B (2:58)

Yeah. And I should also flag that you're speaking, which I'm very excited about.

A (3:03)

Yes, I know. I can't wait to be back with you.

B (3:06)

So I've been organizing AI engineer summits for three years and usually they are kind of generalist. They focus on just whoever are the best speakers I can get and the general state of AI. And I think that now the meta is kind of shifting towards focus and concentration on a certain topic. Because when we have as many sort of applicants as we have, because it's like a, you know, you have to apply to get into this conference, we get to pick. And the best vibes are when everyone you run into is all concentrated on the same theme. You gather for a certain topic and even changing the name and focusing on a certain theme changes the entire vibe of the whole thing, which is very cool, very, very fun. This is something I realized as a meetup organizer. This is our first ever summit entirely focused on AI coding, and we're doing enterprise and individual contributor days as well. But I think the focus is on why coding has emerged as something that has particular product market fit and especially emerged this year. And it seems weird for me to say this as someone who's had a whole career in developer tools and kind of always focused on AI coding. We've never done this before, but I think this is the year most people don't even remember that cloud code only emerged in March this year and is now larger than $600 million business. And it was like after our last Summit in New York when you were emceeing. So a lot has changed. Cognition and cursor have emerged as very large startups. I can't even call them startups anymore. What we've been calling is agent labs that are starting to rival the model labs in terms of market pool, valuation, employees, what have you. And I think it's one of the most interesting stories of the year.

A (5:01)

Yeah, I mean, what's, what's fascinating about this is it is. I don't think anyone would disagree that this is, if not the dominant or most important AI theme of the year, it's certainly got to be among the top two. You know, and it was not on the radar as the thing that was going to drive all conversations. Know when everyone was doing their end of year content, you know, their end of 2024 into 2025 content predictions, no one, at least anyone that I saw was like, this is the year of coding. This is the year of A.I.

B (5:35)

Coding.

A (5:35)

A.I. coding agents. It was the A.I. the year of A.I. agents, broadly. Right. That was sort of like the money's on bet for what happened. The vibe coding. Only Karpathy said that tweet in February.

B (5:45)

Right.

A (5:45)

It's, it's, it, it feels like a million years ago because of the inevitability, but it really, you know, we, we are kind of just catching up with ourselves in some ways a little bit.

B (5:55)

And I actually also have a spicy thing because generally I agree with Andre and everything and most people do, but I think the one thing that is happening right now is that the software engineers are feeling very uncomfortable with vibe coding. And I think you talked about how we are six months ahead of Main street vive coding. I declare the end of vive coding being cool this month. And I think a lot of what we're meaning to discuss at AI Code Summit is what's after vibe coding. How can we avoid the slop and build software that we don't hate, don't get stuck in rabbit holes that the agents might go down sometimes. And it's going to take work from the model labs which we have represented. It's going to take work from the agents and it's going to take work from the customers, which we also want to hear from. So I think it's interesting because there's new terms and people VAI coding, super popular, but I think it also might need to evolve in some way.

A (6:57)

Yeah, well, so let's actually try to unpack this a little bit because this is sort of, to me, this was like, okay, Declaration like Sean's now in spicy mode for what's coming with this event. Right. I think the tweet was RIP vibe coding 2025-2025 or something like that, like perfectly constructed tweet. But so let's talk about what, where the, where the discomfort is coming from and maybe sort of like what the difference between what someone who's sort of excited about this term still is thinking about when they see it versus what this group of engineers who are getting more uncomfortable with when they, when they see that term, what they're kind of perceiving.

B (7:30)

Yeah, I think the issue comes with like every one of us, every software engineer is very happy that people who are non technical can get to somewhere productive without engineers. Engineers are expensive, they're hard to work with, they're divas, you know, whatever. Like just, just, you know, they don't need to help make your website your personal website when lovable and bold exist. And I think that nobody has any issue with that. I think it comes to a head when you start to say like, oh, I vibe coded this, like, come on, it only took me like an hour now here, take it and I expect the full thing by Friday. And like, well, you know, you haven't dealt with any of the hard stuff. You've only painted the sort of superficial picture and you confuse that for the full working app. That's one issue that is the sort of non technical to technical handoff that is not being discussed, negotiated. In fact what is happening is the infra layers are specializing for the non technical people so that the sort of Vibe coders, the non technical people, are basically building of a completely different stack than the technical ones. And so when you hand it off, you have to completely rebuild because it doesn't use any of the same tech. I mean, somewhat exaggerating, I think the best crossover tech right now is Supabase, which is why Supabase is doing so well. They've basically quadrupled valuation this year, but there's a lot of experimentation in just that front. Then there's also the inter software engineer fights where software engineers are also vibe coding, of course, but some of them are being a lot more sloppy than others. And the people who care about software, care about security, care about maintenance, care about honestly just getting things right or understanding your code so that you don't get into trouble because LLMs just do run into rabbit holes and sometimes to really get them out, you have to understand the code. You can't just, just sort of wash your hands off it. Or just flow based on vibes. So when that stuff happens and people are irresponsible, then they also tend to leave PRs to other people have to clean up. So I think people just want something better. A lot of people are talking about spec driven development as a way forward, which is something that Amazon is pushing a lot as well as a number of other people. My top speaker from World's Fair was Sean Grove from OpenAI who was basically pitching spectrum and development and model alignment specs. So I think there's a lot of action around this. The term that has to sort of replace or complement vibe coding hasn't emerged yet, but I can definitely feel it in the air. It's literally present in every conversation I have. Everyone's sick and tired of vibe coding.

A (10:22)

Yeah. So it's super interesting. A couple things. One, there's this classic pattern with change, technology change where we forget temporarily that the paradigm shift isn't going to be from a set of problems to an era of no problems. It's trading one set of problems for another which hopefully are a. It's a good trade off. It's a sufficiently good trade off that that new set of problems we'd rather deal with because of the gains that come from the switch. Right. And I think that that second part of the conversation that you were just mentioning, sort of the intra engineer conversation, is a lot about that. It's like, okay, well now we have to reconcile with, you know, all of the stuff that comes along with if we can do X, Y, Z much faster or automated or with background agents, it creates this new set of problems and we are still going to have to deal with those. We're going have to rearchitect our systems and sort of, you know, the way that we work to accommodate that. And I think that that's a very natural process of like figuring that out and actually sort of rationalizing what it looks like to use these systems well, even as the technology is changing. And I want to come back and kind of talk about maybe the Sync Async spectrum and a couple other things that you've talked about as it relates to kind of where these things are. The first one, you know, I was thinking about this. We really don't, we don't have a word for the difference between sort of professional and amateur in the context of a democratizing technology. Right. Like, you know, if you think about like I was trying to, trying to make the proxy of like content creation with social media, right. TikTok and cap cut come along and everyone can make videos. There's clearly a difference between amateur videos and Christopher Nolan. And no one would not acknowledge that. And in the middle it gets blurry, of course. And there's some people who may not be as technically good, but the things that they produce, people like more and you know, but there's still like, know the terms that we have are all, are all dumb, right? Creator, influencer. Like they just kind of, they don't actually convey this gap. And I think it's actually one, I think it's completely unsurprising to me that coding is sort of figuring this out first in the context of AI. You know, AI becomes this mass democratization technology. But there is still a difference between, to your point, like my sick terminal based, you know, AI daily brief website that I use lovable to maintain and like an actual product that goes out and you know, an enterprise is not going to freak out on because it's got, you know, kind of its security setup. You know, we just don't, we just don't have good, good terminology for that. Which I, which I think is a challenge because to your point, I don't think anyone is actually in disagreement that these things are different things.

B (12:54)

Yeah, I think to some extent it is our job to figure it out. Like this is not an unsolvable problem. And so I don't, I want to put people at ease here in terms of like keep, you know, keep, keep doing what you're doing. Keep, keep. Keep up with the bolts and, and lovables and bipoding in general. I think it is the job of the engineers to try to figure out that transition path because we haven't worked it out yet. I'm gathering people and trying to focus people's energies on this because clearly when a new technology emerges and it is somewhat disruptive to the old technology, people who are tied to the old technology complain, which is exactly what they're doing here, by the way. But also the goal is not to reject the new technology, is to embrace it and figure out how to reshape everything else in order to accommodate it. So I think like there's, there's more, there's more synergy here than like people fear when, when they, when they first hear about this stuff.

A (13:47)

Yeah, I wonder, I wonder if there's, I mean, you know, I don't know if it's an interim solution or not, but it feels like there is, there's a role or at least a function around sort of translating. You know, if you've got all of, especially if you Think inside an organization or a startup, you've got all these folks who are now able to speak with code. Right. Instead of talking about features they want, they can just, you know, mock them up, which is, you know, what we do, what every company I know at this does. You're talking about sort of the challenge of translation. It feels like that's, that's a thing that someone could get really good at, you know, both helping people sort of, you know, build things in the right way in the beginning. But anyways, there's, there's lot, lots of developments that I think are going to, going to come on that front.

B (14:22)

Yep.

A (14:23)

Okay, so the next thing I wanted to talk about, which is sort of, you know, builds off of this a little bit, is what this landscape of AI and agentic coding platforms, the full breadth of it. Now because part of the challenge and why sort of vive coding RIP I think is that like if you go back six months ago, it's like, who's going to win? Bolter Lovable. It's literally that. And then Claude code comes and it's like okay, now Claude, you know, as opposed to now people, people with a passing glance see Lovable Bolt Claude Code Codex cli Cognition Factory. And it is sort of this, you know, this broad spectrum and, and you actually wrote about this a little bit when, when you sort of shared that you were joining cognition. Huge congrats by the way. I think that's by the way from, for my money, maybe the most useful I'm making a career switch blog post that I've ever seen. Usually that's a very, very sort of self indulgent thing. It's just like, here's my trajectory. That was like kind of packed with interesting observations and one of them that you talked about is that this sync async spectrum. I would love, you know, without asking you to kind of boil the ocean, share kind of roughly how you see the topography of these, you know, of these categories of coding tools emerging right now.

B (15:35)

Yeah, you're making me think about other conversations I've had since that publication. But yeah, so totally. I think there are a number of charts that people have made. Basically coding agents are enormously popular now. We're just figuring out what the ideal interfaces for them are. Right. So probably initially started with GitHub Copilot, which is just spicy autocomplete as they say. Devin launched two years ago with the web app sort of interface Code interpreter is also in the mix. Somewhere in there where you can chat and it starts to generate code and run and execute that code, I would say. And then cursor, obviously, with Composer and all the other cursor agent stuff that they're launching. So I think now the form factors are you have the IDE or VS code extension, you have the web app, you have Slack or whatever your sort of team collaboration thing is. You might also want to put linear in there. And then finally you have the terminal, which is obviously the newest that emerged on the scene this year with cloud code. Basically you just need universal handoff among everything. And I think that's the goal. Everything I described, all the surface areas, all the companies pretty much have all of them now. I think with cloud code going to the web and Codex coming to the VS code extension, everyone's got everything. And I think that the handoff is not worked out yet. So cloud code is the first one to work out the hackiest possible version, which is cloud code teleport, where you can just dump the JSON of the chat and continue it in locally because they're the same instance, the same cloud code on both sides. But I think there may be some more evolutions from there because that's not naturally how we transfer context between engineers working differently. And so in my post I started talking about the sick Async spectrum, and you kind of need to own that. Which is why I was very impressed with Cognition buying Windsurf when Windsurf was out for grabs, because, well, here's the number two ide. It's for cheap because a month ago it was worth 3 billion, now it's worth less. The rumors are 300. I actually haven't even confirmed that that number. But yeah, I mean, at some point it's worth buying and actually you start to have a leg up in that sort of sync side of the spectrum while Async is having extreme product market fit. Right. I talked a little bit about the numbers in the Cognition blog post as well. So I think that's good. I think actually Syncasync might be a bad framing, which is really weird for me because one thing that's happening now that you're going to see with cursor 2.0 today, and also what Cognition is launching is that the Async side is moving faster rather than slower because I think there's been a perverse incentive to measure all these coding agents based on the number of hours worked. And where else do we do that? Well, lawyers and everyone and everything that we hate, because you're just incentivizing them to take more time, which is Horrible. No one actually wants that. We're just using that as a poor proxy for what difficulty of work you're actually doing. So everyone's working on faster agents, I think, which is good for users ultimately, because that's what we want in practice. The async side is becoming more synced and then the sync side is changing in terms of the mindset, right? Like why do you want synchronous code? Well, the actual answer is because not everything can be vibe coded. Like the anti vibe code is to turn your brain on instead of off and use AI to augment your skills and thinking rather than to replace it with scrolling Twitter. Right? So the sync mode is for the deepest focused and hardest problems where you need the centaur combination of human and AI. And so that's what I posted in the recent thing we shipped on SRI Grab, where we have the sort of async value of productivity, right? Like either you're super productive because you're in flow and you're focused and you're working on hard problems. If agents take longer, then you start to switch away and change context and lose context and then later on when you start to get more productive again because you are able to employ parallel async background agents on stuff that is really commodity and you can trust all of them to nail it.

A (20:02)

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B (22:34)

Yeah, I think memory and planning are always going to be huge. Context engineering is obviously a huge theme this year. And we have the guy who, one of the three people that coined context engineering speaking, Dex is a fantastic speaker, one of the top speakers at World's Fair. And I think then the other part is honestly just organizational transitions, which actually uniquely as a podcast you will cover, which is rare, which is more of a leadership topic. Sure, the AI exists, but how do you move an existing large organization to take advantage of it to upskill your team and maybe potentially reorg in order to capture the opportunities? I think this is one of those things where for the first time I'm able to feature people from Goldman Sachs and McKinsey and some of the top enterprises in the world. Northwestern Mutual and Bloomberg's coming back this year. There's just a lot of like, very interesting, especially east coast stories that I wanted to feature because a lot of tech is like very west coast centric. But there's a lot of good stuff happening in enterprises too.

A (23:41)

Yeah. On the organizational change piece, one of the things that I think is really interesting about and I think to me was reflective of just how dominant the AI coding theme has been this Year is when we started, you know, when we were kind of first doing some of these agent audits around the beginning of the year. It was very often the case that the engineering departments were surprisingly some of the holdouts. They were the sort of most intransigent around wanting to adopt new systems. And while I don't, while my perception is not that that's gone away entirely, it does feel like there has been a major shift over the course of the year, perhaps as the tools have gotten better, as the models have gotten better, as, you know, maybe our understanding of, you know, how to integrate these systems has gotten better. Certainly not universal, but we see less and less, you know, just, you know, over my dead body kind of engineering departments when it comes to some of these transitions.

B (24:36)

Yeah, totally. I think like, there's a lot of knowledge sharing in this kind of stuff, but it's also like not fully well mapped out. And honestly, I'm waiting to hear from you and you know, the rest of the speakers on the, on the leadership day to map out like the state of affairs and like what is working, what is not among the enterprises that you talk to.

A (24:56)

So speaking of that one term, you know, basically going back to what you were saying about vibe coding, it almost feels like part of the challenge is that it this, this same word or same phrase means different things to different people. Right? I think that context engineering is going to be a term that has a similar bifurcation or potential bifurcation in, in the year because context engineering is, is a very sort of like technical set of, of questions for engineers who are thinking about how to design systems that better interact with context. But it is also now a, a leadership or sort of a change mindset as people like basically sort of akin to prompt engineering for individuals where organizations are thinking about context engineering as how do we sort of organize our data, broadly speaking to be ready to be used by these systems. How do I think as I am prompting individually as a, as a sort of, you know, a frontline worker in a company, how am I making sure that I'm giving it enough work context? And it's not that that's obviously a totally separate thing, but you know, the one is not thinking about different ways for kind of technical methods for the LLM to access different information. It's more of a mindset shift, getting away from just strictly prompt engineering to making sure that your, your Claude skills are updated with all, all the things that they need. And I wouldn't be surprised if we see again there's sort of like the enterprise non technical conversation around context engineering which is going to be sort of like a very broad use of the context and a very broad use of the term engineering as opposed to maybe the more technical conversation.

B (26:28)

Cool. I don't have a view on that yet. That's something that you're picking up better than me. So I'm curious to learn more. Yeah, yeah, it's yeah, Flex shot.

A (26:36)

It's a prediction, not a, not a, not a fait accompli. So the last couple of things I wanted to ask you about, move back to the blog post that we were that I was mentioning the Devins in the details. The two things that I think really stood out to me. One was your kind of very simple 8020 sort of notion of code AGI. I'd love to just sort of like hear hear about that a little bit. So the quote is I'll quote yourself you so you don't have to quote yourself. But the line was, the central realization I had was this code AGI will be achieved in 20% of the time of full AGI and capture 80% of the value of AGI. So talk talk. I would love to hear just a little bit about kind of how you think about that. I think it will resonate even with my non technical audience just based on how much coding has shaped what we've all done with AI this year despite not being coders.

B (27:27)

Yeah, well I mean so I would say that there's a little bit of self cringe when I really boiled it down because obviously the world is never that simple but you have to think about the highest order bit and you have to think about concentrating your bets instead of spreading them out when it comes to power laws. And so 8020 Pareto principle framing is the way that I do it. Okay. And then the other irony is code AGI is, I don't know what's the word for self contradiction because if it's general, it should be general. It shouldn't be. Right. But all that aside, I think that the general sentiment is what I was trying to reflect which is literally value capture versus timelines. And I think those are the right two axes to really think about in terms of where to spend your time and where to invest maybe which are the same thing. You're investing your time or investing your money. And so I think one, I think the obvious statements are all listed in there which is how code is a verifiable domain. It's much faster. The people working on the code are also the people consuming the models so there's just a general virtuous cycle that is obvious in there and basically doesn't need any more elaboration. I think the interesting thing comparatively here is also the value side instead of just the timelines, which is obviously happening now a little bit. But you have to really. And for me to join a company that's worth 10 billion, what's the upside? Like 20? No, it has to be 100. And so I think you have to really think through is that even on the cards? And I think yeah, probably. And that's mostly because of the flexibility of code. I think the best way to communicate this is with how many people and how many times people have observed that you can use cloud code to do non coding tasks. Because it does generalize. It has the sandbox of tools. We used to in the chatbot era only do embeddings retrieval, but now we have agentic search and that basically requires a document library and all the things that people talk about in the modernized LMOS stack. For people who are interested in this, check out Jerry Lu's Talk from the 2025 World's Fair and he talks a little bit about the emerging stack here. And so I think that is probably the case where the things that we learn in coding agents basically generalize. And actually the people who work on coding agents first will find it faster because they already have. It's super obvious to me that they already seen it in some ways. Claude code is a new foundation for Claude itself. When people talk about the cloud platform or people talk about CLAUDE for finance or Excel, which was launched this week, it's all based on a foundation that was built with cloud code. So it's funny because I'm not even really putting my neck out on this thesis. I'm just calling it out as something that's already happening.

A (30:29)

Yeah, no, it's super interesting. Like I said, I think it's a fascinating way to look at things. And the last thing that I wanted to ask you about is, so I've said a number of times on the show, probably enough to start to annoy people, that I think two dominant themes heading into next year, at least for sort of like the business, the AI at work side of things. One is, I actually think is context engineering and just thinking broadly about what's the set of information that we need to provide, you know, whatever AI we're using for it to do better than just whatever it sort of can do out of the box, I think that's going to be a massive theme. And I Think that part of why it's going to be a big theme is that by making it a theme, it gives organizations the license to do unfun very difficult things like, you know, big data projects that were, you know, less sexy than like coming, like coming to this year is like, what, what demonstration agent can I build? I think going into next year it's going to be more like, how do I get this entire house in order? And there's going to be, you know, sort of wind and wind at people's backs for that. So that's one, I think the other very obvious one is ROI and performance. I think it's easier said than done. But I think there's going to be a lot, a lot, a lot, a lot of discussion around, you know, how these AI and agentic systems are actually sort of impacting the world of work. You know, be it time savings, cost savings, new capabilities unlocked, I think that's going to be a major exploration. The third, which I'm just starting to sniff and, and so I'm not ready to sort of call it on that same level is I think that I see this conversation starting around the product era of AI and the emphasis on products in which AI is situated being the things that people are releasing and focusing on as opposed to the conversation just purely being, you know, how does this model compare to the one that was 0.5 before it? And you had. It was not this, this wasn't the conversation. But one of the things that you talked about was this sort of difference between agent labs and model labs. And I love that just that if you want to share that framework because I think it might, might have a stake in that, that larger conversation as well.

B (32:30)

Yeah, okay. There's, there's a lot in there. So first of all, products era is a broader thesis than ancient lab. I think product era is basically in VC termin application layer winning. Right. And definitely two years ago, application layer was very unsexy. People made fun of it. You're just writing GPT wrappers now. They're like $30 billion companies and 50x sales and Harvey and Cursor and all these guys are doing super well, abridged, open evidence, what have you. I think the product era has definitely happened, but the specific type of products that is doing super well is agents. So, so that's how I make that transition. I think as a product person, sometimes you can overthink it. And if you really just look at what the heck people are actually having PMF with, it's just agents replit Spent two years working on AI products and got nowhere. And then they build an agent and then suddenly they're at $300 million revenue. So it's kind of obvious. Just take it literally anywhere. Notion getting series of agents is very good for notion, all that stuff. Okay. The Agent Lab is a thesis that isn't quite fully worked out yet, but it's really just the case for building AI companies in a different way than has been in the past. Obviously I love coining things that are two words and I love the way that people start to organically adopt it, which is why I know this terminology is working because now people are saying it without even me being present in the room. The Agent Lab thesis, I'm going to pull up this guy's coverage of my post, which is really helpful is basically people shipping products first into the model first. A lot of AI companies in the past, they would just basically raise a bunch of money, announce they have a bunch of money, announce they have a bunch of cracked researchers, they buy a bunch of GPUs and then you don't hear from them for six months or a year and then they come out with like, oh, here's our model. You can't try it, but here's some interesting updates from our model. This exactly, by the way. I mean, I'll come right out and say it. When we launched 3 grep in cognition, I was like, oh, this is why magic with their 100 million token model never launched because their model lab and cognition is agent lab. Build the agent first and then build the model. And I think that was like a back to front theme that I think really starts to play well. It remains to be seen obviously, because I think the bitter lesson applies and scale in infrastructure and GPUs is king. How much of the relative value Agent Labs can capture with model labs. But I think that's really bifurcating and it's so weird. Yesterday OpenAI kind of proved my point. Did you watch the live stream from yesterday? Basically Sam was like, we're giving up on products we're building infra. We have ChatGPT, we have Sora, but that's about it. Everything else is third party. We're going to be a platform. You should make more money than us on our models. He said all this and I think to me, who has been watching OpenAI as long as you have, that's never been that clear. They always wanted to be.

A (35:50)

Yeah, totally. I think it's probably been not clear to them. I think they've been debating it Back and forth constantly.

B (35:56)

They hired a CEO of applications. That's curious because now they only have two. But there's going to be applications built on ChatGPT, but that's a different thing anyway, so I think now the swim lanes are very clear. You want to build AGI, go join a model lab. You want to build products that serve users and vertical domains, build an agent lab. And I think that's really what I'm seeing with the agent lab thesis. I think there's probably more to flesh out here on what a good agent lab looks like versus a bad one. But I'm pretty curious and I think that explains the entire differences between the vibes that you get from agent labs versus model labs.

A (36:38)

I think one of the interesting implications, maybe we'll explore this in the talk in a couple weeks, is it might force enterprise buyers to think a little bit differently. I think that it has felt for a while like you could effectively avoid pretty much all that's happening in the long tail and just deal with, you know, the, you know, the, the foundation model companies or maybe the one sort of like leading vertical player in your industry. Like if you're legal, like maybe you deal with Harvey or you know, if you're in medical, you do it, but like, but not, you know. One of the reasons that I don't have a ton of space on the show to cover as many of the cool new products as I'd like is so much of the audience is like, well, I can I just, if I use it in my personal life, great. But there's no universe in which that's coming in. And if it really is the case that the model companies decide that they really are going to be platforms and let, let the sort of, you know, the agent labs build the next set, I think you will have to see an expansion in just the procurement process, which is a very, very discreet part of the conversation, but an interesting one.

B (37:46)

Yeah, no different take on that. I think maybe the one hole in this thesis is maybe anthropic because they are really building out cloud code to be an agent lab within the model lab. And every model lab can easily build an agent lab, for sure. It is just a matter of resources and a matter of honestly, social pecking order. To be an applied AI engineer inside a model lab is low status. You're paid half what the researchers get paid, probably less if you're working on meta. I think that it's interesting how seriously the lab is taken and obviously there's a very, very wide variance, but typically Typically, and I speak to plenty of people in those roles. They are more like the four deployed engineers, but they're not involved in research. And the company clearly values research more. And that's just how it is.

A (38:34)

Well, Sean, awesome conversation. Could talk to you for hours, but excited for the event coming up in a few weeks. Thank you for hanging out. Keep, keep, keep telling us where the future is.

B (38:44)

Yeah, I'm excited for your talk. Do you want to preview your. What you, what are you going to talk about?

A (38:49)

The. I. I don't know yet. But what I do know is that I'd like it to be substantive as possible. So I don't know if you've seen, but I've got this thing live right now. ROI survey. Like I said, I think, I think that next year there's going to be so much conversation of roi. And this is like the kindergarten version of roi. It's literally like, tell us your top use case, which of these eight areas is sort of like the biggest area of benefit? Time save, cost save, whatever, and then give us your subjective rating, you know, of it, like how many hours per week or what. It's. It is so generic. But I still, you know, it's been live at the time of recording for like 36 hours. And we have, you know, 250 plus use cases that people have logged in and said, here's how it's benefiting me. And already that's such a different amount of information that, that we don't really have access to. So I'm hoping that there's something that's interesting there, maybe combined with some of the other readouts and learnings that we've had from Superintelligent. So it's not just me rambling, it's a little bit more data driven. But we'll see, we'll see what's ready by November 20th.

B (39:51)

Good. Yeah. The ROI of AI is a perennial topic, just like every other leadership thing. It's weird because I can just have the same schedule every year and it's totally different.

A (40:04)

Yeah.

B (40:04)

I mean, I hope we solved some things. We'll see. But human problems will always make new ones, you know, to replace the old ones. But, yeah, thanks. I'm really trying to wrap up.

A (40:14)

Yeah, thanks, John. I'll see you soon.

B (40:17)

See you soon.