Are Developers Becoming AI Managers?

Cris Ippolite is in San Francisco for AI Dev Day 2026, joined by Chris Moyer, Vince Menanno, Marcus Swift, and Kate Waldhauser for a live conference debrief. The conversation captures the mood coming out of the event: software development is changing fast, but the group is not buying a simple “developers are dead” story. Instead, they talk through a more nuanced shift where developers increasingly become managers, editors, architects, and reviewers of AI-generated work.

“Developer as manager” – is this a destination or just an awkward transitional phase? The panel compares today’s AI tooling to an early car: clearly transformative, but still missing some of the infrastructure, safety, polish, and shared expectations that would make it feel mature. That framing keeps the conversation grounded. Everyone can see the direction of travel, but the day-to-day reality is still early, uneven, and full of judgment calls.

    The discussion also keeps returning to what FileMaker developers already understand well: data structure, full-stack thinking, business logic, interface design, and the ability to see how information moves through a system. Those skills become more valuable, not less, when AI can generate code or suggest architectures. The group talks about learning by taking apart AI-generated work, much like earlier developers learned by dissecting HyperCard stacks or FileMaker examples.

    The conference itself seems optimistic about the future of software engineering, with panelists rating the outlook high rather than doom-filled. But the conversation does not ignore real concerns. The group talks about local models, hybrid cloud/edge architectures, latency, trust, cost, and the need for deterministic infrastructure around probabilistic models. “Responsible AI” is treated less as a special category and more as something that should become table stakes.

The takeaway is not that AI replaces the developer, but that it changes the developer’s leverage. (and then, eventually, I suppose, AI takes over the entire world)

The people who understand systems, data, users, and business processes are still needed; the tools are just giving them a much larger, stranger, faster team to manage.

Transcript

SPEAKER_04 0:00

Hey everybody, welcome to a very special edition of the podcast. Yes, we are in studio. Yes, we're live. And oh my God, we got a ton of guests here. This is gonna be so much fun. But the most important thing is we're in San Francisco, California, here reporting from AI Dev Day 2026. So first I want to introduce who we've got on the panel. We've got a couple of different rounds. We're gonna start off with a little conference debrief, but I'd like to introduce our guests for today. You're gonna recognize all these faces. First of all, Chris Moyer, you know him from AI News Fame. Uh glad, glad to be have you join us here. And Vince Manano, thank you for joining us as well, too, all the way out from Florida. Marcus Swift, also uh you're joining us here for uh from uh Nanaimo British Columbia of Canada. That's what I was wanting. There we go. All we are the international podcast here, actually, just so that we're clear. And then Kate Walthauser, who we of course uh did our last podcast together in the studio in Austin. So welcome to you from Austin. And so the reason that we're all together here today is because we all attended AI dev, not day, because it's actually two days now. Um, and this is the third conference that they that uh Deep Learning AI has put on uh by Andrew Ng. And I happen to think it's one of my favorite AI conferences. They're generally pretty hit or miss. Uh, this one I enjoyed quite a bit. The first time they went with two days, it they opened it up to a much uh bigger group uh down at Pier 48 in San Francisco. I think it's about three times bigger than the first one that was here a year ago, and even the one in uh New York. Um, so what we're gonna do today is kind of share everybody's perspectives on uh the sessions. We only went through uh one day of it so far, but I think there's some really enlightening things we want to bring it to the community and kind of tie in what maybe some of the relevance might be. So I think what we should start off with was the keynote that frankly I missed. So you guys, I will be the one learning here. So why don't we start off with the keynote? Um so the format of the keynote was uh like four or five, like it was just like a general session with multiple speakers. Uh yeah, yeah.

SPEAKER_00 1:53

And they all uh you know had their topic, they flowed into each other, but right in the middle, they had a panel of of four speakers. And so I'll back up. I I'm a developer, but I also run a number of businesses and I work a lot with the business community. And we've been hearing a lot about the fear of what AI is bringing to the table and what's going on and we're gonna talk about that today. Yeah, one of my big thoughts has always been, you know, whenever we've seen major tech evolutions, the the floor definitely rises, you know, and jobs get lost and things change. Um, but what we don't talk about right now, which is where I've been spending a lot of my time thinking, is the ceiling also rises. And so where is the ceiling rising? Where is this whole industry shifting to? So as business owners, we can start to prepare for that. We can start to find the opportunities and the problems we're gonna need to solve at that at that ceiling space. And so as I was listening to this panel, one of the things that really hit me was that doing is going to get automated.

SPEAKER_04 2:51

So, like we're talking about everything from coding, fingers on the keyboard, code.

SPEAKER_00 2:55

That's already starting, right?

SPEAKER_04 2:56

That that's done, not doing anymore, right? Right, exactly. And then, but like actual work, like knowledge work or office work or whatever it might be.

SPEAKER_00 3:03

I think that's the that's that's also going.

SPEAKER_04 3:05

Okay.

SPEAKER_00 3:06

And and then as robotics hit, even physical labor jobs are going to also start to be affected. So I really start to see over the next number of years, we're gonna see doing getting automated. So then the role of the of the human being in that space was what really hit me out of this panel was that we're the entry-level job is gonna be managing. Is going to be how do I, I'm gonna be given a problem set or a new worker is gonna come in and say, this is your problem to manage, not to get it done. You uh your job isn't to do the doing, your job is to manage either the the bots that are gonna be doing that piece and you make sure that they work, that they're coordinated, that they're getting the job done well, that you're defining the problem. Because one of the things we're learning right now is you can't outsource thinking. But you can outsource, but what I'm starting to conclude is we can outsource outsource doing. And I think we're gonna be able to do more and more of that. And so then as a business owner, it's it's how do we start to work with and prepare for our entry-level people and training them and getting them ready to be more managers? Because I think the entry level is gonna be managers.

SPEAKER_04 4:14

So there's the two tiers here. We've got managers. Um, so so uh doers become managers, correct? And then entry level, what's the opportunity for it?

SPEAKER_00 4:23

Well, entry levels become the managers. Managing is the entry level.

SPEAKER_02 4:26

So my question is what happens if you don't want to be a manager?

SPEAKER_00 4:30

Well, I my only response to that is what happens if you don't want to drive a car and you want to stay with a horse? There's still space for that. That becomes very, very specialized, doesn't it, right?

SPEAKER_02 4:41

Well, yeah, but see, what if we're not here's my here's my theory is what if we're not at the car yet? What if we're or what if we are? But what if we're at the car that they developed that didn't have windshield wipers, didn't have um, you know, didn't have you know all these things, you know, brakes. Yeah, brakes are important. There was no um there were no construction or what if or what if we're not? Like what if we're just somewhere in between? Like, I still like my philosophy is that we're still really, really early. So being a manager might be a stepping point to where we're headed, and it's it's where we are now.

SPEAKER_04 5:19

But is that what the thesis was in the keynote? Um, is that what they were saying?

SPEAKER_00 5:24

That uh No, this is certainly pieces that like shoes were dropping for me. I don't think that was their thrust. But it was like I was coming with this lens thinking about already where's that ceiling going? And I I want to agree. I don't think we're there yet. I think we are in some spaces. I think as developers and coders, we are more and more being pressed around should I be doing the coding anymore? Is that the right space for me? And more and more, it's gonna be the answer is gonna be no.

SPEAKER_04 5:51

Well, the the interesting thing about the timing of it, like so, Chris and Vince, like uh the the role that we all play is we're intermediaries between the technology and the cut and the customer, right? So are you guys already hearing from your customers? Is the tech there already? Like, is it should we jump in? I mean, first of all, are you getting inquiries? Actually, I'm very curious, Chris. How many of your customers are coming to you proactively asking about AI for starters?

SPEAKER_01 6:14

There's definitely interest. Uh Hedy and I had an interesting situation a few months ago, actually. Uh, another file maker developer in Michigan came to us because they were getting asked about AI interesting by their customer. I think they sell hot tubs or something like that. They had five stores and they had this whole laundry list of stuff like I wanted to do our 401k contributions and calculate payroll and stuff like that. I was like, that's all real deterministic stuff. Yeah, bad choice for this. But one of the that's important, by the way. Let's put a pin in that too. One of the use cases, though, was reviewing sales calls. You know, that's a lengthy process. There's hours of calls, and their provider actually had a thing where it could summarize the sales call and say, here's the points they hit, or do the analysis for them if they say make sure you mention the promotion or whatever it is that they wanted to do coaching on. And so there was one great use case. So we thought, well, our work here is done. We've told them you have deterministic stuff, hire your file maker developer to uh add some uh workflow logic in. And then just last week we talked to the same developer again and said they got open claws and stuck Mac Minnie's in all the stores and just YOLO'd the whole thing, didn't listen to a word of advice about it. Well, at least hopefully you're not culpable. You didn't sign anything, did you? But I don't know what to make of that. It's like open clause probably not the best fit for someone who's not technical and well doesn't know what they're doing, and they just did not care.

SPEAKER_04 7:36

As a bit of a T first of all, great framing for is AI there yet? It might be too there in some of those cases. And great framing for this episode, we're gonna finish off the last segment with some some hot open claw talk. Um, but um, and so if for anybody's that might not familiar, we'll get into defining what that is. But I think for the purpose of this conversation, that's the most like aggressive AI there is, basically, right? So when we talk about is AI there yet? Well, the combination of maybe not totally and it being completely in YOLO mode is probably quite dangerous.

SPEAKER_00 8:07

But Chris, this ties in as well to, you know, the owner's job was to manage the AI. They didn't do a good management job there, right? They didn't use the right tool to get the job done. And that's, I think, coming back to more and more of the skill set that we need to be learning and we need to be providing solutions for is that, you know, you're not supposed to use a sledgehammer when you need a hammer. You're not supposed to use a hammer when you need a sledgehammer. And and that becomes management tasks.

SPEAKER_04 8:36

But I think that's the interesting role that all of us here play is we're the ones that know the difference between the hammer and the sledgehammer, right? Um like Vince, it's are you having these conversations with your customers too? Are they coming to you proactively? And are you, do you find yourself responsible for determining like what AI can actually do?

SPEAKER_03 8:51

I mean, to some level, I'm dabbling in it and I'm learning it as everybody is, and that's growing very quickly. Uh, but there's a lot of other people on our teams that are just doing a lot more uh in that space. And so uh that's that's happening. There's there's definitely demand, there's definitely interest, etc. I think going back to the to the keynote or the or the panel discussion, it was interesting to see that it was kind of like uh you know, very positive oriented. It was eight, nine, and ten, like when they were gauging. What was the the question?

SPEAKER_01 9:24

The whole theme of the thing was what's the future of software engineering? Is it like is it dead or what do you think? Oh, and so for humans to be software engineers and so it was sort of a doom index, like on a scale of you know one to ten, with the ten being the future is bright and one being world doomed. What would you do? It was JSON actually was zero to ten, but it was it was very high, ranging from like seven to ten. Oh, really?

SPEAKER_00 9:49

Was the gamut of four panelists, was it? Well, and Replit CEO was ten out of ten. Yeah, yeah. The one who was who would so Replit is like automated coding. And he was like, absolutely AI engineers are our code software engineers are. Well, he was speaking to his book though, in fairness, right?

SPEAKER_04 10:07

I mean, he needs that to be true. And I'm not saying it's not. Um you know, Kate, you and I had a conversation the last time we did an episode together where we were talking about like the doom scale, like what you know, where you kind of put yourself on there. Let's switch it up a little bit. Where do you see software coding on as far as like done being 10? Is that what the framing was uh during the panel? Uh software is just done?

SPEAKER_00 10:29

No, the the higher was the the brightness of the future, zero being the future's doomed. Interesting. Ten being there's it's a really bright future. Oh, wow.

SPEAKER_04 10:39

For human engineering engineers. Let's talk about that. Yeah. Okay, where are you at on that?

SPEAKER_02 10:45

I mean, I think I think it's like it how we're we're changing how we define a software engineer. That's basically what they're meaning. And and to the point of the the panelists, and and I did this so so the way I went through the the conference today was I was I used Claude and I, you know, took some notes, uploaded you know, screenshots of the slides I was seeing, and then had Claude research each speaker, you know, where which company to work for, what could their angle possibly be? Because you realize anybody that's gonna go speak at a conference, they're gonna have an angle, right? So of course, thinking back that, oh yeah, all these people would rate it a high rating. Yeah, I mean that that how could if they said one, right? Well then opposition researchers.

SPEAKER_04 11:25

Opposition research is like that.

SPEAKER_02 11:27

So I think that there could be something that they're saying, yes, it's highly, you know, optimistic for software engineers, but I think software engineers is gonna mean something completely different, you know, even in a few years than what we think of a software engineer today. We're just you know, or maybe it's gonna take on a new turn. I mean, we do, we have uh context engineer prompt engineers, right? But but I I don't know, you know, because software is now like AI is software, like that's that's it, you know, in our current state, you know, we are.

SPEAKER_04 11:55

Marcus, uh so this conference is put on by Andrew Ng from uh Deep Learning AI amongst America. He's like basically a celebrity. Anytime you can't get through the exhibit area, it's because he's there and all the fans are taking pictures of him. Oh, for sure he did. But he also um has a very aggressive stance on, you know, when people ask him whether software, you should still learn software, like young people should still learn learn software. He says yes, emphatically, yes. Um, so what what's your take on that? Somebody knew right out of the, you know, right out of the box, should should learning uh software be a thing, a priority?

SPEAKER_00 12:30

Well, I'm gonna go around a little bit and then come back to what is saying, what, what you're what you're asking. Um what somebody said on the panel that um a junior developer is gonna get more more complex tasks than a junior would have got five years ago. Because they have the knowledge right at their fingertips, you can give them a more complex task.

SPEAKER_04 12:51

You don't have to worry about because they're because they are embellished by the tool sets, whatever harness they're using.

SPEAKER_00 12:57

And and so because of that, you can give them more complexity. And this has been my personal journey. I mean, you and I met a year and a bit ago, and I was like a neophyte in this, in this space, right? And and in a year and a half, I feel like I've come a long way. And and this is what they're talking about that you the the journey from junior to senior gets compressed because you can start to interact with these models that teach you so much. So do I I'm a better at languages now than I started a year and a half ago because of how much I'm looking at the code that that that the AI is generating. So I think you take that away. I couldn't write, I'd be really, really struggled to write a basic Python.

SPEAKER_01 13:38

You've gone through immersion, like immersion learning, like you would learn a language, you just go to that that country and and this concept reminds me of when I first got started in the computer industry, I was really interested in hypercard and I didn't know how to write hyper talk or all that stuff. And I just started taking apart example hypercard stacks that already existed, and I learned by deconstructing existing stuff. And I think there's a real opportunity for new software developers to do the same thing. Have the LLM build something for you and then pick it apart and say, now why does this work? And so for those uh people who are curious about you know what how why does the clock tick, kind of a thing, I think there's a real opportunity for them to learn by osmosis, as it were.

SPEAKER_00 14:22

What's been foundational for me though is the knowledge I brought from FileMaker because FileMaker teaches you how to how to think about data, how to structure data. You you as a FileMaker developer know the full stack and you can see that layer and how it interacts with the front end and how the front end you don't call it that in FileMaker, but but that's really what you're seeing. And so that ability to visualize the data has really translated well to now me writing in JavaScript and Python, right?

SPEAKER_04 14:50

Um there's no doubt. Um, I mean, I can share with you my observation from like the first couple of years of doing services is that we were just thinking, yeah, plug your data into the language model and amazing things will happen. And then we realize all it does is amplify bad data. Right. And one of the things, like you know, the file maker databases are sort of like the HTML code of databases, like they're very forgiving. Like you can have a lot of garbage in there and a lot of columns you never needed, or just kind of sort of forget about things and stack on top of it and it'll just still work. But it's not so forgiving when you get down when you're connecting language models to that.

SPEAKER_03 15:22

So I I think on some level, like as a file maker developer for many years, I think one thing that's really fascinating to me, when especially when we got started with 42U and you were part of that journey, right? You meet these people and they come and they get grav, they gravitate towards it because it's like, wow, it's multidisciplinary. It's like it, I get to do, I just I get to not just do code, but I get to interact with people. And it's like, so it's not for everybody. And so for some people, it's like, hey, just give me the inputs, I'll give you the outputs. That's uh, that's all I want, you know, like just slide the pizza under the door and and I don't have to worry about it to interact with people. But I think for some people, there's there's a there's a beauty in, you know, coming into work and you're working for uh Joe's garage one day and you're working for a financial services institute the next day, you get to be like an actor. And that's so rewarding because it's like it's all dynamic, it's different, it's changing. It's it's it's something that a lot of the people that I've come across who were talented, that were talented in that multidisciplinary thing. And I think that has a big impact on our future because being not just like a coder and being more broad gives us the ability to have empathy with the customer, have understanding in terms of how you solve those their problems, etc. I think that's that's gonna have a lot more longevity for for you know our background.

SPEAKER_04 16:52

Well, I there's no question if we're wondering if you have all those skills. Yeah, right. Let's just say that's your harness, like your career harness, and you have all those skills. Well, then you're thinking about which ones peel away in the AI era and which are which are gonna be serve me well.

SPEAKER_02 17:05

And you're talking about human skills.

SPEAKER_04 17:07

I'm talking about humans. The thing is, to Kate's point, like not everybody wants to talk to people and you know, interact with the customer and gather requirements and you know, do the people things. There are some slide the piece pizza onto the door folks, and like what what future does that hold? So I think that's really kind of where the what why they I mean, first of all, that was the first topic out of the gate at the conference, right? So obviously this is on the top of minds of a lot of folks, but there's easily 3,000 people there who are building and involved in the the industry itself. Um, also with that in mind, what what were some of the other uh topics that you guys heard uh uh bopping around? I would say that last year at this conference, I uh really started to hear context engineering for the first time, and that was like almost de facto in every one of the sessions. Um, any other things that popped out as you guys?

SPEAKER_01 17:54

Sort of another kind of word that was thrown around a lot during the keynote was taste. Oh, yeah, taste, yeah. Yeah, we bring the taste, so we're still important enough to be involved. We get a cookie, you know, that kind of thing. As far as the theme goes, though, uh much has been made of the cloud's nice and all, and the cloud is useful, but the cloud can go down sometimes. So the best approach is a hybrid approach. And the models are getting small enough and capable enough that we really need to take seriously the idea of having models running at the edge. Uh uh local and uh pushing the cloud, no question. Several companies, Redis is talking about let's have our storage being close to where the data is being generated, not up in the cloud.

SPEAKER_04 18:42

So is there talk about like is does local equal trust in those did it was anybody talking about it?

SPEAKER_01 18:49

It's like you know, if you have a data lake, some of that data is old and you can mine it for trends and things like that. But if you're trying to find out is my equipment about to fail, you need real-time data and to have it, you know, trucking you know, terabytes of data up into the cloud and bringing it back down is expensive. So saying, okay, I have this big gob of data here locally on-prem, get my uh AI infrastructure wrapped around it locally, so the latency is really sl uh really tight. Yep, and I can get real-time uh determinations of whatever I'm trying to figure out.

SPEAKER_04 19:23

I'm shocked that people aren't hammering the trust thing more, honestly, because that was literally the only reason anybody ever used an open source model in 2021 is because you didn't trust open AI, basically, right? Um, and there's definitely an advantage to that.

SPEAKER_03 19:35

Uh Vince, what were some of the I mean, I I I attended the last session that was talking about uh hurt uh you know agents as hurting cats. Uh-huh. And uh it was band.ai, I think it was. And this fellow from uh from Greece was giving that talk. It just fascinated me because at one point he had a slide that he was talking about the evolution of agents and how agents evolved uh for from being like something you you directly interact with to all the way to to the point where they have this uh kind of network. I don't know if anybody else uh participated in that talk where uh they they kind of like a a broker of uh of agents that you would talk to and it would find the right agent that you could talk to, and then you know, you would have that communication with all those different agents. So uh yeah, that was that was fascinating to me just to see that whole evolution and where that's gotten. And to the point where it's like I could see uh, you know, maybe maybe where it's heading in terms of, you know, uh if you're a company and you want to build a product or a service, and you would put your agents into the pool or a mix, and uh then it brings it begs the question of like how good is each agent? You know, it's like almost as the same as other people, right? You rely on this person for a job. Is that person gonna do the right thing, et cetera?

SPEAKER_04 20:54

Where those specialties come from and and and whatnot. And again, we're gonna have another segment here coming up in just a few minutes where we're gonna dive into the claw discussions and we'll kind of explore some of those things. Uh Marcus, what was uh you mentioned some of the stuff you talked about outside of the opening keynote stuff, what were uh some of the keywords?

SPEAKER_00 21:11

I think another key theme was was um memory uh memory con memory context, right? Or memory engineering. So we certainly context engineering was a buzzword a year ago, right? Now they're talking memory engineering was huge. And then we're just starting to talk about harness engineering. We haven't heard a lot of that, but I think that's the word the phrase to watch for in the future. But a lot of presenters and booths on memory management. I find that really an intriguing idea.

SPEAKER_04 21:40

I I I will say that my second observation uh out from the services world is like uh data matters, but then also, are you kidding me? Everyone's like blown away by memory, like statefulness? Like we like we've been living with in state, trying to figure out what to do with state for 20, 30 years, right, in our in our careers. And here this community is like, wait, you mean? More things and I can retrieve them in another conversation. Uh Kate, your observations from uh some of the other sessions.

SPEAKER_02 22:06

Well, you know, so I again like I was using Claude, so so as I would did this, so I did a little I know, I know. So Claude and I, um, we came up with some themes, right? Of of what, but again, this is my personal experience of the conference, because we didn't go to all the same sessions or I was taking notes on certain things. So again, like this is this is the AI of one, right? But this is my personal, you know, but but again, like I I'm I'm coming from the responsible AI perspective. And to me, this was a conference of responsible AI, but nobody called it responsible AI because the topics were observability, reliability. I mean, I there were several sessions, security. But but what was interesting to me was going to multiple sessions about, you know, um, you know, using RAG, using um embeddings, using um, you know, graph um yeah, and and different ways that are all, you know, either can work, you know, in conjunction or apart to achieve the same goal is like having less hallucinations, right? Having more trustworthy AI, which again is like, and so I I've always thought like responsible AI, the word responsible is gonna go away. Like this is AI.

SPEAKER_04 23:10

This is table states though, right? Yeah. I mean, from your perspective, is this good that responsible AIs become AI?

SPEAKER_02 23:17

It's it's just, it's just, yeah, it's just this is what we talk about. And it is, it's bringing back in all the determinism that we're used to because you can't have just an LLM put it out there. It's like you need all the other infrastructure around it. And I think we're still at the, you know, there's sessions about, oh, you could do this, you could do that, you could do that. And I don't think there's one right or wrong way. It depends on the situation and then, you know, what tools you have at hand, and then of course what budget. Nobody was talking about budget here. Nobody talked about cost. And I was thinking, well, how much is it gonna cost me? So I think there's a lot of factors that that are still go into this, and and we as consultants, right, this is what we have to do is is know all the resources we need to know and have all the tools at hand.

SPEAKER_04 23:57

I think that's a great observation. And I know that's that's an area that you spend a lot of time in too. So it's uh particularly curious uh having that be, well, actually, what Claude told you.

SPEAKER_02 24:06

Yeah, yeah, yeah.

SPEAKER_04 24:08

So as we wrap up the segment here, we're just gonna do a just a rapid round here. What uh for all the other FileMaker folks, all our all our um, you know, friends in the community, uh, what's your top line thought, Chris? What would you tell them about what you're seeing out here in at an AI conference? What is something, you know, some advice or a takeaway that you would give to FileMaker people in particular?

SPEAKER_01 24:27

One aspect of FileMaker is that it's kind of a joyful thing to work in. You can move so quickly in some areas, and there's a lot of fun to it. The original vibe coding. And there's a lot, I mean, Google just ran through all their stuff and they made like a Hindi techno song about organic chemistry and just you know, just a world. Yeah. And so there is a lot of fun to be had just playing with, you know, get back to play and play with some of these tools because there's there's joy to be had there. I love that.

SPEAKER_03 24:57

Uh Vince. Yeah, explore curiosity, those are words that kind of ring in my head. But uh I I thought uh at some point, just like Chris was saying, we were doing this with FileMaker so many years ago. Like iteration was a thing back then, like layout mode, browse mode, layout mode, browse mode, and you have like the beauty for the customer is like, wow, you just did that. Oh, that's so so exciting. But you know, we we've dabbled there, and and AI is giving that to us in a very different level, you know. So I love that parallel.

SPEAKER_00 25:27

Marcus, your thoughts? This one stumped me. I'm glad they went first. But it's come together. I mean, when you when you approach AI in a playful way with your years of knowledge, because you can't outsource knowledge, right? When you're working with AI, you're still in charge of telling it what to do of managing it. When you take all of your experience as your FM developer and and approach AI playfully, magic seems to happen. And that's what got me hooked. That's what got me hooked in FileMaker, that's what got me so hooked with AI was it just it's so fun. But now I come to it with knowledge that that a vibe coder doesn't have because of my years in FileMaker, which is a superpower.

SPEAKER_04 26:08

Okay.

SPEAKER_02 26:09

I guess um, oh yeah, this is tough. Um I don't know, I think I the word trust is coming up for me, and I think that's maybe trust your gut, trust your intuition, trust your human intelligence, but also like learn, you know, and I think I think AI, yeah, it's it's a whole new world, right? And and um I guess I think you know, we all have to move at our own pace with it. And I think it's but it's also important to listen to people that have been like like us, like we're out there, we're listening, we're gaining information, and and there's a lot of experts, and there's a lot of there's a lot of movement to just make it better and better. And like there's a saying, like, today's AI is the worst AI you will ever use. And it's like that's true, you know, that that that we're just we're in a process with it. So Chris, what are your thoughts?

SPEAKER_04 27:01

Well, I would say um, you know, if you don't trust AI, maybe you can trust some of your your fellow constituents here from the AI community who've uh ventured out and you know dove into this space. The come on in, the water's warm uh is really what I would let everybody know. So you guys are really appreciate you. First of all, it's been a lot of fun being at the conference with you guys. Look forward to day two. We've got a whole bunch more observations. Uh and uh and then next we're gonna put together a segment on um uh open some hot open claw talk. So thanks a ton, guys. Uh, look forward to another day at the conference with you guys. Appreciate this.