3 Shocking Things Happening in Research Tech Right Now

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The data economy just hit a tipping point. Databricks' $100 billion valuation isn't just another big number—it signals a fundamental shift where data infrastructure becomes the ultimate competitive moat. Meanwhile, AI-powered platforms are collapsing research timelines from weeks to minutes, strategic partnerships are replacing winner-take-all competition, and counterintuitively, smaller specialized AI models are outperforming their massive counterparts.

If you're wondering how these seismic shifts will reshape your industry, this episode breaks down the patterns that every business leader needs to understand.

Many thanks to our producer, Karley Dartouzos. 

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Transcript

Lenny Murphy: And there we go.

Karen Lynch: Hello, everybody. Happy Friday. It is Friday. Thank the good Lord it's Friday, as I say around here. I think that the people, the listeners, the viewers, they would be surprised to know that you and I stopped talking seconds before we start. And Karley, our producer, is like, I'm going to step out just in case we go live with you to just shoot in the breeze.

Lenny Murphy: So anyway. Yeah. It's like the awkward, uh, the awkward silence, uh, type of pairing anyway. Um, I want to give a shout out though, really quickly.

Karen Lynch: Yes.

Lenny Murphy: The comment that was made that we are, uh, flotation devices. That was a good one.

Karen Lynch: Uh, that was a good one. I'm like, Oh, right. I shared that. Right. It was Susan. Yes. Yes. Susan Patel. Yes. Susan Patel. Karen, your and Lenny's exchange episodes are awesome flotation devices in the tsunami of change our industry is facing with AI. Yes. So, Susan, I love you.

Lenny Murphy: Thank you. My new nickname now is Noodle as a result of that. So, folks, you have like, oh, Lenny is a pool noodle, not a, oh, I guess it's worse than the things we compared to, but that was pretty funny. So thank you. I don't know who's calling you that, but Susan, Susan. I'm more like a stuffed shell, I think. You were going to eat noodles because I have a grandchild.

Karen Lynch: I was going to like, you know, like those floaties. Yeah. I mean, you go off on that tangent, but we won't. All right.

Lenny Murphy: Let's dive in.

Karen Lynch: Yeah, because there's some you know, there's always there's always good stuff, right? But there's some really interesting stuff right now. And I think that, you know, it's not, it's not self fulfilling prophecies. But we've been talking about some part of the need for partnerships, and we're seeing a lot of that in the last week, a few several partnerships were announced. So sometimes things that Lenny and I talked about actually would come to fruition. So it's really cool for us to see that. But let's start with the money, right? Show me the money.

Lenny Murphy: Yeah, yeah, well, the, and speaking of partnerships, right, Thrive Capital, double down on Databricks, 100 billion valuation, Series K, which is interesting, Series K, you know, but what really, Databricks is a place where data is kind of, effectively kind of a data marketplace. They do more than that, it's data cleaning, et cetera, et cetera, kind of an infrastructure provider. What particularly jumped out at me on that though was that that was going for the development of their own agentic marketplace and for data feeds. What, you know. Right.

Karen Lynch: Spoiler alert, we'll be talking about agents later in this episode.

Lenny Murphy: We will, but that, but from that idea of, okay, these data marketplaces, any marketplace, right? It's about, how easy can you make the supply chain, right? The entire distribution process. And that's just an example, again, where the technologies make, really allow for a more seamless and frictionless process with data as an asset, which feeds all of the beasts and Databricks, Snowflake, you know, there's other companies out there that are in this arena have been positioning themselves for years in this. I can't blame $100 million valuation and more money going into it, a Series K for a company that seems like they may actually become as integral to the infrastructure of the world as anything else.

Karen Lynch: So yeah, that was it. And in this release, it gives the stat that Snowflake recorded 20 year-over-year revenue growth in May. So, you know, following the money, there's some confidence in the type of work that these companies are doing in our disrupted world. Yep.

Lenny Murphy: Well, did you see, I just thought about this a couple weeks ago, I don't know if we even covered it, but Sint had made an announcement about integration of their data into Snowflake.

Karen Lynch: I think we did.

Lenny Murphy: Um, yeah, I don't recall if we did or not. But there was a panel company that's sitting on specific data assets now integrated into that ecosystem. Yeah. And I think we're gonna see a heck of a lot more of that. Well, we'll touch on it with Nielsen. Yeah, yeah, yeah.

Karen Lynch: Yeah, there's a lot of common threads in some of these stories. So let's also talk about this, this platform is not too confused with dig insights, right? So we have a dig in and dig insights, but there's another platform that raised some money 14 million Series A funding to expand insights capabilities. And this is social video intelligence. And, you know, I just think that, you know, part of what they're saying is here, it's like unlocking insights at scale. Yeah, yeah, yeah. Tracking perceptions in real time. And I'm like, you know what, as we get more and more into this socially integrated world that we're in, and that's where a lot of these conversations are happening, I'm like, yeah, I'd keep an eye on this company also. Because I think there's something about the ability now for AI to be even stronger in that social mining, but now also social mining of video, which is a whole different animal, so. Cool.

Lenny Murphy: Well, it's also, this says DIG, and then the next one, MX-8, is that how you pronounce it?

Karen Lynch: Yeah, yeah, MX-8, I think MX-8.

Lenny Murphy: I mean, I guess I heard it out loud, but yeah, let's go there. Yeah, launched to speed up survey-based research. Yeah. So, congratulations to both those companies. I don't mean this in any disparaging way, but I look at this, and it's like, but there's a whole industry that's been doing this for a long time, so what is different?

Karen Lynch: I know, I read that too. I thought the same thing. I was like, well, that's interesting, but what is the difference? The difference is the capabilities and speed. Today's technology is allowing them to launch with speed as part of their offering, as opposed to people who are like, and now we can do our research faster. These people are coming to the market faster.

Lenny Murphy: Well, that's a good point, because I would argue the core capability, being able to conduct, you know, n equals 1000, you know, studies in an hour for a long time. But I think what so that yes, everything you just said, but there's also this element of but they're not selling into the research space, right, they're taking advantage of the I believe, the expanding user base, you know, buyer universe of, of potential they don't have anything to do with the insights organization. And similar to what SurveyMonkey, they didn't sell to market researchers, SurveyMonkey sold to marketers. Qualtrics sold to CX. So these companies continue to prove that that opportunity to create what is absolutely research, we look at it, it looks like research, smells like research, tastes like research, but it's the buying process. It's who they're targeting that is different.

Karen Lynch: And they're talking about this, you know, and I say, they're talking about a solution to track the daily swings in customer sentiment, as opposed to the survey lagging that can happen, right? Which, you know, yes, that's, that's a great value when it's positioned to that way, you know, against kind of surveys that are quickly going to be improving thanks to technology, they're like, guess what, you don't have to wait for other systems that you're using to improve, we got you. So, you know, and let's talk about this YouGov and Metricom thing, because there's this, this three of them, when I really looked at this, you know, what Digg is doing, what MX 8 is doing, and what YouGov and Metricom are doing, they're all kind of related, right to this, this, this real timeness being realized at this point in time, you know, to keep the insights kind of in the current context and not at all dated. So I think that's what all three of these have in common. So you know, you go better than me. Tell me about this tool that they've introduced. Yeah.

Lenny Murphy: I mean, you can, one of their core products is basically brand health tracker. This is making it more real time with the brand index data of media analytics to measure press impact on brand health. So where brand tracking is generally more of a fairly static thing, it's not particularly, it's not looking at daily type of issues, where this really is looking, hey, we just put out a press release, what's the brand impact on that? Which is generally gonna be fairly short. I mean, we do that every week, right? We're talking about press releases, getting to boost for a week or so. But then, you know, it goes back to a kind of stasis. So, yes, moving more towards a real time, ongoing 360 degree view of different inputs that impact brands. And also, you know, Metricom is not a company that plays in the research space. So it's a, you know, partnership and they're happening outside of the usual suspects. That takes us to our next one, right? Nielsen and InfoSum. The Nielsen Marketing Cloud, which keep in mind, Nielsen's had a very successful IPO. So they went back and it worked real well. I actually kicked myself that I didn't buy some stock within my public again, because it's done really, really well. And it's because of this idea that they are a data feed company. And now we're seeing they've built this Nielsen market in the cloud and combined InfoSum and other other solutions so that it becomes a feed into a variety of systems that are all going to be wrapped around AI. That looks more like subscription, it's recurring revenues. I mean, it's a great model. And that's similar to real time, right?

Karen Lynch: Yeah, real time. And yeah, so before we move on to talk about the next point, let's just stay with this a minute, because I think there's something to be noted for everybody listening, like, you know, Lenny and I connecting these dots for you, something to be noted about real time insights being realized through these providers. And therefore that is your new company, if you are in that world, and you have surveys that are taking some time to put out there, you are competing with people who can deliver it and deliver immediate results to you. If these companies, others like it, like that's, that is your competition. Your competition is delivering immediacy in context feedback. Right. And always on.

Lenny Murphy: And so I think that that's, you know, as we position this overall, that the insights world, which is new, we're talking about it forever, but now we can live in a world of always-on intelligence. And then immediacy of the data is a huge component of that. Are we gonna be able to do that with everything? No, right? We're not. There's some things that's gonna take a little bit longer to get to. But a ton is and can be a real-time feed into an always-on system and that feeds the synthetic, right? You go there first. Okay, we got all this data, now let's model this. And okay, then it just changes the research process. We are not the keepers of information anymore. So we are spokes on a wheel. And to your point, we are not the only ones who spoke. So. Yeah, yeah.

Karen Lynch: It's a whole lot of spokes.

Lenny Murphy: It's a whole lot of spokes, you know? Hopefully you put cards, Anyway, sorry, I'm dating myself. It's like cards on the spokes to make the noise. All right, so.

Karen Lynch: Have you ever done that? That was so funny, because the first time I was with you, and I'm like, what is he talking about? And then you're like, to make the noise. And then I'm like, oh, that's what he means. Anyway, literally riding bikes with cards. Yeah, didn't you?

Lenny Murphy: I had on my Huffy. I remember I had this. I had that. Did you on your Schwinn? Did you have a Schwinn or a Huffy? What did you have?

Karen Lynch: No, but I did have the bell with the little plastic things coming out of the handlebars.

Lenny Murphy: And I was like, Oh, my God, I'm going to have to do this.

Karen Lynch: I'm going to have to do this. I'm going to have to do this.

Lenny Murphy: I'm going to have to do this. I'm going to have to do this.

Karen Lynch: I'm going to have to do this.

Lenny Murphy: I'm going to have to do this.

Karen Lynch: Lenny and I were, we would not have been in the same circle at that time of our lives. Oh, everybody.

Karen Lynch: Nevermind.

Lenny Murphy: I was going to say something's probably inappropriate.

Karen Lynch: I know we date ourselves. We need to dial it back. And, you know, I want to talk about this next story before we move on to some of the big tech stories of the week, but, um, e-scout partner, um, partnering with responding to launch partner panels, uh, expanding access to global participants. You know, I have used de-scout. Back in the day when I was executing, D-Scout was a great platform for that. But what I thought was really interesting about this, and it wasn't overtly said in this release, but where my mind went is everybody's got to be coming up with their solution to the data quality situation in the pipeline, right? Now, D-Scout has proprietary scouts. So they have a solution, but they're forging this other partnership to, again, expand what they can offer and how their panel can be used and vice versa. So I just think this is probably going to be the first of many of these where people are like, okay, I trust you. Let's align, let's combine forces in the world of panels. And that's how I interpreted that. I thought it was just worth calling out for that reason. We're going to see more of this. And I think it's a good thing because people are taking the quality of their panels seriously.

Lenny Murphy: I 100% agree, and so no one good high quality panel is big enough to handle all the needs, right? The, so when you look at these types of partnerships, it's the, you know, it allows you to start getting scale through kind of a marketplace functionality, which we would argue that, you know, the marketplaces have done for years, but it was more programmatic. These are handles. So you know, there's an engagement discount when you download an app, you know, the discount app, I think the respondent does as well. So it is just a different engagement level with the response. It's a different level of quality in terms of knowing, kind of, you know, know your customer. You know, there's, there's just different elements of that than the programmatic world, which is optimized for speed, efficiency and cost. Now we're optimizing for quality and scale through these types of relationships. So hats off to, uh, to both of them. Yeah. Yeah. Yes. Yes.

Karen Lynch: For sure. All right. Ready? We're about halfway through with our time here. We have to start talking.

Lenny Murphy: We are. Well, we're doing good.

Karen Lynch: We only got four things listed. Big kind of big picture tech stories. Um, um, and I, and I think that I, anyway, a lot of this is for future reading a lot of papers. Um, but, but, again, we'll just get into them. The first one is talking about, which I thought was interesting and worth reading, it's a study, right? I think you have found this one. Study about how the future might not just be these large models, but it might be smaller ones, faster trained, custom for specific uses. And that might become more practical as we move into more agentic AI. So a really interesting article here, study here. Your big takeaway, because I'll tell you mine, other than just what I just said.

Lenny Murphy: I mean, I think that was kind of it, that is removing two agents that are specialized in the research world. For brand, let's call it the PNG agentic, just because I always pick on PNG. The, the, the PNG agent that's trained on PNG data that they have built. Through our proprietary data sets. That may be big because P&G is big, but it's not as big as everything else. So that level of specialization that's focused on their business applications makes an awful lot of sense. And I think we'll see more of that. Now that we've unlocked the agent capability, we can train it on more specialized stuff. And I think we've all seen some elements of that already. You and I have trained some elements of agents or AI on our individual profiles.

Karen Lynch: But those are not, and this is what, one of the things I wanted to say is that those are still large models underneath it, right? So what we've done, when you build a, for people listening, because I did a deep dive here because I was very curious about the tech. When we use a custom chat GPT or custom GPT and we create our own, Lenny and I are both playing around a lot with that. You're still relying on an L, there is still the power of an LLM behind it, whichever one you're using. But the future may be smaller ones And I, you know, again, I talked to OpenAI's chat GPT to try to understand this and find an example that I knew. And we ended up using Grammarly. Grammarly is a small language model, very specific case or use cases for, it doesn't have to be trained in the knowledge of the world. It has to be trained on grammatical knowledge. And that is one specific use of it. With that in mind, going back to your P&G, you know, P&G teasing, if it doesn't have to be trained. The LLM or the small language model that they may be building doesn't have to be trained on the world's knowledge. Right.

Lenny Murphy: It's just theirs. It's just theirs.

Karen Lynch: And I think that that's what's happening. So the same skills you and I are applying, but we're relying on the knowledge of the world. Right.

Lenny Murphy: Right. Well, but that was my point bringing up was an example of, you know, you can see that progression. Yeah. Of course, if we're doing these individualized applications based on the LLMs, right, the large ones, well, why can't you do the specialized ones? And obviously you can, and that's what that was about, which is also, yeah, I think it's at least thematically connected to the next one. So Microsoft, Google, all these startups, embedding agents in everything, but particularly spreadsheets and browsers. And AI-native productivity tools. So, you know, we used to build macros in Excel. Now, we would be building, some people did. Fair enough. Shout out to Nelson. The, but now, agents in those tools, and that's also, and what that, we've talked about this, this was the broader component of that. You think there's going to be somebody who's going to usurp the major tech players, you're wrong. I'm sorry, I just gotta tell you because they are staying at parity as well. So we think about this world. It's not that we are there, maybe we have open AI now, right? We got perplexed and there's some other broader ecosystem players in there.

Karen Lynch: Yeah.

Lenny Murphy: But if you think that you're that suddenly it's going to be that Google or Microsoft or Apple or Amazon are going to go away. You are, I'm sorry, I know you're smoking, but that's not gonna happen because they are fundamental to how businesses run. And here's an example of that where they're incorporating the good old spreadsheet. The good old spreadsheet is becoming agentic. And then you train that so you don't have the macro, particularly have an agent that's kind of trained on how you do things. And that's not the future, that's now. It's only gonna be us getting used to them.

Karen Lynch: It's interesting because there's, you know, there was, I think it was at some point in the last year or so, whatever, like where you could be in your email and then open up a sidebar and be able to like schedule a meeting right from your email, which is kind of like having that agency feature in there. Like, you know, those two productivity tools are linked. The calendar in your email is linked. But now, you know, you can, It's not going to even be an effort. It's the prompt. It's just going to happen.

Lenny Murphy: Yeah, right. I mean, it's just you set it up and it's a prompt and then it's running all the time. I have not gotten that far myself. You know, I'm still a point and click GUI interface, you know, but I see it. I just haven't. And that's the barrier. That is the barrier. The adoption is, you know, old fogies like me that just I'm setting my ways, you Move the button, where's my button? And that's not insignificant. But here's what's interesting about this, and I think it's worth differentiating.

Karen Lynch: When Lenny and I segue into these types of stories, this is changing, these types of changes, Microsoft's the Google's the Amazon's whatever embedding these things into their platforms that we use for work This is changing how we work how we as professionals work and that's very different from how Researchers conduct research. So some of the stuff that we talk about like, you know, the first half of the show. It's all talking about what does this mean for you as a researcher? This stuff starts to be what does this mean for you as a professional doing business?

Lenny Murphy: Like, you know professional I mean just because these things are baked into our lives my kids You know the Right. I mean these are foundational. Yeah to just Navigate the world. Yeah, you know today and they continue to to transform so but my recommendation on this for everybody listening is you can't. Everything's changing really fast in so many ways some things don't, right? It's kind of like the law of physics, right? I mean, physics still applies. We may come up with some grand new propulsion system for interstellar travel, right? That may happen, but it's still gonna be based on physics. So that's not gonna change. So focus on the integrations and the evolution of the solutions that are foundational. That's the path to success, in my humble opinion.

Karen Lynch: And say yes, when whatever tool you use, whichever system you're in, whether it's you know, the Google system or whether it's Microsoft, when you get that pop-up that says, you want to try this, try it, experiment, see what it can do for you. Because this is going to improve your workday.

Lenny Murphy: Absolutely. And I look forward to the day I get over looking for my button to realize that, oh, why have I not, why am I still messing with that button? That was stupid. I need to just use the agent.

Karen Lynch: So, yeah, let's get into this next one. Cause I thought this was really cool because so many Green Book customers were mentioned in this article. And I was like, it was like, oh, okay, we know all of these players. Interesting. So Foundation Capital has a piece out highlighting how AI agents will redefine user research. And I mean, what in the beginning, you know, throw out Sint, throw out Qualtrics, like there's some, you know, some of the heavy hitters that have been around a while, but they also called out startups that are also customers of ours, listen labs, a competition winner. We knew, you know, we've talked about their funding outset, conveyor and how, and talking about how they're all running kind of the full interview loop, right? So recruiting, scheduling, conducting. So they're doing it all, right? These startup platforms go back up to MX eight, like MX eight, you're not the first one to be doing this, right? Right.

Lenny Murphy: This was cool. It was a VC, early stage, which by the way, in that, if you're an early stage company, they were saying, hey, entrepreneurs, founders, come, we wanna talk to you.

Karen Lynch: So that was- Yeah, we wanted to just scroll to the bottom, that's in the bottom call to action.

Lenny Murphy: Yeah, so that was cool, just from that standpoint. They're obviously engaged in the ecosystem. And of course, there's bias, because they have a thesis, but you don't make money in the VC world unless you're really smart, don't, um, the, uh, uh, or I should just throw money away. And, uh, so these guys, they have a very specific view of the future of the industry. I'm not going to disagree with them. It may be a little more bullish than, uh, than I'd like to admit publicly, but the, uh, but they're, they're still not wrong. And, um, and they're putting their money where their mouth is. And these businesses are achieving commercial success and you know, You got to put your thumb, your finger in the air and see which way the wind is blowing. This is very indicative of where the wind is blowing. And it's the second one, because we also had the Andreessen Horowitz post a few weeks back.

Karen Lynch: It was very similar. So follow the money, guys. It's important. Follow the money. So yeah. And also, just shout out to everybody who's cited in this, because it's pretty cool, and hopefully readers will be taking you all in, like we did. And listeners of this show will take that article in. So appreciate that. Yep.

Lenny Murphy: I thought this last one was really cool, too. It was also one of those things we've been talking about. And now here's other people going, oh, yeah. Yeah. Yeah. You go ahead and share this one.

Karen Lynch: I pulled it up. I'm like, I really want to dig into this one. So it's still in my Read This Next. But you have to hear about it. Because I didn't get there, but I really want to.

Lenny Murphy: Just rest assured. Look at the kind of macro which I'm not. I'm actually not familiar with the rest of the world as it seems to be a tech centric publication, but anyway, basically how the next generation of AI is behavioral data and not just a kind of Internet of Things. That's a piece of it 100% but also transactional traffic, etc, etc. And that's, look at the relationships that the big AI companies are doing, particularly in emerging markets, with partners, because they just want the data. So one example, and I forget the exact details, was OpenAI, I believe, giving free access to a large social network in India. It's not because they're just these wonderfully virtuous people. They want the data. So they want, there's solutions integrated in so they could form the models. And we've been talking about this for years as well. Now that the behavioral data, attitude data is useful. Behavioral data is far more useful because it has so many applications. And you take that even a step further, the Waymo and the robots and they're capturing real world data. And so we're and this integration of just gobbling up real data from every possible source, because that's what feeds the AI and therefore that's what feeds the business applications that are being developed off of it. So yeah, that was the gist of that. Yeah, very cool.

Karen Lynch: Very cool. So I mean, look, there's still a lot going on, and I just hope that Lenny and I are delivering it to you in a way where you can digest it, take it all in, and rest assured that this is what's happening. We're here to translate.

Lenny Murphy: Sorry, do we want to give them a little sneak peek on our 100th episode experiment? Because I thought that was really, really cool, what we did.

Karen Lynch: Well, I think that the reality is Lenny and I have been at this for about two years. So that means if you add up how many weeks are there in a year, give or take a week or two where we've canceled, we're coming up on our 100th episode. So that's going to be, what was this one? And I don't want to call Karley in because I'm like, oh my gosh, was this one 98? Was this one 97? 98. This one's 98. So we'll have next week. But then the week after, it's our 100th episode. We'll be sharing some of what the themes have been over the last 100 episodes. First of all, it's like we're becoming outliers in the number of times we have talked one-on-one. We're already at 100 episodes. I wonder what our outlier episode is for us. But we'll be sharing some of the themes that the large language models have helped us uncover that we've covered in the last, 100 episodes. It's pretty cool, actually. Really, really was.

Lenny Murphy: I mean, we'll talk about it more then, but we just know we've been experimenting. We put our money where our mouth is, guys, right? And we've been analyzing, utilizing AI to help analyze what is inherently unstructured data. The transcripts and the sessions and the links and looking at what are the themes and pretty, pretty interesting for sure. And we're not exactly sure what we're going to do But we'll, we'll talk about it more, but

 Karen Lynch: Well, and I do, you know, shout out, shout out to, um, shout out to my husband, Tim Lynch, who, uh, started to do some work on his own because everything's on YouTube and he ended up creating, I haven't even shared it with Lenny yet. I don't know what I'm going to do with this, but he ended up doing like an AI video recap of our episodes and of all of all hundred. I'm like, I did not ask you to do this. You are busy. Don't put time into this. And he's like, Hey, I'm just a fan doing what I do with publicly available information.

Lenny Murphy: We love you, Tim. Well, but it was important when looking at that. I just got to say, guys, I don't want to give anything away, but it was compelling. If you've been, that was our goal. Our goal has always been to share information that we think is interesting and relevant to the industry that can help you make better decisions going forward. And taking that retrospective, It proves to me of, well, damn right. We've been doing that, right? We, we have been, we have been a real time tracker of, of significant issues, uh, in news relevant to the industry that has become predictive over time. And that's not a shout out like, Oh, we're so smart.

Karen Lynch: The point is we're like, we, we speak for ourselves.

Lenny Murphy: But this, the reason we do this is ingesting information to share. And, now we have the ability to look at that. And, uh, in a very, I mean, it was easy to do it really, uh, that shows. Pay attention because these things that we've been talking about have been relevant, impactful and real and continue to play an important role in the future. Out that way. Again, not because we're so special. We just, that's why we do this. And it was gratifying to see that there was actually, you know, okay, what we're doing has been impactful, I think. Yeah. Yeah. So, so that'll be, that'll be a special episode.

Karen Lynch: So we better not have any other news that week.

Lenny Murphy: Oh, I'm sure that's when the aliens are going to land. I'm telling you.

Karen Lynch: No, the good news is the hundredth episode is the Friday after Labor Day weekend. So we expect quiet news that week. So, you know, because people are like, it's Labor Day weekend here in the US, we're not going to be doing too much. So we expect to be able to, you know, share some of this good stuff and not miss out too much on the news so we don't miss a beat.

Lenny Murphy: And we stay contextually relevant.

Karen Lynch: Yeah. And on that note, we will see you all in a week. If you want to message us at theexchangeatgreenbook.org in advance of that, feel free. You know, we're looking at it every day. So TheExchange at greenbook.org. If we don't see you there, we will see you next Friday. Here's what I always like to do. I like to click on this and show it. Just because when Karley puts it there, I'm always like, look. Email us, keep the conversation going. We'll see you next Friday. Have a great weekend.

Lenny Murphy: That's it. Bye, everybody. Take care.

Karen Lynch: Bye.

Lenny Murphy: Bye.

Links from the episode:

Thrive doubles down on Databricks, leading Series K at $100B valuation 

Social video insight business Dig raises Series A investment 

MX8 Labs Launches With A Plan To Speed Up The Survey-Based Research Biz 

YouGov and Metricomm partner on a new tool which accurately quantifies the impact of media coverage on brand health 

NMC Data Added to InfoSum 

Dscout Launches Partner Panels with Respondent to Expand Access to Global Research Participants 

Small Language Models are the Future of Agentic AI 

AI Agents Inside Spreadsheets 

How AI agents will redefine market research 

AI giants race to scoop up elusive real-world data