A (3:12)

Right.

B (3:13)

Over two years. Legal AI. Another area where you're seeing a lot of activity. Harvey lagora. Harvey's over 100 million in annual revenue. Search gleam. Rapid prototyping of web apps. Lovable. So, I mean, there are some companies with staggering growth rates. I mean, and just to put that in perspective for later listeners, we used to think getting from 0 to 100 million in revenue in about eight to 10 years, that was best in class for decades. They kind of get down to that was the sort of the Salesforce DocuSign era. Then it got down to like 3 to 4 with Wiz and Coreweave. Now it's like a year. These companies, I just mentioned, many of them, it took one year to go from zero to 100. So there's a lot of explosive growth. But it's specific to the certain areas and those are businesses that are either targeting developers, early adopters of new tech, or businesses where the fit between generative AI is just hand in glove. Like Legal, perfect example, right. It's texted, maxed out, sort of tailor made. Beyond that, it's still a lot of experimentation, especially when you talk about the enterprise world, right? Enormous pressure to do something, right?

A (4:31)

I mean, enormous.

B (4:34)

Give me a win, you know, show. Let me get out there in front of Wall street and say that, you know, we're an AI company, but it's a slog, right? Getting widespread enterprise adoption is not easy. Getting these deployments into production, really hard. But, but, but it's coming, right? 90% of it budgets are going to be up next year and of that, 90% of them think they're going to increase their AI spending. And you know, so I mean, don't get me wrong. And the vendor spend, that's where the real action is, right? I mean, you know, as in I can't, I can't.

A (5:10)

When you say vendor spend, tell, tell our listeners what you mean by that.

B (5:13)

I'm talking about, you know, the hyperscalers, the companies that are actually building out these data centers to run AI. And then the AI platform providers themselves. The best statistic I've seen on this topic, there's a, there's a, there is a renowned venture capitalist, a guy who's been around for forever, named Roger McNamee. And he that by the end of 2025 the tech industry will have invested about three quarters of a trillion over the last three years into LLM AI and that infrastructure supporting it. And if you believe that we're, you know, that's a trend line that will invest a, you know, relatively comparable amount in the next year, that the AI industry will have received more capital than has been invested in the rest of the tech industry from the dawn of Silicon Valley. Right. Sort of the mid-50s, when, you know, the transistor was invented. And I mean, that's just. It's staggering.

A (6:15)

But the valuations are, are going with it and the hype is going with it. So how do you as an investor think about these valuations? Because it's, it's like, you know, and also the circular investing thing that's going on, how do you look at that? I mean, as both an investor and an industry observer?

B (6:40)

Yeah, yeah, it is. It's challenging, to say the least. First of all, people are like, is there a bubble? You know, the bubble question, we might as well address it because I know it'll come up at some point. Yeah, there's no doubt this is a bubble. Right. I mean, something like, if you look at the public market, something like 80% of all gains this year are coming from here. That's right. If you just pull out, you know, the public markets are not really performing very well, but. But it's totally clouded over by AI. And by the way, there's also this huge bifurcation public. If they say you're an AI winner, you trade it, you know, many multiples of revenue.

A (7:20)

Oh, yeah, we're changing our name to CMO Confidential. AI.

B (7:26)

Perfect. AI Confidential, even better. And in the private markets, as you point out, you're right, the valuations are extraordinary. And you know this, I've been around long enough, this doesn't end well. And it's not just that valuations are going up, by the way, it's that there's a lack of oversight creeping in. It's that rounds are happening at an unbelievably accelerated.

A (7:50)

When you say rounds, that he means investment rounds, where people are just taking money without, I'm going to say, as much due diligence as they might historically have applied.

B (8:02)

But again, to try to make it, to put a point here, it's not unusual. Now, for if we're investing in a company, let's call it the Series B round. Before that Series B round has closed, another investor has come to the company and said, hey, we'd like to put money in it. 2x the price of the round that is just isn't even closed. And so, you know, you're having the series C happen, you know, right away, right after the series B. It's, it's absolutely, you know, just not a great fact pattern. I'm not done with the terrifying.

A (8:42)

Okay, go ahead.

B (8:45)

The other thing you we have to worry about is there's this phenomenon of what is known as AI Washington. Right. The practice of, you know, every company just what you were doing with your own podcast name. Yeah, Exaggerating, you know, your, your company's AI use or capabilities and you know, every deal is an AI. Oh no, Rob.

A (9:04)

We use it for the newsletter and some other things. So we're really early adopters.

B (9:10)

And I will say, you know, the technology, the underlying theme that's going to come out here is this technology is moving so rapidly and therefore what is really successful today. There's a lot of question about that long term viability of that business. And remember, I can't, I'm not a public investor. I can't just decide tomorrow, hey, I don't really like this business anymore. There's a better one over there, I'm going to sell it. I'm in this thing, you know, because.

A (9:40)

When, when a VC invests, they're in, they, they can't get the money out until another round or someone buys them out.

B (9:47)

So these are illiquid securities. And yeah, a competitor can leapfrog your technology. We're seeing it already in AI, you know, again and again and the switching costs are so low. So the cost of being wrong here is just, you know, extraordinary, extraordinarily high. I mean, just to give you a sense for this durability of revenue concern, we looked at a legal AI company a few 18 months ago, two years ago, and we did a diligence call with a really well known legal firm in Silicon Valley. You know, one of the, one of the customers that you absolutely like to have. And the feedback was extremely positive on this AI software. And we got to the end of the call and of course we said, well, so you're going to buy this, right? Because it was just running a proof of concept where they get to use the software for several months and try it out. And, and his response was, oh God no, why would I, why would I do that? I a little thin veneer, you know, that sits on top of this AI software between the users and the software. And you know, once the PoC is done, I'll go grab the next great thing because there's be something that'll be Way better. And I'll just unplug this and plug that in and, you know, my lawyers won't even know the difference.

A (11:08)

And POC is proof of concept.

B (11:12)

Yeah. So that's. And I guess there's one more thing I'd point out. Unlike in 2000 with the Internet bubble, the startups had a big advantage because the incumbents were basically catatonic. Right. They were asleep at the wheel. It wasn't really a big fight back. That is not the case this time. You know who's leading the charge here? It's Microsoft, it's Google, it's Amazon. And these are formidable competitors, right? Highly ambitious. They've got great leadership, you know, you know, very, very strong, limitless resources.

A (11:46)

Almost.

B (11:46)

Yeah, yeah, but okay, so that's all the negative. Let me give you the reason though, that people then are still, you know, you know, leaning in or jumping in. Vcs, we live for platform shifts, right? That is, it's not, it's not wholly untrue to say that you as a vc, you make all your money in these periods like you, and in between, you sort of just try to keep your head above water until that next.

A (12:14)

Big kind of platform shift would be the Digital Revolution. Mobile 2.0.

B (12:19)

Cloud computing farther back, obviously the Internet, farther.

A (12:24)

Social. Yeah.

B (12:25)

There's just been waves of them. Right. And it's, and it's not also, you know, really unusual to say that this is the mother of all, you know, waves.

A (12:36)

And no, one of our, one of our guests, the, the, the authors that wrote AI first said, this is the biggest prize in the history of capitalism.

B (12:45)

You got it. You got it. So, you know, you are not, as a venture capitalist, you do not want to miss out on, this is your moment to shine, maybe nothing like this ever again in your, your career. And so, yeah, that's why despite everything else I just said, there is an unbelievable amount of activity. And to be fair, there are all kinds of really interesting startups doing great things and succeeding. There's also some advantages to this AI wave that startups never had before. I mean, part of the challenge or the trade off, I guess let's put it this way, is startups always were able to, you know, deliver a much simpler product experience. But, but the incumbents would win still because you, you know, they could configure things and customize things in a way that was really useful for, for, you know, that individual purchaser consulting firms and folks like that really, you know, make the playing field, you know, more difficult for startups with LLMs. Now they can enable configurability and customization in a really rapid manner. And so it's given startups equal footing, so to speak, in terms of fighting the battle on the large incumbents battleground. So it's a super terrifying time, but it's super exciting time too.

A (14:11)

So we talked a little pre show about how you gauge how these companies turn profitable, especially during the investment phase. And then also if you're going to adopt these as a company, the switching costs may be small now, but if you really get everybody trained on one or two of them, how's the switching cost later? How do you guys think about that? When you look at profitability, sustainability, like companies that are going to win and companies that are more focused and are going to get eventually passed, bought or crushed.

B (14:48)

Yeah, to be fair, and we did joke about this to begin with, we don't really look at profitability. I mean the farthest out we will look at is when will this company become cash flow positive, becoming profitable even as often, Many, many for at least several years beyond that point in time. And I will tell you that is, you know, there's always a bit of a trade off between growth and profitability. Right? Yeah, grow. It's easier to get profitable if you grow at a slower rate because almost by definition when you're growing more quickly, it means you're having to, you know, hire more developers and hire more salespeople and you're layering in more operating costs. Now that has definitely flipped a little bit here with, with AI and, and it's a big part of it is because companies are able to get to market so much faster with such, you know, such lower levels of headcount. You know, there's a famous story about the, you know, cursor coming to market and it was like a, you know, three person team that actually brought the product out originally. That's, you know, that's a little bit of a hyperbolic example, but it's, but it's directionally. Right. And we see this even when in our own portfolio. Right. It's not unusual to see companies that have gotten to tens of millions of revenue and their headcount is, you know, 10 people.

A (16:21)

Right.

B (16:22)

You will see some of these businesses, you know, become a little bit more profitable certainly at an earlier stage. Not to say they still aren't raising huge rounds because of these huge, these, these big valuations. And I might also say venture capitalists are partly to blame. There's a, there's a very terrible term going around called the foie. Gras effect and it's basically larding startups with capital until they, till they burst. And because again, the average venture fund has grown so big and has so much capital and they're just desperate to find places to put it. So even these profitable or relatively efficient companies are still raising mass.

A (17:07)

That's a foie gras bubble. I mean, the foie gras thing is now stuck in my head. So we're also seeing all the circular investing. You have the foie gras thing. We just had the core weave announcement where they lost, you know, $40 billion valuation almost in no time. How do you think about all this circular investing? And then you know, Amari's law, you know that you're underestimating now and it's, it's going to have an effect later. How should our marketers be looking at this when they are evaluating what to do?

B (17:47)

Yeah, I, it's a problem. It's real, you know, I mean, get me wrong, real dollars are being spent and chips are being shipped, but a lot of it is circular now. It's a little better than what we saw in the dot com boom.

A (18:04)

Right where it was, that was just trading money. I mean, that was circular.

B (18:08)

Yeah, there were no real assets here. There's some physical assets. Right. So it's much, I, it's much closer to sort of the Nortel Lucent vendor financing deals back in the day. Again, those didn't end well either. Balance sheets are a little better to begin with, but the sheer scale means that even with healthy balance sheets, it's, it's eye opening. And you know, the scary part is so much of this data center spend, it's not even, I'm not even sure we, you know, it's going to pay off even if it gets done and gets done properly. And here's what I mean by that. As an example, there's these companies called NeoClouds that are like together and Lambda and they basically offer their cloud providers. They're startups that offer, you know, a GPU centric architecture, right, Tailored for AI workloads. And you'd say to yourself, why in the hell, why are these companies, you know, able to succeed? The hyperscale, you know, the Amazons, the Microsoft Azure, the gcp, Google cloud platform, they've got tons of data centers. The reason is those data centers are not optimized for AI. It turns out the value of a data center, it's highly dependent on the specific use case. Right. And back to what I said earlier. But you know, AI is changing really rapidly. So will all this construction that's happening today even be the right kind of data center architecture in five years? You know, that's far from clear.

A (19:37)

That's a big question.

B (19:39)

Let me, let me layer on another sort of scary thought again. This technology is progressing incredibly rapidly. So how do we really know what that demand is going to look like? Because this speed and scale of improvement means it's impossible to forecast demand. Right.

A (19:55)

I just have to ask this. So essentially if you were building ships as, and I'm doing an analogy, and data centers are ships and you are building battleships.

B (20:07)

Exactly. It's a perfect.

A (20:08)

And five years from now you realize, oh, this is going to be an air war. I should have built carriers. These battleships are not usable. You have to scrap them and build aircraft carriers.

B (20:21)

Yeah, great analogy. Right. You can be parading your great white fleet around the globe like we did in early 1900s and then suddenly realize, oh, this really isn't where the game, the puck has moved. And the proof of how tenuous this is starting to become is just look at the way the finance world is starting to react to these deals. Credit default swaps, boy, we all thought we'd never hear that term again after the mortgage financial crisis of a decade ago. The purchase of those, by the way, has exploded in the last.

A (21:02)

Everybody how credit default works and just a couple of sentences.

B (21:07)

You're betting against something. Right? Back in the financial crisis, you bet against the housing market. The big bet that paid off spectacularly was that, you know, we were, we were selling, you know, packaging together and selling these mortgages, you know, that were all subprime but is packaging them together and, and washing them and saying they were prime. And so it's essentially a bet insurance policy against investment. So you pay a premium to the seller and it pays out if that borrower, in this case the borrower defaults on a loan. So you're.

A (21:36)

So back to my analogy. I'm saying you're going to be building all these battleships and I believe you're going to need carriers. So I am buying a credit default swap against your battleship.

B (21:50)

Yes, exactly right. And the biggest targets now are Oracle, they're coreweave. They're all the businesses that are, you know, the less credit worthy relative in the, you know, in this great game that's going on right now.

A (22:06)

So if I'm sitting there listening to this show and I might, you know, we have a lot of C level people listening to this show and also a lot of agencies and Consultants. How should they be picking vendors or picking people to play with in this market?

B (22:24)

Yeah, I think that the best thing today is to choose someone that's a, you know, fits under the umbrella of a trusted advisor. Right. It's not, this is not a technology bet alone because again, as I mentioned, things are moving so rapidly. What you really need is somebody that you trust can help you navigate, you know, those, those twists and turns. And the other part of it is you need somebody who can help educate your team. Right. This is again, incredibly tumultuous, period. You need, you need somebody who has exceptional client experience. Right. You need somebody who can help you with change management. This is, you know, when we can get in the topic of why some things are succeeding and why some things aren't. I think a big, this is a big part of it, right? You, you have to, you have to have that approach. You can't skip that. You know, you can't. Don't, don't skip the people part. Right. This is. People are.

A (23:25)

Yeah, we did a whole show that. The hardest part about AI is the culture.

B (23:30)

Yeah. Change management. I mean, it sounds boring, but it's absolutely true. And, and it's doubly true here because expectations are so out of control. Right. People think this is, you know, black magic. It's going to solve everything and they're going to, you know, so they start applying it everywhere, all over the organization. Not the way to adopt AI. Right.

A (23:50)

Small focused use cases. But here's one of some of our other guests have said, you got to have a use case, you got to actually watch it really hard. And then you got to put this into the org. In a way, the org accepts it. But we've, and we've also had a couple guests say, don't be buying small little answers like go with a frontier model. And then if you want to buy some small answers, great. But don't be messing around with a million little startups if you're a big company. What do you say to that?

B (24:26)

Yeah, I think there's definitely a time and a place for that. But again, it gets back to who's going to be there. Who do I trust can deliver, you know, a successful outcome for me? Sometimes startups are the ones that can, will give you the most attention and be the most, you know, give you the most bespoke solution that allows you to win. The other thing I'd say that people skip over is it's not, you know, the people that see the real results aren't just the ones that have the most advanced models, but it's the ones that can manage the complexity of the data layers. You know, said another way, there's no AI strategy without a really sound data.

A (25:07)

It'd be a good example of someone managing the data layers really well.

B (25:11)

Well, here's the way I would say, first thing is, you know, you need to have incredible context. You need to know, I mean, AI is a, some level a black box. And so therefore how are you going to trust the output of these models unless you trust the inputs, you know, you know where the data is and you know, you know who touched it and whether it's, you know, the freshest data. The puzzle analogy is a good one. Right. The, the data are the pieces of the puzzle, but the, the context that you need is the image on the box. Right. Allows you to sort of put the pieces together and transform it into, into action. And you know, shameless plug. We've got a portfolio company named Atlin and this is what they do, right. For, for large organizations, they provide that data context layer. And it's very different, by the way, for AI than bi, right. People will say, oh, I've got a data strategy for business intelligence. Sorry, that's bi. That was sort of the world of data before we had AI, but it's totally flipped, right? In bi, structured data rows and columns was really easy. The unstructured stuff was hard. Now it's reverse, you know, unstructured, actually easier for, for AI that structured data, you need a context layer. Because here's an example, I could say to you, you know, what are our high value orders that are at risk? And you know, the, how's the AI going to know what I actually mean by that? What's at risk?

A (26:44)

Or yeah, if I said I want to go on vacation, it could give me 100 vacations, but it would have to know what I kind of wanted.

B (26:51)

That's right. Right. And, and, and by the way, you know, the AI is never going to say I don't know. Right. It'll hallucinate. Oh.

A (26:59)

No matter what you ask it, it always says. That's a great question, Rob. I like how it treats me that way. Yeah.

B (27:07)

Is, you know, our, our, our, our, you know, my colleague and your close friend Paul's Madera's favorite phrase. Right. Frequently wrong, but never in doubt.

A (27:14)

I mean, never in doubt. Yes. Hey, so if I'm sitting out there and I'm, I'm watching VC patterns because, you know, I want to pay attention as a marketer or a business person, what Can I be watching in this industry to just increase my knowledge base?

B (27:37)

Yeah, yeah, yeah. I think, you know, it's, here's the way I'd say it. I would say it's, you want to dive in, right. You know, now is the time, if you haven't already started, you know, yesterday was not too soon and the way where we have seen success, at least within our portfolio companies and even with our firm. And I'll talk a little bit about how we use AI, but you know, as a marketer, start again with those easy wins. Bring AI to initiatives that you're already employing, you know, that you're already using, you're doing manually, but you can't properly scale. Right. Without hiring a ton of humans. So you're sort of rate limited without using AI. So what do I mean by that? What's in that bucket? You know, targeting. Right. More data leads to much more accurate sort of lead scoring content personalization, you know, and, and we've got a great company, shameless plug number two called Clay that does this.

A (28:42)

You're doing a great job of getting.

B (28:43)

These in subtly, you know, venture capitalist, what can I say?

A (28:50)

But we'll do PR training separately.

B (28:53)

They will, they will, they will be able to use specific phrases, you know, used by these prospects in the past and, and you know, and bring that into the content that you're creating back to these individuals. So it's that, that level of detail at the same time, you know, avoid the stuff that's really elusive. Right. Certainly the creative stuff, I mean, AI, everything I've heard, it's still unable to come up with, with sort of out of the box ideas, even intent, you know, that's very, very specific to your product and market. And it's not often easy to, to do, you know, looping to how we use it. I mean, we, there are areas that, you know, you experiment, you figure out and you, and you rapidly iterate. You realize there's areas where it's really useful. I mean, researching a company, incredibly useful. I mean, we do it in a deep research Mode. It'll take 20 minutes, but you'll get a prep pack on a company better than any associate could ever pull together. We'll do it for note taking, we'll do it for translating text to voice so we can listen to something while we're driving. What we, we don't do it, we don't use it for sending emails to founders. It's just not good enough. You know, it's still the very definition of, you know, AI works a Lot. But I'm sure you've probably heard that phrase.

A (30:12)

No, it's what if my one of our guests said it's like having a bunch of well intentioned, earnest 18 year old interns working for you.

B (30:22)

Yeah, best of intentions. But boy, the product is just, yeah. Not, not good enough. So yeah, that's the way we can learn from how we're using it for sure.

A (30:34)

Hey, so beneath all this, there's been a lot of people that have said they're laying off all these people because of AI. But beneath what you just said, that hasn't even really happened yet. Are these layoffs real and what's really happening down the road here in terms of actual jobs?

B (30:54)

Yeah, I mean certainly you are reading about layoffs and I think unemployment set up at a four or five month high right now. But I'll be a little cynical. I mean it's the ultimate free pass, right? For the.

A (31:08)

It's a free pass. You bet.

B (31:10)

Enterprise CEO whose business is, you know, flailing and they need to cut heads. It's like, oh yeah, we're going to be. So I'm sure there's some of that, you know, going on. I'll give you an example that's just too good not to share. There's a company called Klarna, I believe it's a public company now.

A (31:26)

Oh yeah, yeah, yeah. They're deferred payments.

B (31:30)

Yes, exactly. And the CEO is, he's a bit sensational. He announced, this guy first announced a few years ago he was getting rid of all his SaaS. He was moving off SaaS and going completely to AI and getting rid of Workday, getting rid of Salesforce, which he did get rid of. He failed to mention though that he actually brought in another one of our portfolio companies. Okay. I'm up to three Pigment, which is a great company to do a lot of his work but you know, so this is who we're dealing with. So he came out about a year ago and said, I'm going to lay off all 700 of my customer support people and replace them with AI. You know, this is, AI can do everything, I don't need humans anymore, blah, blah, blah. Well, just a few weeks ago he said, ah, turns out the effectiveness is degraded a little bit. I'm actually going to be bringing some humans back and customer support. So I think, you know, the truth is, is somewhere in the middle you're getting these bold statements because back to that change management thing. You need to, you know, one way to get your team to act and to act aggressively is that, you know, you sort of have to plant the flag aggressively and be the strong leader to motivate employees because nobody likes to change. I mean, everybody's comfortable, you know, in their same old, same old. So that's, I think that's a lot of it. There's no doubt it's harder for a younger developer to get a job. What we see is it's really the low end jobs, they're the tougher ones. I think this is true even in the marketing organization. Right?

A (33:05)

Without a doubt, without a doubt.

B (33:08)

AI is a godsend for a cmo. But there's certain people lower in the organization that you know it's going to be tough. On the other hand, I'll say there's areas like demand gen that you know, you're going to, you're going to need humans. So it's not, it's unclear.

A (33:24)

Right.

B (33:25)

It's obviously going to have an effect, but I don't know if it's going to be as dramatic as the headlines portray it to be.

A (33:29)

And if you were, if you were advising our listeners on questions, a killer question or two, they should ask either candidates or potential partners on AI, do you have any like great questions they should ask to say yes, you are really AI savvy or you really get it? Get this.

B (33:52)

I, you know, I don't know that I have a super original answer other than yeah, just explain to me how you're using it in your, you know what, in your day to day life today, how have you brought it in? Where have you seen success? Where have you seen failure? You just want, you, you need somebody who is, you know, an AI native. That's, that's what you're looking for, right? To hire every new hire you're bringing in. Whatever level you're at these, you, you want the people. You know, one of the hard things for, for technology companies is finding the, the early adopters, the evangelists. Right. It's a broad product. You know, obviously if you're selling a accounting software, you know, right. Who to go to. But you know, selling Tableau, it's a business intelligence software. Everybody uses it. How do I know who the right people are to target? And I bring this up because this is the kind of person you want to bring in to your organization. The one that's willing to try the new technology and to figure out ways to have success and then also broadcast that success throughout the organization. That's, you know, you can't have enough people like that right now.

A (34:57)

Yes. If you want To Rob. We will list all your portfolio companies in the comments if you want to send it to us when we post the show.

B (35:08)

I haven't even gotten to our new AI investment.

A (35:10)

I know, but we'll just do it for you. Hey, before we get to our traditional last question, is there any free AI thoughts or advice to our listeners on this topic that you want to share that we haven't talked about?

B (35:30)

We haven't talked about what else is interesting? To me? I think the other interesting thing to think about or look at is the whole safety question. I mean, it's just, hey, and I don't, and I'm not really talking as much about national security and, and nor even the, you know, the, you know, the black swan, you know, AI is going to take over the world. It's more just where are we headed and how, how do we make sure, you know, we are using AI in the most socially responsible manner? Maybe this is a, this is a hot topic for me personally as somebody who invested in social media companies and felt pretty let down by their inability to, you know, make progress on that front. I think the national security stuff is fascinating too because, and it impacts safety because, you know, we've got China to deal with here and they've come out with not only really promising open source frontier models, but thanks to our sanctions, you know, those were the best things for the Chinese semiconductor industry than anything else in the world. Also, you look at the amount of data center capacity that they're adding relative to the U.S. it's a little, it's a little alarming. So that's a whole nother topic.

A (36:49)

But I, we may have to do a whole nother show, you know, in late 2026 where you look back on this. So we are now to our traditional last question. It's a two parter. You can take one part or both, but you have to take at least one funniest story you can share on the air or any practical advice we haven't discussed yet.

B (37:14)

Yeah, and I mentioned I was going to bring up yet another Maritech portfolio company, but this one, you are a.

A (37:22)

PR person's dream, Rob Ward. I mean you are really just bringing it.

B (37:27)

But this one everyone already knows about because it's Astronomer. And for the one person, listener out there that wasn't aware of what happened with the Coldplay concert this summer, just look it up online. And I'm not going to get into the actual incident, but I think the aftermath is not only a pretty fun story, but it's also a great it falls under the good, practical advice. The incident in question required the company to remove their CEO and remove their head of HR and do it really rapidly. And since this whole thing went viral, we really had to change the public perception of Astronomer and change that narrative and pretty quickly. And what came next was pretty inspired. I'm guessing you've seen the. The video I'm referring to with Gwyneth Paltrow. Yes. Do you know where that. The source of that inspiration for that video was?

A (38:24)

No. Tell us about. Tell us the people that haven't seen the video. Tell us about the video and tell us the inspiration.

B (38:29)

The video itself was. Was. Was just, you know, I guess people, you know, the comments afterwards were like, it was a case study in crisis management. An absolute masterclass because it. It had Gwyneth Paltrow as a. As a temporary spokesperson for Astronomer. The. The rub being Gwyneth used to date.

A (38:52)

The Coldplay lead singer. Yeah.

B (38:54)

And. And she said, look, I'm sure you have a lot of questions about Astronomer. And it started, you know, with a question popped up like, what the actual S. You know, you know, blah, blah, blah. And she said, yes, I know Astronomer is the best solution for building data pipelines. So it totally reframed the scandal and humanized the brand and sort of cleverly tied that ad back to the event and just completely moved beyond it, by the way. Also created a ton of awareness. That wasn't even the goal, but the number of impressions they got was as good as any flagship ad campaign for any software company ever. The point of the story is that that whole inspiration came from Ryan Reynolds. Ryan Reynolds, the actor. Ryan Reynolds personally made sure that ad was run to the point there where his. His ad agency, you know, he waived his fee, did it for free. He said, I know how to totally change the narrative here. I so want to do this. I can deliver. Gwyneth. Let me have at it. And, you know, it was incredible. So the practical advice is if your company ever finds itself in a very public and embarrassing situation, you want to. You want to get down on your knees and pray for Ryan Reynolds.

A (40:10)

I mean, the end here is good, but I wouldn't advise anybody to go through the process.

B (40:15)

No, no, no, no. But things happen, right? And the guy did it for Peloton as well. You know, I don't know if you remember those ads, but so. And, you know, the advice is don't. Don't run from the issue. Don't hide from it, own it and laugh at it and alter their narrative so that's that's my advice for the day.

A (40:34)

Well, I think that is a great way to end the show. Thank you for joining us Rob, and thanks to everyone for listening to CMO Confidential. If you are enjoying our content, please like share and subscribe. New shows drop every Tuesday and you can find all of our more than 150 shows on Apple, YouTube and Spotify, which include Colonel Mustard in the study with the job spec. What your CFO wants to tell you, but won't the AI application layer the good, the bad and the ugly? And why can can't. Hey all you marketers stay safe out there. This is Mike Linton signing off for CMO Confident.