AI Agents and the Future of Revenue Ops with People.ai

and it's an important part of the selling process. It's still [music] peopleto people, right? Um what what's not helpful and not useful is sending time spending [music] time entering information into fields, you know, double-checking information, moving data around, analyzing data, all of these types of things. And so the version [music] of two is that sort stuff should just be handled by the software and the AI. And you know, [music] systems are going to talk to themselves and that's going to take us out of the tabs and out in front of customers. >> Hi, my name is Demetri Bonichi and I'm a content creator, agency owner and [music] AI enthusiast. You're listening to the AI agents podcast brought to you by Jot Form and featuring our very own CEO and founder Idkin Tank. This is the show where artificial intelligence meets innovation, productivity, and the tools

shaping the future of work. Enjoy the show. Hello and welcome back to another episode of the AI Agents podcast. In this episode today, we have Jason Ambrose who's the CEO of People AI. How you doing today, Jason? >> Pretty good, Demetri. Glad to be here. >> Yeah, glad to have you. I'm really excited to uh chat a little bit more about what you got uh going on there at People AI. But before we kind of get into that, obviously the world of AI is an interesting landscape. A lot of people have a cool background of getting into it, their experiences with uh how in general they felt like the um AI world kind of came to them. What was your uh kind of story behind how you got into AI in the first place? >> Yeah, it's an interesting question. So, so I've spent a

lot of time in, you know, sort of the rev tech go to market world, particularly in the world of CRM. And I think in that foundation of seeing how people used that product and the premise of people having to manually enter information and try to find answers, I always felt like it should be a lot better and it wasn't improving. So uh then as I saw AI start to hit and saw the possibilities that's what really drew me to the space and to people AI in particular is thinking about gosh AI could do so much more for us to make us productive in the sales organization and I had some exposure before that when I was in the world of fintech but uh that that was I think the the primary driver for me to to be interested get involved that you know a lot

of the things that have been lingering for the past 15 years in rev and CRM um or things that we can finally solve with AI. >> Yeah, I would love to kind of hear a little bit more about what uh RevTech is specifically. I I think I have a pretty good grasp on it, but I maybe some of the audience doesn't. >> I think it depends on who you ask and how you choose to interpret it. >> Okay. What were you doing in Revtech then? What was what was your Yeah, let's just talk about you. So um I guess to me you know rev is everything around you know supporting the go to market motion right so there's probably different categories and uh maybe there's some purists who keep me more honest but you know there's sort of the middle office stuff of CPQ and

how do you get deals done and moving orders through the system but CRM at its core if you think of that as a core platform is how do we make sure that we understand what's happening with our deals and our accounts uh and how do we move that through the process and involve the sellers and everybody who supports them to do that. So, if I think about that as the the anchor to Revtech, because that's ultimately what's generating the sales that turn into revenue, then there's a lot of different niches that could hang off off of it. And the marketing side, the top of the funnel, the bottom of the funnel, uh, post deal support, whatever else. But, uh, to me, I think it anchors around CRM as the core platform for selling and growing revenue. >> Got it. Okay. So, that was like how

you first got that experience in there. Okay. Yeah. cuz tech I feels like tech kind of made this natural transition to AI. Anyone who's in the space kind of you know adjusted. I mean I I was working in tech then I started working I was doing content for tech now I'm doing content for AI and like just completely >> kind of moved in that direction. So then how did you end up at people AI? >> Yeah. So I got connected to Oleg our founder uh last year. Um it was fun coincidence that several people that I really like that I worked with were working here and and they said you should really talk to this guy and because of my background and interest in the space and for the reasons that I said I was I was really interested in this company and this technology.

So I worked quite a bit with Oleg on the strategy and looking at how much the space was changing and how that was creating opportunities for us and then it just naturally evolved that there was an opportunity to come in and fill in for him. As I mentioned, there was just an incredible opportunity for him related to his home country in Ukraine and felt like a natural transition for us to for me to step into the role given how long I'd been working with him on the strategy, how aligned we were on that and our shared view of where I was going to take the space and what the opportunity was for us at People AI. >> Okay. And what what do you think is uh you know I got taglines here all over the place with the website but what do you use as

a phrase to describe what people AI does in in a sentence or two? >> Yeah. So we're we're we're focused on this idea of how to generate answers, right? So, if you think about almost everything that we do in sales starts with the goal, whether it's the number for the quarter or it's a meeting or it's an email that you're sending and you may have a set of questions of how do I how do I get to that goal? Finding those answers can sometimes be really difficult and it needs to draw AI needs to draw from context to do that. Once you have those answers, then you go take actions and you have a set of outcomes. Now a lot of the energy in the space is around automating that back half of how do I take those answers? How do I write and send

the email for you? You know those types of questions and maybe how do we track the outcomes. But the answers it tend turns out to be a really hard problem if you look beyond the numbers and into what's the activity and what's actually happening in the interactions with customers. So our job is to provide those answers based on how your sales team is interacting with your customers that help you through that at all levels of activity. >> So yeah >> um extending it a little bit right historic probably the next question is okay how did we get there and what's been our evolution. So we spent a lot of time around activity capture and providing AI that matches that to the CRM records, right? So in CRM usually, you know, you have these opportunity objects and you're trying to track what happened in a meeting,

what happened in a phone call. Postcoid that got a lot easier because everything was happening on Zoom and our evolution has been to work off of that foundation to add our own AI on top of it and surface that through our APIs or MCP connection or some of our products to provide those answers. So, it's probably a little longer than uh the tagline you were looking for, but I think it helps. >> No, that was a good explanation. You know, it's a it's a it's a primer to force you to try to say it in as least amount as possible, but you did good. Um the question I would have then is what are kind of the main customers that you end up working with? Is it like I I say sales leaders, sellers, uh revops, what is like a common relationship look like between

you and your customers and how they find you and that sort of thing. Like what are they struggling with? And >> yeah, it's really it's so we think about the where you are in your journey with AI, right? So I think we we probably all feel like the middle of the curve is this experimentation phase, right? And then the more mature folks like Red Hat, one of our customers is well down the path of how they're adopting agents and they're adopting an AI in almost everything that they do. Uh and then there are some people who are really just trying to get to experimentation and figure out what they want to do. We tend to have customers who are sort of in the middle or moving to that later phase of okay, we understand what AI does and we have some ideas of how how

we want to do it and we need to make this real. And when they have the experience of trying to do that without us, without a platform that provides the answers that we do, uh they're the folks who get it and say, "Okay, we get it. We can't just throw LLMs at our, you know, our emails and our calendar meetings and have that work. We need something else." Uh but I think what's interesting to me is we have a very wide gamut of you know we're selling to AI companies people as forward as you can imagine big names that are out there to you know an Iron Mountain that you know does document destruction and digital data services that you wouldn't think of as being AI forward or on this journey but they are. So that's been surprising to me the mix of traditional businesses

with you know probably the most forward AI thinking businesses. >> Yeah, traditional is a is an area that I think could benefit from this a lot uh this upcoming year is um I think a great opportunity for every business but traditional businesses as well. Um it just seems to me that >> we all assumed knowledge work type stuff um immediately had the biggest opportunities to be time saved. So that meant like you know tech companies and knowledge work focused companies but traditional companies as well still need those things solved. >> Yeah. So like manufacturing we see a lot of like chip manufacturers. So I think folks who have you know sales processes that don't follow the traditional sales stages you know or or look like a SAS selling motion you know I think as a space we've we've sold to ourselves as a starting point

most of the time and so we've we've usually been the first adopters of new innovations that have come in technology related to go to market but because of like you said the way that some of these techn traditional companies sell and the fact that they haven't been up on the this curve and they're not doing a bunch of stuff digitally some ways that does prepare them to lean in harder and get more benefit faster from AI if they're prepared to move that fast which is I think what separates the the movers from the followers in a lot of these verticals. >> Yeah, I totally agree. Um what do you think? Obviously, we talked a little bit about some of the problems that you've solved, uh, you know, or that you solve for. What do you think is, um, kind of a key differentiating factor between

what you're doing and what other companies are attempting to do in the space? >> Yeah, I think it's two parts. One is we, especially now, we're really leaning into this notion of providing the answers where you want them, right? So, what I've noticed is a lot of customers want to get out of the wall garden idea, right? So companies that are trying to drive you into their user experience and force you to buy all the products underneath that and sort of lock in salespeople into that experience. Customers don't want that anymore, right? They're they're the build buy equation is changing the best of breed versus, you know, uh one-stop vendor shops. Uh that's also changing, right? So so they want openness. They want to be able to build their own agents or buy specialized AI that focuses in certain areas of their business and they

want on their own to figure out how that should come together for sellers. Right? I think the second piece is it's just the output the quality of the output of what we generate. It's hard work, right? So we have our own AI that we've built and it's it's it's very difficult to process a lot of this qualitative information and get answers that are actionable, right? So an example would be you know what's the risk on a sales deal that I have right you can take AI in a generic sense or with some of these platforms and it'll tell you something like okay you need to build trust with your stakeholders what does that mean what do I go do right but being able to process the information and say okay the CISO has concerns about GDPR and you know your ability to protect personal information

so you need to deliver them this content that explains our story of how we handle GDPR and then you need to triangulate with your business side so that the the business side makes sure that security is comfortable like that's an actionable set of answers right so we're we're we're moving toward aspiring to that quality not just to humans but agents because as you try to automate some of these higher level sales activities you need answers that are more qualitative and uh have deeper context than just like yes no answers. >> Yeah. Would you say that's something that uh I I think AI is at this stage where a lot of people are questioning its ability to do those qualitative answers versus just like basic generic tasks. What do you think kind of the future is in in your specific industry and what you're trying to

solve for with the capabilities of that improving and and where do you think it sits now uh maybe relative to where people would hope it is? >> [snorts] >> Yeah, I think the biggest thing is, as you said, is they're as they're coming out of this experimentation journey, they're they're learning the hey, it can't do everything, right? We're we're getting through that. Uh what what exactly should it do and what is it good at and therefore what is the need for having some of these answers. I I think our aspiration is at all levels of the organization they're getting quicker and faster action uh answers that are moving them into to actions that help them uh derisk and achieve better success. So um the agents will take a lot more of this low-level activity off the plates as you get lower in the organization. So

Matt the CEO at Red Hat talked about like this is what they're experiencing as he's planning automation gets down at the you know sort of individual contributor level and they're seeing a lot of benefit there but bringing reasoning in and chain of thought models becomes a lot more important higher in the organization because they're dealing with bigger and more abstract questions. So what those models need is the proper context to be able to reason through the questions they're being asked. Right? So we want to sort of take AI up from the let's write and send emails from you to the what's my plan for the year at a CEO or CRO level and what are the major risks that we need to address in doing that and so that end to end architecture moves from automation at at the lowest levels which is still important

and impactful to uh deeper strategic reasoning type questions and as these chain of thought models improve and as people get proficient with it that experience for executives and the kinds of questions and productivity that AI is generating will be higher level essentially. >> H yeah know that that that makes a lot of sense. It's it's interesting. Every different uh founder I or sorry, not founder, every different CEO person that's was running companies like these has um a unique perspective on where things are kind of at, especially with how their customers perceive um >> yeah, >> where things are at. And trying to bridge that knowledge gap between what is possible um in AI with their expectations and what you can provide is always an interesting uh little battle I feel like that you're fighting as a company like this. Yeah, sort of and to your

question on like where are the gaps, right? Um I think to that point, it's it's it's aligning the expectations, but then also thinking a bit differently about how fast you can move on some of this stuff. Uh and you know, I think right now there's a there's a pretty quick step to staying with sort of a human in the loop model and starting to experiment with this stuff. But there's still people who feel like we can automate away everything and we can hand it all over. And I think that disconnect is maybe stalling or slowing people down. And if they just focused on a more pragmatic approach to let's let's keep people involved. Let's let's try to work this through and get us all habituated to using AI at all levels. Like there's plenty to do there and plenty of benefit to get there with

that. Yeah, I think human in the loop is something that was uh you know talked about for a little bit. I do feel like there was a there was a level of like um for some reason pause in people understanding that that was kind of a requirement uh to to be the case for things as models continue to get better, right? >> You they they started to make bigger assumptions on >> complete follow-through capability. like it went from I think what happened maybe and you can maybe speak to this a little bit is that reasoning got really good so people were like oh look how smart the thing is. >> Yeah. >> And I think at a similar level just like with creative writing because this is still the case >> early on we're at like 85% of the way there with a lot of

things and then it would take someone to do 20%. So then when it got really good with reasoning the number of tasks expanded within that quote 80% of like there and now we're maybe closer to 85 90%ish with some with more tasks not all tasks but more tasks and people are now then assuming that since it's more capable that means that it's past that 90% to 100 so to speak Maybe. >> Yeah, it's an interesting idea. Part of what I heard and what you're saying is, you know, as it's had these capabilities, they're they're sort of drawing more things in that they're trying to solve and maybe those things are more complex and well, >> yes, >> maybe mentally assuming that they're getting to 90 or 100 on the stuff they were doing, they're adding new things that are sort of keeping you in that

>> 80 to 85%. Right. And >> yeah, exactly. I I think there's some truth to that in that as we go to these more complex tasks especially in an enterprise uh you know I think the easy the straightforward stuff that we first started using the LLMs on uh they could get good results because they're just finding the patterns from you know sort of public domain information and it's we're moving into more that requires proprietary context of what's happening in these enterprises that's where there needs to be a compliment. You know, we talk about this idea of expert agents, right? So, you have a first agent, could be Claude, could be chat GPT, and that's your sort of smart intern that it will go find out a bunch of stuff, but it doesn't really know much, you know, about how to work in your organization and

it needs the expert agents in different parts of the functions to tell it this is how we price, this is how we do deals, right? You know, this is how we generate leads or whatever else. And so to your point, as they as customers go to these more complex tasks, they need more support from those types of agents which either comes from humans staying in the loop and putting in more time or there's a different set of AI capabilities that has to provide that to that you know sort of gating agent at the front end of the process. >> Yeah, absolutely. the sort of director agents that we've seen um and orchestrator agents that we've seen um over the last >> few months because agents wasn't even a term like a year ago you know um [laughter] >> when I started this podcast a month

a year ago was when the term at first even had any >> conversational weight whatsoever and it was minimal because even I didn't even know what the heck I was talking about the topic because it was so new. Um, you know, I did want to kind of dive in a little bit more into some potential maybe clients you've worked with or stuff you've done. It seems like you've had some really cool and interesting success. I'm not wrong. You guys did raise a couple years ago like 100 million. Is that correct >> in funding? >> Yeah, it wasn't quite that much, but yeah, it was a couple years ago. We had a we had a pretty big round. Yes. >> Okay. Yeah. No, so you you guys have had had the the you know early portion of the AI world to or not early portion the

like early timeline, right? To build out something. Uh I'm very curious kind of like as this has evolved, right, as AI has continued to improve. >> Yeah. where you saw maybe some I know you you earlier on in the company but where you guys kind of tracked your progress as to like well we added this we added this we added this and how you've managed to kind of stay up with the trends of the models improving and capabilities enhancing >> so I have this sort of like fake brag joke of you know we were AI before it was cool right um >> it sounds like it [laughter] >> yeah well it creates a challenge right so you know Oleg was uh our founder was he was in the Y cominator class with Sam Elman so that he could see what was coming there and I

think even in those days he had a sense that this could be a possibility. The question was when and what would the timing be right and [snorts] to your point on the journey we started on this data element because we understood that capturing and gathering this activity a was you know a valuable seven eight even today years ago for humans who are still working within CRM and then the question would be what would be the progression for AI to take some of the act the the use of CRM that would move out of the system and what would that mean for our product. So we spent a lot of time on this data question which is a hard problem to solve. And then what we found is like okay it wasn't enough because the market was certainly wasn't ready for AI yet in in a

sort of front-end experience that could see the benefits of having better data but was that enough? And then so then we went into visualization of let's show the results like have them interact with the information in a way again still humans who are looking for these answers. Right? Then when the LLMs did come out, we started figuring out how to use them paired with our data and with our own models and AI to okay, now how do we distill this information into a set of answers that are useful still to humans, right? Um, and that was still I think the market was still trying to understand exactly what these LLM did. We embedded it in our product and in CRM. It led us to forecasting which is a you know sort of more direct application of this to say okay when you build a forecast

you want to know what the risks are and you want your platform to go bottom up on a dealbydeal basis to tell you what's happening there. So that was like a practical application that could bring it to life. The big unlock for us in the past year has been MCP, right? So even at the start of the year, you know, a lot of people couldn't spell MCP, let alone to your point around agents before understand what it really meant. But when we could bring that to life and have people use claw to ask questions that then ask its own set of questions to our platform to get really deep and meaningful answers. That's been the aha moment of it right now. And frankly for us, we're just in the transition to really lean into that. But there's some incredible stuff that we can do with

the product now that we're we're starting to see show up with our customers. So to your your point on the way that we work with customers. So Red Hat, you know, Matt uses it every day, right? He's he's got goose hooked up, which is a sort of open source version of an LLM, and he asks questions at his level to understand what's happening in the business. We've we've done things like um you know I've myself have taken our winwire form which basically says why did we win this deal fed it in and said go fill this out for the sales rep and it does an awesome job and that's just a tactical thing but these are all the kind of use cases that we're just now seeing that open up with agent to agent. >> Yeah. No, that's that's that's really cool. Um and let's

talk a little bit more about agents. Um >> when were you guys I guess when you guys are you know working through what you guys are doing just want to learn a little bit more how the product actually um fun functions itself. Um [snorts] how does the end user kind of interact with the agents that you have and where do they sit in the process because you know big question for everybody is how does uh how do I interact with the AI? Is it going to replace me at certain levels of the job so I don't have to do the work or is it going to replace me overall? Like just trying to learn a little bit more about that. >> Yeah, it gets back to the point of this is why we're opening it up because there's a lot of different ways that customers

want to uh be able to interact with our system. So let's start with, you know, humans versus agents, right? Two two different ways to ask questions and get answers, right? Right. So on the human side, you know, we embed that in places like CRM. So it's sitting alongside the record. So if you're looking at an opportunity in CRM, sort of a chat interface where you can say, okay, who are the key stakeholders in this deal and what's their position on our product? Things like that. So it's a way to get the, you know, the full story of what you're seeing in the records and that's in a different UX. We have our own user experience for looking at things like, you know, your overall deals. That's called Glass. And it's sort of like a simple interface, but the the the AI is tightly embedded with

that for people who want to start with the structured data and understand, you know, the the answers from the unstructured data that give them a better view of what that means. But we also do it through APIs. So you can build your own UX. You can you can surface it up wherever you want, right? So that's kind of the human side. The agentic side, first and foremost, MCP, right? So, so that agent can effectively ask questions of it in the same way that a human would in a chat interface. And it's really cool to see the >> chain of thought from >> Claude or the others that interacts with it to to get the true answer that you're asking at the at the start of the prompt, right? Um, we also have just APIs so they can talk directly or we can trigger workflows based

on the on the answers and the reasons come out. So that's a that's an interesting place where agents might ask questions of us that then prompt us to send triggers to workflows or the other way around. Uh, so a bit deeper in the sort of orchestration and automation area that you might find agents. >> Yeah, let's talk about MCP a little bit uh further. I'd love to hear more about that because that is to me one of the big unlocks for products like yourself uh in the last couple months. I totally agree and what I think is really compelling about it for us and our customers is you know if you start with this idea of a question and an answer you know we don't have to burn a bunch of roadmap to build the capabilities right it's just as simple as just go ask

it another set of questions and let it reason through how to go get those answers so I think if I step all the way back to like software and how we've thought about this is like we had to think about and understand the ways to use and build a user experience and workflows, you know, and and and really sort of explicitly codify it in our products. But now what changes is the things that I want as a user just start with a question and the AI's figure it out, right? The chain of thought on one side takes your one question into the right 40 questions or whatever to go ask our model, our platform and our platform is set up to respond to that. So it really opens up the way that uh you think about using AI because it's not a big roll out.

It's not a big new product skew or whatever else. It's the same thing. It's just you're sitting in cloud and asking these questions, right? And same thing for pointing agents to it, right? You can point whatever agents you want and they can ask their questions and get what they need through MCP and uh you know whatever the client might be. And the reason you might have a different client in MCP that is working through a different set of information to talk to our server or other servers, you know, it's setting up this world of um really, you know, deeper interactions between AI that don't have to be explicitly built and connected than the same way that you got to wire an API to something on the other end. You just show up at MCP. It asks its questions. It understands what type of questions it

can ask and off it goes. >> Yeah. No, that makes that makes me think a little bit more deeply on it. I I love the comment you made there about Claude and just interacting with it. example I gave on a previous interview is I um trying to help more people like yourself get on more podcasts uh with my experience in the space doing like a new PR service and I because I was receiving a lot of emails that felt like they were um uh you know for getting people on the show and I was like I wonder if how how well I could do that because I have I have great outreach capabilities with my AI centered mindset and I feel like the PR space is a little bit behind there to be to be a little honest. So, at first I asked an employee,

I was like, "You know what? Hey, could you go check out all the recent emails I've received from these PR agencies about potential guests and check out what they're doing, what they're offering service-wise, see how maybe there's a gap there between uh services provided and services that we could provide as well." And I I then 20 minutes later realized because this is around when MCP came out uh to be inside like for sorry for Gmail specifically inside of Claude. >> And I just went to myself and I said >> and I took what I asked him and just pasted it into Claude and it did it. And then I just laughed and I was like, "Hey man, I don't need you to do this. We're fine." like it would have taken him the whole day if he didn't use AI, right? But instead, it was

he parsed out the recent emails, it scraped their websites, it uh then just asked me, "Well, what are you providing service-wise? I told a couple different things." Like, >> it's crazy. If you literally change your mindset from let me voice note or let me talk to somebody to let me voice note to AI that's connected to the tools that have access to my information. become just completely different level of productive. It's wild. >> Totally. So, I had a really interesting walk when I first started doing this of, you know, we we we wanted to write this case study on a customer, right? And, you know, historically that's a lot of work is somebody has to go talk to the AI and the account team and find out everything that's happening and somebody sits and writes it down and it's a salesperson who's look is not

she's not getting paid to write book reports, right? She's getting paid to sell deals. So takes a long time to do and the team is like well this would be you know four weeks and while we were sitting there I was like it's just curious let me ask Claude and have a talk to our product and see what it can do built [snorts] out this beautiful case study I sent it to the [laughter] like that's exactly right right but then I took that and thought hm could I turn this into a post right and then so I asked it to look at my style of posts and then it drafted something that was pretty good right and then and I said, "Gosh, it feels like this would be a good story to share with a particular contact. So, see my email interactions with this contact

and draft something that's sort of appropriate in the flow based on this as source content." Right? Like it was just this walk through to your point all these different things that you do on a daily basis that if you habituate to just keep coming back and using the tool to do this stuff you find boy there's a lot of stuff that just happens faster and gets easier. Right. >> Yeah. It's very compelling when it manages to kind of work its magic on you. Like when you when you start to experience this properly, you just you're mind boggled, right? Like that like >> I really like it. I think it's a very positive um I think it's a very positive thing to experience uh and use. There are concerns that people have though, right? Um I'm curious kind of where you stand on this uh place

of, you know, what is AI going to kind of do to the market? Um, from a job perspective, people are in a couple of camps. Uh, there's three I've identified answer-wise that I've received. Um, one is doomsday, >> you know. Yeah, >> the that's the only way I could describe that. two is it's going to save us a lot of time uh at work to do more high quality thinking and actual work versus nonsensical click-clack BS. >> And then the third category is sure there will be lost jobs but then it will just make a bunch of very high focus solo entrepreneurs or small businesses with very niche good products. So the latter two are a little bit more on the optimistic standpoint. I fall somewhere between two and three. You know, some industries will just lay people off. Okay. Uh but maybe but I

think the people who have it within them will just be like, you know what, I'm going to make something specific and then the people remaining at the working places will just be doing much less grunt work type stuff. That's where I sit. What do you kind of think about how uh it's going to play out? So it's probably a ver a different version of two and three to an extent right so we talk about this internally as you know sort of leaning into our brand a little bit of you know we believe that people will work with people and AI does the rest right so especially in a salesation you're wired to spend time and talk to people that's what you like doing and it's an important part of the selling process it's still peopleto people right um But what's not helpful and not useful

is sending time spending time entering double-checking information, moving data around, analyzing data, all of these types of things. And so the version of two is that sort stuff should AI. And you know, systems are going to talk to themselves and that's going to take us out of the tabs and out in front of customers, right? Now the way that I think that that becomes a version of three is you know maybe there are some independence I think certain roles like anything that's like specialized content generation you know those types of things yeah you you could be super productive across a number of customers and they probably can't keep you busy you know forever right indefinitely. So does it make sense for you to be inhouse versus coming in on a project basis? probably more of the latter for some of those roles. Um, but the

the humanto human roles, maybe it's reasoning, but more communication and interaction like this is still a fundamental part of business, right? You know, even what we're doing here, right? This is you and me talking, right? Could we have a bot on either side of us? Maybe. But that feels, you know, Terminator 2 and really not that interesting, right? Like I think we're still social creatures and we want that interaction at all parts of the business cycle. So I see humans leaning into that stuff. It's rich context. The things that we're wired to do in terms of pattern recognition and understanding how people are communicating still very important hard hard to put in the hands of AI, but what AI is good at is information retrieval processing all that. >> Yeah, I think that's a that's a fair point. Like I'm just imagining even earlier in

my experience of a career that was still obviously very short. Um, it it's kind of like I'm imagining what it can be like in the future versus what it's like now. And I wouldn't like much to change practically outside of like nonsensical task be removed and something is kind of helping me do the little things um that I don't want to do click clockwise. And you know, yeah, like the Terminators type type stuff, I try to stick away from that in my head, you know, like I just don't I don't think that practically we're ever going to get into a world where it's going to be doomsday. Um, even from a work perspective, right? Like there's um one of my favorite what's the what's the name of it? uh in praise of idleness was an article written by Bertrren Russell in the uh 19 um

1919 I always forget it was it was essentially so 1932 it was immediately after World War I ended or a couple years after his entire premise or thesis was like the world is going to not work much just wait now the reason that he said this was because we just had had a entire world war and where half the workforce didn't exist and we're arbitrarily blowing ing each other up >> and he was basically saying like >> all this manufacturing was done. Look at all these automations. He uses the term a lot actually because of the fact that >> you know mechanical automation was occurring at that time. >> He's predicting this decrease in work. >> He's predicting this increase in leisure. That's why it's called in praise of idleness. >> Interesting. >> Yeah. He was a precursor to the Elon like it'll all be

great. We'll be on UBI and nobody has >> Yeah. No. Yeah. So this has been a this has been a predicted possible thing since the 1930s that was supposed to be done by now, right? So like this commentary that people are saying, right, because of AI and I'm not saying that it can't occur, I'm just putting this level of context of like well prior to knowing that we could interact with a computer, right? Like there's been stages of this, right? We went from mechanical to um the old version of computer which was like binary and then we had the mouse clicking computer. Then we got automations and APIs. Now we have AI. I'm like I don't know if anyone's going to properly predict the next tangental move where we will still find a way to have ourselves working 40 hours a week, right? It's been

this has been a thing for like hundreds of years at this point. If you go prior to the mechanical, it was the physical labor and then so anyways. Yeah, it's a really interesting. You're right. It's I think we lose sight of that. This is the next thing. I mean, you could probably sure there were thought processes that said, "Oh, when the fax machine is here, all these jobs were going to go away." I do remember this when the internet came out like, "Oh my gosh, like all this stuff is going to change." And I think jobs just change, right? And so I think to where we agree on this idea of more in two and three versus one uh that probably supports that to say look we've been through this cycle before maybe this time it's different right but seems unlikely because history tends to

repeat itself. >> Absolutely. Yeah. It's a it's a nice way to kind of put that into context as a positive spin because very quickly I have a lot of family, a lot of friends that are like, "Oh, you're working in AI, you know, like that." It's like, "What are you are you concerned?" And I'm like, "Guys, >> guys, like there you [snorts] still have banks that just got off of using servers for their trading platforms, managing billions of dollars. I do not want to hear this this like insanity that you think every company's going to magically adopt the full power of AI in the next like five years." It's like people are so they're so slow to adopt anyways. Like the people with the money are going to be late, which is funny. >> There's still physical paper moving the economy. >> My dad works

in finance and they just moved off of a serverbased system, like a physical serverbased system instead of a cloud for trading. >> Yeah. [snorts] >> Like [laughter] it's current still running on mainframes out there. Like you know, >> that's that's my point. Yeah. Like we're all the big money. Sure. big money in tech is going to probably be able to handle this transition quickly, but nobody else. [laughter] >> And those who do will will succeed and it'll go great, but it's not going to be everyone. >> Yeah. Yeah. >> Um I think part of what drives this is the pace of innovation is so fast and just the >> Yes. the experience of what comes out looks so impressive and compelling. That's what generates a lot of excitement. But at the same time, oh my gosh. But when you really start sitting with it and

and working with this output and you get a sense of the limitations and there's much harder work to get through to do some of these things, you can see, okay, the horizons are going to be a bit longer. the the value is going to be super high short-term, medium, longterm, but we're in a longer arc than it feels like when, you know, LLM have a brand new model every month and, you know, the league charts on DeepS versus OpenAI versus Anthropic feels like it shifts dramatically, you know, in the span of a few weeks, right? >> Yeah. Yeah. Absolutely. Well, uh, I have a question that's more on a personal note, um, in regards to your experience with AI. Um, what do you think is, um, for you? Actually, two questions. One, what's kind of got you most interested, uh, AI wise right now? I

know that it seems like there's a lot of stuff, um, that you can do with AI now on a daily basis, and everyone's focused on their little category. like what what to you when you're using AI on a daily basis gets you the most excited and then follow up. Is there a specific product that gets you the most excited that you you use all the time? I'm trying to ask all these like important people like what do you use? You know, cuz if you're AI first and you're a smart person, I feel like it's fair to hear. I've heard a lot of new ones every week that I ask this question. [snorts] >> Yeah. I I I wish I could say something that didn't didn't feel like uh I'm a bit of a homer on this question, but like I really am excited about what

we do with the product and I almost every day. So like I'll give an example, right? Because I I'll try to make this as generalized as possible in this because it really isn't a pitch. It's like what I personally believe, but like we were just talking with uh Kimberly, my head of marketing about events, right? And the problem with events is like how do you measure value all the time right? So you know you go to a trade show you have a webinar you know historically we've had this idea of influence revenue everybody feels like it's because you touched an opportunity right how do I know that like the real impact of that that's absent anything else but in what we do I can say okay I know this person from this forward I'll just pick an account right attended our event maybe it's a

webinar maybe it's an inerson event and we have some context of of what they were exposed to in that. Right? Now I can take that to our AI and say hey you know Jim Farley showed up at our events and talked about these things. What happened after that? Right? So I can see did he now show up and he was talking about our forecasting product or not? Did he introduce us to other people in an organization? Like how did this actually show up in our interactions with that customer? how do we think differently about measuring success of these events as opposed to you know basically the reason CFOs try to go for influence revenue is they always say what's the ROI when they don't understand the value of something right and now you can say look I can have qualitative contextual answers about what's happening

that really help change and solve a lot of problems that were difficult to solve before right and this is not just our product I think this idea with expert agents that build and deliver all this context, you know, from the proprietary information that's happening in an enterprise to solve some of these challenges with a front-end agent that's making it easier for you and I to ask questions, work through, you know, have something that supports us, get to a right set of questions that solves these things like all the ways that we can apply that that that basic framework of a proprietary expert agent that this deep understanding of a certain set of skills and the specifics of a company with these generalized capabilities at the top. Like that interaction to me is what's super exciting because I keep trying stuff and being surprised at how

well it works. >> Sure. Yeah. Now, outside of your own product, >> what's something you like something you like using? >> Yeah. Like I I think I'm still in the you know, it's just really fun to watch what's going on with the LLMs, right? And and that's So everything that's happening at the front end of that process, it's causing us to think about how does this redefine the the way that we all interact with technology. And this is a this is a interesting but also exciting uh way to think about your question. Right? So let's take a salesperson versus a CRO, right? the CRO really deals with unstructured like they they don't want to be sitting in dashboards. They don't want to do this stuff. They they do because they have to. They they just want to get quick like clawed is a very natural

experience for them and it's great. Like I can just ask a question. Maybe they need to get better at like understanding how to write the questions, but they don't need all this visualization because they're not having to process information. They never did and they don't want to, right? Um, but a seller does have to work with structured information and this could be true in different roles. And so what is the blend of what comes out of the LLM plus what comes out of structured quantitative data which we know the LLM are bad at. How do you blend those two together to make something that's really productive for people? I think as the LLMs and that front-end experience matures, you're going to see more uh blending of the user experience between interaction with structured data and you know sort of this chat interface that I think

is going to start to become a really exciting way for people to interact with information and data. And I think you're starting to see that of as they get into creating some of these other assets. still a long way to go on, you know, videos and presentations or whatever else, but where this is headed as this is my one-stop shop for everything that I need from information, like that's still exciting to me to stay on top of an experience and figure out how to apply and how I work in our product works. I think just the pace of innovation that these vendors are putting out is is really cool. >> Yeah, I agree. and all the little like nooks and crannies of capabilities that I feel like they're competing with each other on. Um, >> yeah, I mean I'm I'm kind of to your point

is like it's really fun to watch the arms race of like they're really >> like uh it's been fun to watch. I think Claude and Google have really had such a comeback um in in this well I mean Google the main comeback Claude like came out of you know the woodwork pretty strong but I think it's crazy that like um Google is so incredible when a lot of people had it down it out after I mean they had the wrong name first it was bar then it was Gemini first Gemini version was awful um Claude came out pretty strong was the best sounding writing wise GPT was leading for a while and then now like uh I don't know if you saw GPT 5.2 after GPT5 was a debacle GPT 5.2 is good at coding but the everyday use people were once again freaking out

asking a genuine question how did you manage to make uh 5.2 to worse than 40 >> for like daily task usage. >> Right now the advanced stuff it's really cool and you can make presentations and spreadsheets but just daily chatting with itm once again there's people are having the same freakout again >> as with five. So it's like they came out with 5.1 they fixed it and then Sam's is saying he's in code red and he releases 5.2. It's like it's that has been fun to me in my opinion. >> Yeah. I think it's, you know, we're we're we're watching the canvas get painted on a lot of these things, right? And so sometimes it's a little hard of, you know, you look at the pencil sketch and they haven't really inked anything in all the way and but you're expecting to see the finished

product um because they have to race and uh you know, uh show that they're getting stuff out there and they're keeping pace. So, you know, hey, maybe uh maybe the way to think about it is five and 5.2 are setting the stage for six or whatever they want to call it. That >> sure catch up, you know, >> who knows? But yeah, I mean, I think it's back to the point of like how fast the league how much the league tables change when somebody has a new release, right? To your point like Google earlier this year, nobody was even really talking about and now everybody's saying they're the top of the list, >> Yeah. I think people who are really in the no saw that the uh you know like the the issues were were actually not that bad for Google and they had made

big strides with two 2.5 but >> that what I was trying to point out is the sentiment drives so much of the narrative >> and with Google's five flop people are just distrusting at this point until they overcome it they will be distrusted so all right well this was uh actually longer than I expected but I appreciate the combo it was really fun man um last thing I'd just ask to close things out is where can people find um what you guys are doing? >> Yeah. Uh people.ai uh you know that's back to doing this before was cool. We've got a pretty easy uh URL to remember but um the website you can hit me up on LinkedIn too if uh you know you got specific questions but um yeah come find us there. >> Awesome. Well with that being said thank you so much

for listening to this episode. We appreciate your time and everyone, please make sure to check out everything Jason and People AI are doing at People AI. That is literally the most simple domain, people. They probably got it prior to when it was expensive. Thanks for watching and we'll see you in the next one. Bye.