
And it still refused to do it.
00:28:35.690 --> 00:28:37.289
And in my, and I'm like, oh my God.
00:28:37.289 --> 00:28:39.210
And I'm like furiously typing.
00:28:39.210 --> 00:28:40.410
And I'm like, what is wrong with me?
00:28:40.410 --> 00:28:42.329
Like, why am I arguing with my computer about this?
00:28:42.329 --> 00:28:43.369
Like, put it on the left side.
00:28:43.369 --> 00:28:44.490
And then eventually I just gave up.
00:28:44.490 --> 00:28:47.210
And I'm it's like 12th attempt or no more, no more polite.
00:28:47.210 --> 00:28:48.009
Uh no more.
00:28:48.009 --> 00:28:51.690
And I'm just like, why are you not doing what I'm asking you to do?
00:28:51.690 --> 00:28:52.250
Right.
00:28:52.250 --> 00:28:57.930
And it said something like, I have really poor spatial awareness, so I can't understand your prompt.
00:28:57.930 --> 00:28:59.129
And I was like, What?
00:28:59.129 --> 00:29:01.690
Okay, you know, so anyways.
00:29:01.690 --> 00:29:03.769
Yeah.
00:29:03.769 --> 00:29:05.210
So I'm like, okay, is it me?
00:29:05.210 --> 00:29:06.730
Is it what's the skill gap here?
00:29:06.730 --> 00:29:07.609
Is it me or is it?
00:29:07.609 --> 00:29:08.009
I don't know.
00:29:08.009 --> 00:29:09.930
Anyways, and then the third expectation, right?
00:29:09.930 --> 00:29:12.410
So it's like, and this is an example of it, right?
00:29:12.410 --> 00:29:20.009
So I have this expectation that AI is gonna not read my mind, but pretty close.
00:29:20.009 --> 00:29:24.490
You know, I have this expectation that's just gonna do what I tell it to do, transform it, and then the payoff is gonna be great.
00:29:24.490 --> 00:29:25.849
I'm like, okay, yeah, that looks great.
00:29:25.849 --> 00:29:27.609
I can totally visualize what I want to do.
00:29:27.609 --> 00:29:30.569
And, you know, it just saved me so much time.
00:29:30.569 --> 00:29:37.529
But the reality of it is like you end up, it just ends up being like slower, messier, and just more frustrating experience.
00:29:37.529 --> 00:29:37.930
Right.
00:29:37.930 --> 00:29:44.569
And so I think you can take those three gaps and apply them to many scenarios.
00:29:44.569 --> 00:29:47.609
So, like, yeah, the belief, the skill, and the expectation part.
00:29:47.609 --> 00:29:59.049
I that's just kind of like how I see see things right now in organizations, in day-to-day life, in how people use AI.
00:29:59.049 --> 00:30:00.410
I don't know what you guys think about that.
00:30:00.889 --> 00:30:03.529
I I think I would add a fourth dimension, which is fear.
00:30:03.529 --> 00:30:08.490
Like, I think people are fear about what it means to their job, right?
00:30:08.490 --> 00:30:10.250
Or their whatever, right?
00:30:10.250 --> 00:30:11.769
Their agency.
00:30:11.769 --> 00:30:14.889
Um I think there's a lot of benefits of it.
00:30:14.889 --> 00:30:22.089
But it's it's interesting because you brought up that point where it doesn't, it feels like it's not it's sort of ignoring what you're asking it to do.
00:30:22.730 --> 00:30:28.170
I asked it to remove a chair and it added two more instead.
00:30:28.170 --> 00:30:32.490
I'm like, what am I what is like what am I missing here?
00:30:32.730 --> 00:30:36.170
So so this is why it'd be no chairs in the picture.
00:30:36.170 --> 00:30:38.009
Zero chairs.
00:30:38.569 --> 00:30:40.569
So um it's interesting.
00:30:40.569 --> 00:30:56.809
So this is the other part, like a risk part that I think and we were talking before we started recording about how I I think anecdotally, I think there are a lot of companies who thought, oh, we can get rid of marketing, we can get rid of these roles, and we can replace it with AI and agent, agentic AI and stuff, and that they're now quietly rehiring.
00:30:56.809 --> 00:31:03.609
I I think there's more of that happening than I than makes big news, and maybe it's just wishful thinking.
00:31:03.609 --> 00:31:19.210
But one of the things I've found that even though I spent a lot of time, and I still need to go try this with try stuff with Claude, I haven't switched over to spending time with that because like it would have I put a lot of invested a lot of time in training Chat GPT for me and what a lot of things I do.
00:31:19.210 --> 00:31:28.490
But even with that, like I find myself repeating, going like, hey, I told you don't do this, or I told you go do this, right?
00:31:28.490 --> 00:31:31.129
And it keeps it's sort of it still veers off.
00:31:31.129 --> 00:31:44.170
And what I think the the the the the risk part that I worry about, in addition to all the other ones, right, about privacy, yada, yada, yada, um, lack of control, is like just personal experience.
00:31:44.170 --> 00:31:54.250
If you're if you're relying on something like that to just work and you're not monitoring it, or it's really it can run in the background, like, oh, it's just gonna run 24-7, do all this.
00:31:54.250 --> 00:32:01.369
Like, I my take right now is like I would be really worried about that not working the way it intended.
00:32:01.369 --> 00:32:09.609
Like, we did all this, we got it right, and then it's it's like when you shoot a gun or an arrow or something like that, right?
00:32:09.609 --> 00:32:14.089
If you're off by a little bit at the beginning, it's gonna be way off when you get to the target.
00:32:14.089 --> 00:32:20.490
And it feels like that happens with AI stuff that I've personally experienced a lot.
00:32:20.889 --> 00:32:26.089
Well, that's that kind of I can't remember where uh it was on LinkedIn, someone had published something, but I can't remember who.
00:32:26.089 --> 00:32:31.609
And they had it, they had kind of like compared it to the gold rush, right?
00:32:31.609 --> 00:32:37.930
Like people who sold shovels during the gold rush, like you pay for the shovel, and then that was it.
00:32:37.930 --> 00:32:39.129
It was like good luck.
00:32:39.129 --> 00:32:43.529
And then if you struck gold or not, or went broke or whatever, it was like not their problem, you know.
00:32:43.529 --> 00:32:48.809
Like they there's an entire economy built on selling shovels in a gold rush where not everybody finds gold.
00:32:48.809 --> 00:32:50.089
So I'm done.
00:32:50.089 --> 00:32:51.769
I just thought like that, right?
00:32:51.930 --> 00:32:58.809
And me by I've often I've often uh reflected on that same sort of notion.
00:32:58.809 --> 00:33:10.490
Like when I, you know, I'll be on a walk and I'm like, I actually want to be the person that makes the special bolt to put that light up because there's lots of those lights here, and those bolts are really imperative to that light being installed.
00:33:10.490 --> 00:33:13.210
I don't care about the light, I care about the bolts.
00:33:13.210 --> 00:33:15.450
Yeah, you know, I think it's funny.
00:33:15.529 --> 00:33:16.490
It's that reminds me.
00:33:16.490 --> 00:33:23.529
So like I remember back before internet was available on planes and I would travel a lot, sat next to a guy, we actually had to talk.
00:33:23.529 --> 00:33:30.730
And we actually talk, we and uh the whole we search and start chatting, and we he, I was like, What do you do?
00:33:30.730 --> 00:33:31.289
What do you do?
00:33:31.289 --> 00:33:34.250
And he's like, I sell food grade adhesive.
00:33:34.250 --> 00:33:35.690
And I was like, What?
00:33:35.690 --> 00:33:37.049
What do you mean?
00:33:37.049 --> 00:33:41.210
And basically, it's like if you get cereal or a candy bar or whatever, right?
00:33:41.289 --> 00:33:44.649
There's adhesive that is used to steal the package, yeah.
00:33:44.970 --> 00:33:46.970
And I just like I don't know why, right?
00:33:46.970 --> 00:33:47.529
Very naively.
00:33:47.529 --> 00:33:59.289
I was like, that's just as part of whatever whoever comp whatever company produces the food i right, yeah, and it's not, and I was like, oh my god, there's all these things that are part of the economy, yeah.
00:33:59.289 --> 00:34:05.129
The supply chain, like pieces like there's all like have you heard about the the the SA i pencil?
00:34:05.129 --> 00:34:07.690
No, it's called eye pencil, yeah.
00:34:07.690 --> 00:34:14.329
So uh I don't have to I'd have to go find it, but I've heard about it multiple basically it's like somebody talks about like if you're using a pencil, right?
00:34:14.329 --> 00:34:20.410
A standard I don't have one here, I have a mechanical pencil, but you know, a wooden pencil, right?
00:34:20.410 --> 00:34:25.369
It's like you don't realize like all the things that went into producing that.
00:34:25.369 --> 00:34:30.410
If you don't like when you start thinking about it, like somebody had to go you go like go all the way back, right?
00:34:30.410 --> 00:34:38.490
Somebody had to go mine the metal for the little metal piece, somebody had to get the rubber, somebody had to get the wood, had to get the graphite, right?
00:34:38.490 --> 00:34:40.570
All that stuff, like all these things.
00:34:40.890 --> 00:34:42.250
Yeah, yeah, yeah, totally.
00:34:42.250 --> 00:34:46.010
And totally different materials combine in a very unique way, right?
00:34:46.010 --> 00:34:47.530
To like create this thing.
00:34:47.530 --> 00:34:48.650
So fascinating.
00:34:48.890 --> 00:34:52.810
And then it comes together and you pay hardly anything for it, right?
00:34:53.130 --> 00:34:54.570
Yeah, yeah.
00:34:54.570 --> 00:34:56.170
That's so crazy.
00:34:56.170 --> 00:34:57.610
Yeah, I don't know.
00:34:57.769 --> 00:35:08.570
I think I think that there's to kind of go back to what you're talking about, like people getting quietly hired back to these organizations.
00:35:08.570 --> 00:35:12.730
I think that there's probably going to continue to be more of that.
00:35:12.730 --> 00:35:19.769
Uh it doesn't dismiss the fact that AI does allow you to operate more efficiently with just one person.
00:35:19.769 --> 00:35:21.769
So perhaps what you have to do.
00:35:22.010 --> 00:35:23.210
I wouldn't assume it, yeah.
00:35:23.530 --> 00:35:23.850
Right.
00:35:23.850 --> 00:35:27.530
It it it it allows you to do that in some regard.
00:35:27.530 --> 00:35:30.730
It doesn't do it perfectly.
00:35:30.730 --> 00:35:32.970
Uh, will it do it perfectly one day?
00:35:32.970 --> 00:35:34.570
Maybe I don't, I don't know.
00:35:34.570 --> 00:35:39.370
But I think to to that end, it probably opens up this.
00:35:39.370 --> 00:35:45.050
I can run a little longer with just like two people instead of five or whatever.
00:35:45.050 --> 00:35:49.130
Um, but eventually there's like this um sort of plateau, right?
00:35:49.130 --> 00:35:55.290
There's only so much so much time in the day and so many so many things you can possibly be doing.
00:35:55.290 --> 00:36:02.970
Um I think it takes people to be able to make these things work really well.
00:36:02.970 --> 00:36:12.330
If I like I don't did I give the example on an episode previously of like the little coworker agent that I built?
00:36:12.330 --> 00:36:14.090
Did I share that with y'all?
00:36:14.330 --> 00:36:17.370
No, it sounds vaguely familiar, but I don't I don't remember it.
00:36:17.450 --> 00:36:20.090
So it might have just been you and me talking on the phone too.
00:36:20.090 --> 00:36:30.570
So that the the punchline, or I'll try to I'll try to be as as concise as I can uh on this on this example, but I think the context matters for for our listeners.
00:36:30.570 --> 00:36:35.210
Um so you know, today Cloud's got lots of capabilities.
00:36:35.210 --> 00:36:40.250
Uh people in marketing ops are often looking at the cloud code side of things, which is fascinating.
00:36:40.250 --> 00:36:43.370
I'm just not I don't have the bandwidth to be able to go look there.
00:36:43.370 --> 00:36:50.250
But what I did look at as this uh MCP connection, this the server connection to my HubSpot account.
00:36:50.250 --> 00:36:51.690
And I said, hey, you know what?
00:36:51.690 --> 00:36:59.930
We've got a lot of folks in our community that um I believe are interested in becoming members and and there's lots of value we can bring to them.
00:36:59.930 --> 00:37:01.930
And I just don't know how else to reach them.
00:37:01.930 --> 00:37:02.810
I don't have the time.
00:37:02.810 --> 00:37:04.730
In fact, we've never really tried before.
00:37:04.730 --> 00:37:14.730
And so let me let me like maybe go hire somebody, a person, to to like say, hey, let's try to let people know about what membership is.
00:37:14.730 --> 00:37:16.010
That was my plan.
00:37:16.010 --> 00:37:22.490
So I share this with my with my a peer of mine who runs another organization similar to ours.
00:37:22.490 --> 00:37:26.170
And uh, and his response to me was like, Why wouldn't you just make that an agent?
00:37:26.170 --> 00:37:26.490
Right?
00:37:26.490 --> 00:37:28.570
Like, why wouldn't an agent just do that for you?
00:37:28.570 --> 00:37:30.010
And I was like, Oh, okay, fine.
00:37:30.010 --> 00:37:32.250
He's like, you know, center your business around agents.
00:37:32.250 --> 00:37:33.210
I was like, okay, fine.
00:37:33.210 --> 00:37:41.450
So, all right, Claude, instead of writing the job description and all the steps in the workbook, the playbook for the person, I need you to pivot this to an agent.
00:37:41.450 --> 00:37:45.370
Does a nice job for that, creates a folder on my desktop.
00:37:45.370 --> 00:37:49.050
I point Claude co-work at the folder and I say, execute the job.
00:37:49.050 --> 00:37:55.769
So it hits up Hubspot, finds the 10 most likely candidates that look like they're interested in membership.
00:37:55.769 --> 00:37:59.690
It finds its own data point, didn't have to tell it which one it was.
00:37:59.690 --> 00:38:01.050
Uh, it was the right one.
00:38:01.050 --> 00:38:02.890
I was pleased by that.
00:38:02.890 --> 00:38:06.570
And uh it drafts 10 emails for me in my inbox, right?
00:38:06.570 --> 00:38:11.210
Personalized, very customized emails, like very customized emails.
00:38:11.210 --> 00:38:17.930
Like, no way anybody could do this in the short amount of time it didn't, let alone probably at all, right?
00:38:18.170 --> 00:38:18.490
Right.
00:38:18.730 --> 00:38:25.450
Um and here's the thing, everybody like this is this is the part where Naomi, you and I were talking like recently.
00:38:25.450 --> 00:38:32.490
This is the part where everybody goes to LinkedIn and is like, look at that, look at that, I did it.
00:38:32.490 --> 00:38:34.970
I have mastered the world.
00:38:34.970 --> 00:38:36.410
I am God, right?
00:38:36.410 --> 00:38:38.170
Like this is like this is ROI.
00:38:38.170 --> 00:38:39.850
This is the part, yeah.
00:38:39.850 --> 00:38:42.410
Everybody goes and shares it right with the world.
00:38:42.410 --> 00:38:46.170
The reality is that was great.
00:38:46.170 --> 00:38:48.650
Thank you, Claude Covert, for doing that.
00:38:48.650 --> 00:38:50.490
It was about 80% correct.
00:38:50.490 --> 00:38:52.010
I probably still need to coach it.
00:38:52.010 --> 00:38:55.769
I could probably get it to 90, 95% correct to where I feel really good.
00:38:55.769 --> 00:39:02.970
And I could actually probably run this on a cron job or a daily schedule to have it do 10 emails for me every single morning.
00:39:02.970 --> 00:39:09.530
But the truth of the matter is that it is not gonna continue to self-optimize.
00:39:09.530 --> 00:39:12.650
So just take it a couple steps further, right?
00:39:12.650 --> 00:39:16.650
For all of you watching or listening to this, and for you, Naomi and Hartman, right?
00:39:16.650 --> 00:39:29.530
Like, I built this little product that is focused on membership sales, that I don't have an endpoint, and I could point it at things like, hey, uh, at the end of this, go back and look to see if anybody actually signed up.
00:39:29.530 --> 00:39:32.970
Like, I could give it some more data to try to do some of these things.
00:39:32.970 --> 00:39:36.090
But to then go beyond that and be like, hey, you know what?
00:39:36.090 --> 00:39:40.730
Should we try two touches, three touches, four touches instead of just one?
00:39:40.730 --> 00:39:49.130
Should we do something like all of the rest of the things that continue to optimize the product that I built is a person's job.
00:39:49.130 --> 00:40:03.130
And they need to guide it on the nuances of the things that are happening in the market, the things that are happening in our little business, the conversations that are happening inside of our community, and help point it in the right direction because it can't pick up on all of those cues.
00:40:03.130 --> 00:40:04.490
It can't possibly do that.
00:40:04.650 --> 00:40:04.890
Right.
00:40:05.050 --> 00:40:08.490
And this is where I was like, yeah, this is great.
00:40:08.490 --> 00:40:10.490
I'm a subject matter expert in my business.
00:40:10.490 --> 00:40:15.210
I built a little AI product, and now I need to hand this off to a human.
00:40:15.210 --> 00:40:21.290
So where I started is where I ended up, which is like, I actually need a person to do this thing.
00:40:21.290 --> 00:40:37.130
And I think that's what's going to happen is that you're gonna find these businesses that are realizing, oh, I can build these little tools to do things really well, but I still need someone to optimize them and make them more effective and efficient and better.
00:40:37.130 --> 00:40:43.530
Um, you know, whether it makes them as an individual better or solves for a KPI that we're pursuing.
00:40:43.530 --> 00:40:45.210
I think that's where we're going.
00:40:45.210 --> 00:40:51.530
And that's why people are going to get hired back, is ultimately understanding the business and the technology's capabilities.
00:40:52.170 --> 00:40:52.330
Yeah.
00:40:52.490 --> 00:40:54.170
And that was the short version of the story.
00:40:54.250 --> 00:40:54.410
Yeah.
00:40:54.730 --> 00:40:59.530
No, it's it's interesting because I was at a uh an event at an engineering school last night.
00:40:59.530 --> 00:41:09.290
Um, and it was a networking thing for graduate students in in the engineering school to meet people in industry and and stuff.
00:41:09.290 --> 00:41:27.769
And um the it was interesting, the questions that came, there were some uh panel speakers uh and the questions that came from the audience, a lot of them were like you know, how are you changing how you hire engineers, especially for computer science, right?
00:41:27.769 --> 00:41:32.570
One person was at AWS, one was at a startup, uh, and is CTO.
00:41:32.570 --> 00:41:38.090
And it was really fascinating because like on the one hand, they were like, it's not really changing it.
00:41:38.090 --> 00:41:46.250
I still like the CTO guy was like, Yeah, I still look for attitude and aptitude combination, right?
00:41:46.250 --> 00:41:50.810
And somebody would fit their AWS one was a different like gigantic company.
00:41:50.810 --> 00:42:02.970
And it's like I guess I think that what this goes back to the fear, like I think these people are in graduate school, it's it's vested a lot of money or somebody did, right, to get them in there.
00:42:02.970 --> 00:42:05.450
They're like, what am I gonna do when I get out?
00:42:05.450 --> 00:42:15.530
Um, and I'm I guess I'm hopeful that there will be stuff, especially as a parent of you know, college students right now.
00:42:16.170 --> 00:42:16.810
Yeah.
00:42:16.810 --> 00:42:18.890
I think there will be.
00:42:18.890 --> 00:42:19.769
I do too.
00:42:19.769 --> 00:42:21.610
I think I think there will be.
00:42:21.610 --> 00:42:24.970
But I think it's gonna look a little different than it has in the past.
00:42:24.970 --> 00:42:43.610
It's not gonna be this like sort of I don't know, in some regards, I I I I can't say it with definitive like gumption or anything like that, but like I I do think it'll it won't feel as like myopic in in terms of like you know how to do like this particular thing.
00:42:43.610 --> 00:42:54.890
You you sort of like are almost forced to try to understand more in order to use the thing, the AI capabilities to their advantage, right?
00:42:54.890 --> 00:43:08.650
Like you're having to learn business and and the the nature of the market and the products and the things that they're saying because all of that context allows you to do more with the tool that we have access to nowadays.
00:43:08.970 --> 00:43:11.769
Now how to run an early early stage sales call.
00:43:11.769 --> 00:43:13.850
Right, right.
00:43:13.850 --> 00:43:16.570
Sorry, inside inside inside baseball days.
00:43:16.810 --> 00:43:18.090
We'll spare you all the story.
00:43:18.090 --> 00:43:28.970
But if you're running an early stage sales team, let's just make sure that you you flip for this audience, flip the deck upside down because that is Naomi's claim to fame.
00:43:28.970 --> 00:43:30.010
You're muted.
00:43:30.010 --> 00:43:30.810
You're unmuted.
00:43:31.370 --> 00:43:33.050
Naomi, perfect timing for that.
00:43:33.050 --> 00:43:33.530
Sorry.
00:43:33.530 --> 00:43:35.290
Yes, present it backwards to me.
00:43:35.290 --> 00:43:40.090
Um I want to see the price first and what it integrates with, and then everything else after.
00:43:40.170 --> 00:43:41.050
Yeah, yeah, yeah.
00:43:41.050 --> 00:43:41.930
What is the price?
00:43:41.930 --> 00:43:42.890
What does it integrate with?
00:43:42.890 --> 00:43:43.930
And let's go back.
00:43:43.930 --> 00:43:46.010
Now show me some of the capabilities.
00:43:46.170 --> 00:43:48.570
Okay, now show me who your customers are.
00:43:48.570 --> 00:43:51.050
Just start there.
00:43:51.050 --> 00:43:51.930
I don't care.
00:43:52.090 --> 00:43:53.450
I don't care about the rest.
00:43:53.450 --> 00:43:54.810
That's so funny.
00:43:54.810 --> 00:43:55.610
Yeah.
00:43:55.610 --> 00:43:56.330
Anyway, anyway.
00:43:56.730 --> 00:43:59.610
I wish I wish people would send, like, actually, that this is a thing.
00:43:59.610 --> 00:44:05.850
I challenge anyone who's listening, send a prospecting nurture email with your price to me.
00:44:05.850 --> 00:44:12.170
If you send me an email that's like, hey, our product costs this, are you can you will you join a call to learn more?
00:44:12.170 --> 00:44:13.210
I'll be like, yes.
00:44:13.210 --> 00:44:15.610
Tell me how much it costs, I will join a call.
00:44:16.010 --> 00:44:16.410
Yeah.
00:44:16.410 --> 00:44:16.810
Yeah.
00:44:16.810 --> 00:44:17.370
Right?
00:44:17.370 --> 00:44:28.170
I it's funny because like whenever I like I've run web teams and as well and It drives me crazy that people are like, oh, we don't want to publish pricing.
00:44:28.170 --> 00:44:29.130
Our competitors will get it.
00:44:29.130 --> 00:44:30.970
I was like, they already made it.
00:44:30.970 --> 00:44:32.170
But it's not it's not published.
00:44:32.490 --> 00:44:35.850
This is like you know, send it, send it as a direct email, like a one year one.
00:44:36.410 --> 00:44:41.130
But I'm like, but competitors gonna go like competitors gonna get it.
00:44:41.130 --> 00:44:42.890
They already do, right?
00:44:42.890 --> 00:44:43.370
Yeah.
00:44:43.370 --> 00:44:54.970
Like every like I would never go buy tech right now without contacting people through the community or whatever, and go like, who's got experience with this?
00:44:54.970 --> 00:44:55.850
What are you paying?
00:44:55.850 --> 00:44:58.170
Because I've been burned before, like pre-community.
00:44:58.170 --> 00:45:01.610
Like I found out after the fact like we were getting screwed by a vendor.
00:45:01.610 --> 00:45:05.290
And and it's like, and it pisses me off.
00:45:05.290 --> 00:45:11.930
And yeah, if they were doing it like I don't like pisses me off that I let that happen, right?
00:45:11.930 --> 00:45:16.650
But I didn't have any other way to get that inside information, right?
00:45:16.650 --> 00:45:20.170
And now that's pretty much the default, it feels like.
00:45:20.970 --> 00:45:22.090
Yeah, yeah.
00:45:22.250 --> 00:45:25.210
So yeah, well, go ahead, send the email with your pricing.
00:45:25.530 --> 00:45:26.090
There you go.
00:45:26.090 --> 00:45:30.170
Don't just for all the listeners, do the do the list pricing, right?
00:45:30.330 --> 00:45:31.050
List price, yeah.
00:45:31.130 --> 00:45:35.610
Just tell us like, hey, we generally sell for this range depending on a number of factors.
00:45:35.690 --> 00:45:36.570
Yeah, hear the factors.
00:45:36.730 --> 00:45:38.650
We can get on a call to discuss what those are.
00:45:38.650 --> 00:45:40.250
Yeah, okay.
00:45:40.250 --> 00:45:41.690
Sounds good.
00:45:42.090 --> 00:45:44.330
Yeah, I I would totally do that.
00:45:44.330 --> 00:45:54.650
And I still say I want vendor, I wish vendors would do instead of a free whatever premium trial for a week or a month, right?
00:45:54.650 --> 00:46:01.930
Give me three months for a cheap amount that I could pay so I can actually spend time exercising it.
00:46:01.930 --> 00:46:06.730
And because I'm putting some skin in the game, I'm gonna be more incented to spend the time to figure it out.
00:46:06.730 --> 00:46:10.970
Yeah, you have do you need to have more infrastructure for that?
00:46:10.970 --> 00:46:19.850
Maybe like and maybe you lose some deals because of that, probably, but like you're at least getting a little bit to recover from that.
00:46:20.010 --> 00:46:34.650
Like, I I think I think as as someone who's come from, you know, oh, all of us have interacted with SaaS companies most of our careers, but like as someone who came out of SaaS, like I I think I think there's also a tremendous value add to that offer, right?
00:46:34.650 --> 00:46:43.290
Which is like creating a stickier customer that is like more invested and feels like they're gonna stick around for the LTV that you want them to be around for, right?
00:46:43.290 --> 00:46:58.490
And so like le if you can like do a proof of concept motion, have them pay some amount of money to be on the platform for three months and give them an opt-out, and it shows that you're invested in seeing them like have success.
00:46:58.490 --> 00:47:03.210
And they're like, wow, like this is you know, this is great.
00:47:03.210 --> 00:47:17.290
Um, I know that organizations aren't always built to support that, but you know, yeah, I think it's money well spent compared to ad dollars, for example.
00:47:18.010 --> 00:47:18.650
Yeah.
00:47:18.650 --> 00:47:23.370
Well, as always, fun chat and here we are.
00:47:23.370 --> 00:47:23.930
Here we go.
00:47:23.930 --> 00:47:24.650
We're at the end.
00:47:24.650 --> 00:47:25.290
We're at the end.
00:47:25.290 --> 00:47:27.690
Top of the hour, top the hour.
00:47:27.690 --> 00:47:31.210
I yeah, I've got I've gotta disappear.
00:47:31.210 --> 00:47:33.130
Um, well, thanks again.
00:47:33.130 --> 00:47:34.810
Like I wish we did, I do.
00:47:34.810 --> 00:47:36.010
We need to do this more often.
00:47:36.010 --> 00:47:39.050
I think we say that every time, and then life happens.
00:47:39.610 --> 00:47:41.130
And then life happens, yeah.
00:47:41.130 --> 00:47:42.330
Uh that time.
00:47:42.570 --> 00:47:42.970
Yeah.
00:47:42.970 --> 00:47:44.570
So we'll do it again, I'm sure.
00:47:44.570 --> 00:47:45.850
Anyway, thanks, Joe.
00:47:45.850 --> 00:47:50.250
Thanks to all those out there who've uh listened and supported us over the years.
00:47:50.250 --> 00:47:54.010
Uh we always are looking for guests and topics.
00:47:54.010 --> 00:47:59.370
So if you have either one of those or we want to be a guest, you can reach out to any any one of us, and we'd be happy to get the ball rolling.
00:47:59.370 --> 00:48:00.090
Until next time.
00:48:00.090 --> 00:48:01.210
Bye, everybody.
00:48:01.610 --> 00:48:02.410
Bye, everyone.
00:48:02.650 --> 00:48:03.210
Bye.