Building the Foundation for the Agentic AI Era

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Hey, welcome to another edition of the Practical AI Podcast. I am your co-host, going solo today. I'm Chris Benson. Daniel's not with me this time, but we have an excellent conversation coming up for you. With me today, I have Angie Jones, who is the Vice President of the Agentic AI Foundation, which I think is a super cool title to have at a super cool named place. And I'm really looking forward to finding out more about it. Angie, welcome to the show.

Thanks so much, Chris.

So, like I said there in the intro, and I said it before the show started and stuff, like, if somebody in AI was looking for a place like the Agentic, I mean, like, It is, that is the coolest sounding thing you can have. And, but before we dive too far into the foundation, I'd really like to kind of hear like, how does someone like, like, how do you develop in your career so that you end up doing that? Like, could you tell us a little bit about your background? Because that's a, that, you know, that's like, that's one of those things. If you, if you went and just said something to like, I work with tons of people working on agentic AI, but like leading the foundation would be cool. So I'm just curious, like, how do you get to that point?

Yeah. So I'm your traditional techie. So I've worked as an engineer for a couple of decades. So, you know, at places like IBM and Twitter, and the last role was at Block in an engineering leadership capacity. And so, In that role, one of my tasks was to basically teach the entire company, 12,000 people, how to use AI agents. And this is as I'm learning myself, because I mean, there's no book for this, you know, right out the gate. And that was early, like 2024. So a lot of this stuff was brand new. It was not even common in tech, let alone in other verticals, right? And so I also like lead developer relations. And so a big part of my role is helping developers worldwide understand new technologies and how to use them. And so our company Block created like this internal AI agent named Goose. And Goose, we were— the developers were using Goose to like automate engineering tasks, help with coding and things like that. And so we were teaching developers across the globe, what is an AI agent? Like, we were very early in this. And so Jack Dorsey, who leads Block, was like, hey, Angie, you're teaching like everybody else about agents. I really would love for everyone in this company to learn how to use agents. And so I'm talking finance, marketing, like, you know, design, HR, everyone. needed to learn about AI and more specifically how to utilize agents. And so me and the team went like really deep on this. And then it got to a point, like pretty AI fluent company where everyone is comfortable using this. I needed to go really deep on the engineering org. And so that was my home. Our engineers were using this, but we weren't seeing a big Difference in like developer velocity, for example, right? And so we, we explored that and learned that, you know, we're only at the tip of the iceberg. We really could do a whole lot more to get to this autonomous engineering org, right? And so I pretty much drank from the firehose of like all of the news, all of the releases, everything that's going out. And I consume that, kind of filter out a lot of the noise, and then bring the things that are valuable to the engineers. And so I would say, like, I know a lot about the space. And also, at my time at Block, I worked on a protocol. So this was like a cross-border money movement protocol. So those who don't know Block, that's the company, the finance tech company that houses Square and Cash App, right? So money is our jam.

Mm-hmm.

So I worked on this protocol, never thought I would be a protocol girl, but learned a lot about, like, just kind of open standards and how all of that works as well. Also really big in open source throughout my career. I've always, like, believed in open source, contribute to open source, like, advocate for it, right? So all of that kind of came together in this perfect storm as OpenAI, Anthropic, and Block wanted to form a foundation for agentic AI. They understood that, hey, some of these standards, some of these open source projects that we're coming up with, we probably shouldn't be the sole authors or owners of these things, right? MCP is a great example. So MCP is the Model Context Protocol. This is what agents use to connect To applications and tools, right? Which we saw across Block. Everyone in Block needed MCP service to connect to whatever applications they were using. And so like Anthropic realized, yeah, this probably should live in a neutral home. And so those 3 companies came together to form the Agentic Foundation under the Linux Foundation. So Linux Foundation has been around for decades. Everyone knows them. And so, yeah, so this is a new foundation. So, once we stood this up, me and our head of open source came over to the foundation, and we thought it was so cool, we started working here full-time.

Very cool. And I like the Jack Dorsey name drop there. That was, that's pretty good. Like, so you actually, you were actually working directly with him as well along the way.

Yeah, that's right. So, I worked with Jack at Twitter, and then he brought me over to Block when they started developing, like, these open-source projects and protocols.

Very cool. I want to back up for a moment because I got a couple of questions from things that you brought up there. And one is the education, because I think educating the organizations that folks are in, like, you were, you know, pathfinding along the same kind of task that a lot of organizations are trying to do right now. And that is, especially every year recently, but especially 2026 has been just insanely fast in terms of the level of progress and the onset of agentics. They were there last year, but this year it just has taken over the world. And so, every org is dealing with that now. And you have taken point on this notion of education, not just for developers, but for the whole org and kind of parts of the org that maybe people aren't thinking about as much, 'cause they tend to be very focused on developers. Can you talk a little bit about what creating that kind of change looks like in terms of, and I'm gonna separate it, I wanna ask about the non-developers first.

Yeah.

Like when you're going into finance and you're going into these other organizational departments that are not thinking about the bits and bytes of AI all the time, And you're trying to say, here's a new tool. And like, how do you approach that? Not only from the upskilling that's required, but also from the kind of the, like, getting people to accept it. Because I mean, you know, you see like people out there, there's a lot of resistance to AI in the general population out there. And so like, how do you navigate that when you're trying to move the org forward like that?

Yeah, these were 2 very big and different challenges, right? So one is the whole change management of it all. Like, you're essentially asking people to think differently and do their jobs differently. These are experts in their domains, right? Who've maybe been doing this a couple of decades, and you're like, oh, kind of throw away, like, your processes and everything, and we want you to do this. Not only that, to your point, resistance, right? Fear, lots of different emotions involved in that. And then the whole technical part of it where like these tools, like I said, this was very early on, 2024, 2025. You didn't have these nice desktop applications. You had a CLI, right? You were copying JSON to like get an MCP server working. This was foreign to folks who, like, don't use these tools every day. So it was a really big job. And so, like, you have to consider all of that. It's not just, hey, let me show you how to use a terminal, right? It's helping them understand where they still fit into the process, right? With things, it's okay to let that go. Like, you probably should not be doing this anymore because it's not a good use of your time anymore, right? And then also, I like to say there's people on both spectrums. There's people that, you know, they think AI is the best thing ever and can do all the things. And there's other people who are like, oh, AI is stupid. It can't do anything. I like to kind of be in the middle there where, you know, I can be realistic about its strengths, its weaknesses, what I should use it for and what I shouldn't use it for. And just leading with that helped a lot because I'm not trying to get you to drink Kool-Aid, right? I'm saying, hey, here's a different approach that could speed you up, right? That can also give you access to a lot of stuff that maybe you didn't have before. And that part was the key. So when there's, let's say, data, right? Everybody needs data, no matter what role you're in. If I say, hey, listen, I can get you access with this agent and be like some MCP server, To, let's say, uh, records that are in this database that you would have had to go to another team, put in a request, and ask for a report, right? And then try to figure that report out yourself. I can get you that data in seconds so that you can go and, like, do your best work. Sold! People are, like, sold on that kind of thing, right? So you take them step by step and give them things that, uh, are meaningful to them that might not be their core job, So, just so they could get used to it and they could start building some trust, right? And then eventually they start delegating a little bit more and a little bit more, um, until they find that right spot of, okay, I shouldn't delegate this. The AI does not do well with that. That part I'll take, right? So, that, that was my, my overall approach to it.

I, I really like that. And when I know, when I get in conversations with folks, uh, about this topic, um, one of the things that I often say, which I think is, is kind of resonating with your story there is that like find the place where the human has a distinct advantage from the AI and differentiate the AI, you know, go down through the job requirements and find what, where the human is still better at this point. And that may be fluid, you know, that may change over time depending on that.

Yeah.

So like having an open mind is good, but I think that's incredible advice. that you're giving people in terms of how to navigate, because I think that's one of the biggest questions that people have out there based on the conversations I'm having. To flip to the other side of the coin, when you're dealing with developers and you're— and I'm a lifelong developer, I've been doing it for decades, and I see every day on social media and stuff, I see people, you see the developers that are Embracing agentics fully and they're orchestrating. And then you see the ones that are like, oh, it still sucks. And then, you know, I'm still, you know, all that stuff. And like, and how, what was your experience as you dived into the pool of developers trying to get the embrace going here?

So, for a while, if we look back, so back then you're like, oh, this is great. But it wasn't that great, right? I mean, and they're in the early days. it was good for that time, but compared to now, it was— it's not. And so developers who would like maybe try, um, the tools and they didn't do a great job, you ask it to write a feature or something, it's like, what? This is stupid, you know? Um, those developers, I found once they tried it once, if it didn't work out well, they kind of threw their hands up. Um, and it wasn't until Like end of maybe 2025 when the, like, was it 4, like Sonnet 4.5 or 4.6? I don't even remember the versions anymore, but it was like this point in about November of 2025 when the models—

That was it.

The models just got really good. But we couldn't wait until then. Like I had a mandate to like get everybody using AI and to increase developer velocity. And so What I did, thinking back to that change management, and I, it was too much of a lift to get, it was 3,500 developers, to lift everyone at the same time, especially when you have all of these emotions involved and you have this resistance, right? And I would say like we had quite a few people who were resistant to this. So what I did instead, there's this this rule. It's called the 1-9-90 rule. And this rule essentially says that in any community, there's going to be 1% of that community that are creators. Think about like social media, anything like that. You'll have 9% that, you know, they'll dabble here and there, the tinkerers, if you will. And then the 90, the bulk of people are consumers. Right? And so I said, if I look at our engineering organization through that lens, let me go and put together the one person. And so I went across the org and I pulled people, and they didn't necessarily have to be the drunk off the Kool-Aid, like, AI-pilled people, but I just needed representation from every major repo that we had. You know, our largest ones, our critical ones. I wanted people from various teams, different types of repos as well. So I needed frontend and backend and mobile, iOS and Android. You know, I needed these people. And so I did this little week-long campaign just going across the org. These people don't necessarily, I'm not their first line manager, right? So I need to get buy-in from their first line manager. That's a— Totally different conversation because a lot of them weren't even bought into this, right? But I say, hey, listen, you know you have this mandate. You got to get people to use AI on your team. Give me one person that I can have 30% of their time. And what I want to do is, one, they all come together, we learn all of this stuff, but it's not just for them. What I need them to do is build that knowledge back into the systems themselves so that your entire team benefits from this, right? And so they were there with me. We're drinking. It was 50 of them drinking from the fire hose, trying things out because things are changing so quickly. There's so much noise. You don't know what'll work, what won't work, right? Everything looks great on Twitter. In a little demo. But we're working with huge, you know, monorepos with, you know, hundreds of services in them. Like, you know, we're working with people's money. You know, these are enterprise systems that you have to approach this a bit differently and much more carefully. And so they would try things out. We'll see things, we'll try them. Some things will work for Maybe a specific type of repo, like, okay, this works great on web, sucks for mobile, right? We can't use that technique. But together, the 50 of us were able to, like, try a lot of things, figure out what works. Maybe I do have something that works on my iOS repo. Hey, other iOS repos, here's what I figured out, right? And then, like I said, embed this stuff into the system. So things like, context engineering techniques, things like, you know, agents MD files and agent skills and all of that baked into the repo so that no matter who was pointing their agent at this repo, the agent could work the way that your team wanted it to work because it was baked into the system. So we didn't have to, you know, teach everybody how to do this. These champions essentially did that for them and filtered out the noise for their team. So they would bring back The things that actually work that are tried and true. And then they would do a brown bag session or something with their team. Like, let me show you guys what I put in the repo and why it's working for you all of a sudden, you know? Um, so that, that was the technique, uh, that we used there and it worked really, really well.

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So that is super cool. I'm going to borrow your techniques myself going forward. I bet a few other folks listening or watching will do the same. Really insightful there in terms of how to approach that. As you arrived, having gone through a lot of this, and you arrive at the Agentic AI Foundation now, and you're kind of, it's brand new as an organization, you're standing it up, you're taking on the responsibilities. Could you talk a bit about like, what was it like to get the foundation going and get it focused and get the right people and know that you were— were doing the things that needed and what those are.

Yeah. So this, the Agentic AI Foundation was formed end of 2025. So it's been what, about 8 or 9 months at this point. Lots of excitement, right? We had dozens of companies that were interested in being a part because everyone sees the need for this. Everyone is also innovating at lightning speed. There's so much innovation that's happening right now. And if you have anything that you're creating that's like, you know, oh, this is— we don't have this figured out. We need a new standard for this. No one wants that standard being cooked in one kitchen, right? If we're talking about agentic commerce, for example, PayPal doesn't want Strike figuring that out by themselves, right? They want to work together or they need to work together and they know they need to work together. And so with the Agentic AI Foundation, has done is provided this neutral home where all of these various companies can come and have these conversations together. So we have working groups essentially where the members, they've, okay, which working group do you want to be a part of? And there's so many of them. There's like, like I said, the agentic commerce, right? So if you're figuring out how agents are going to buy stuff on the web, all right, I need all, I need Visa, I need PayPal, I need Stripe, I need, you know, all of these various companies together and they work on these standards together, I like to think of it as they are defining the rules of the game and creating the game board together. And once they have that figured out, it's like, all right, now we can compete. Deal, you know, deal me in, right? Um, and so that's what the Agentic Foundation does. It also houses those standards or those projects. So, uh, right now the projects are MCP, AgentsMD, Goose, Agent Gateway, and then also just this week, A2A, which is Google's agent-to-agent protocol, has come into the foundation. And so these projects, now you have companies from all over contributing to these projects, right? Or these standards. You then give it like, you know, more of a voice. If you If you wanted to use one of these projects, but it lives in one of these companies, you might be a bit hesitant, right? As a company to build your products on top of this, if it lives with this one company, I don't know what they're gonna do. I don't know if they'll like kill it. I don't know if they'll just, the roadmap will only reflect their goals. You know what I mean? And so this being a part of the Agentic AI Foundation gives it that confidence that, hey, this is in a neutral place. I now can build on top of this. And so that's what it looks like. We do a lot of education around the projects, the protocols. We throw conferences in all parts of the globe. And I'll say like one really cool aspect of this role. So in my last role, went deep on agents, but I'm also inside of my company. Right? A little bit in open source, I'm consuming, but I'm still— my bubble is probably North America. Now, this is a global foundation where my job is to pay attention to what is happening across the world. How are they adopting agentic AI in Japan, in China, in Africa? You know what I mean? And so now I have this this global perspective, which is absolutely fascinating. And part of the role is also to help these various countries come together on these standards and protocols and projects so that they're interoperable across the board.

I'm curious, so that, that does sound super cool. I'm curious with that global perspective that you're, that you have developed in this role, How do you assess? I think it's very easy for pretty much, I think it's easy for anyone kind of coming from their own perspective and their own little bubble that they're in to kind of assume I'm doing agentics and everybody else is doing it just like me or has the same needs and stuff like that. And so I think it's very easy to forget that diversity of location, diversity of life, Um, all those things can change the user's need, uh, for that. And so do you have any, any particular highlights from, uh, that global perspective on things maybe that surprised you or that caught your interest? I'd love to hear some of that.

So I would say in places like China that are mobile first, right? Um, the way they use technology is a bit different than us.

Yes.

So we're in North America, I would say a very like SaaS-heavy culture, whereas everything is like mobile over there. And so now, like they, for example, they have a big need for a protocol like agent-to-agent where you need like this app to be able to talk to that app and they're both agentic apps, right? And so they could like use that, like for example, like WeChat. uses that, you know, with various agents and stuff like that. So that was really fascinating to me to just see like, oh, even the types of applications you're using essentially influence like the types of tech that are standards or like anything like that that you might need. And so that's just one example. Yeah.

That's cool. I'm curious. So, and this is where I'm gonna be selfish on my part. I'm, as an engineer and a research scientist, I'm very focused on autonomy. And as you're getting into mobile, and that's getting awfully close to thinking about edge concerns and stuff like that. And embodied intelligence is such a hot area. It's certainly the area that I'm focused on. And the rapid rise of robotics in all domains, whether they're ground robots or flying things or whatever, is truly— taking off like it never has before, no pun intended there. I'm curious how with this kind of— and I think in some parts of the world you see that more than others. I think in China, Japan, and there are certain places where you're going to see a lot more robotics than you will in the US. For US listeners or watchers, we don't have nearly as much of that here.

That's right.

And so, when you're looking from From trying to take these projects that the foundation has, and you're looking at those different needs and you're seeing, well, the Americans and the Canadians and such kind of have one way of doing it, and the Chinese and the Japanese and other, like, that may have a different way. How do you reconcile? Because that's quite different in terms of how you're using agents in those ways.

Yeah.

And the way you're configuring it and the way you're constructing them and what their utility is. How do you, how do you approach that when you're trying to make everybody happy with standards that are truly meant to be global?

Yeah, that, that is why you need everyone at the table, right? And so, uh, if you only had like the, your, your top like FAANG companies in the US kind of determining all of this, and there's no one from these other countries that, hey, robotics is like a big deal here. Like we have to think about the physical applications as well, right? then you miss that perspective, right? And not to say like they don't have places there, but you need leadership from those companies to also have a part in crafting the story, right? And so that's exactly like what the working groups do, like find your lane, get into it, you know. Europe is another good example. So they just rolled out the EU AI Act, right? And so that like It's going to change the transparency that these systems have had. And that's global. If you're going to be serving anything to someone in Europe, then you have to be able to adhere to these new legislations. And so that's another example where, okay, I don't care what country you come from. You have to, one, be aware of this, and two, also help define how this should be done, right? There was a lot of backlash on how Anthropic rolled this out, for example, right? And so we have a working group that is looking at, are there some systems that we could build or some standards or something so that everyone doesn't have to reinvent this and figure out how they're going to do it, right? So then you have the voices from various countries together to figure this out and say, okay, what if we did it this way? They haven't solved that yet, by the way, but that's an active group that's meeting regularly. And by the way, all of these working groups are open, so anyone can join them. Just, you know, they have meetings, they're public. You could just like kind of go into it, see what they're talking about, even chime in with thoughts of your own. But these folks meet, and this is— you have one for security, you have one for Identity, like, you know, any lane that you care about, um, there's, there's someone there like kind of working on this from across the globe.

Yeah, I know, um, as a very specific to, to that, uh, it was just a few days ago that Anthropic released their paper, uh, along with a blog post on watermarking, um, so that you can detect, you know, AI-generated content and all that, which was a fact. And like, if, if, uh, This is probably not the only episode that's going to come up on it. I think we're going to probably do a deep dive on that very soon, and hint, hint. But that is one of those things that, to your point, affects everybody.

Yeah.

And there probably needs to be somewhat universal approaches to what is watermarking, how should it be applied, what should it be applied to, how do you utilize that? Both as the provider and as the consumer of that.

Yeah. Or do you even do watermarking? Is there a better way to do that?

That's right. That's right. So, I think I'm starting to see lots and lots of different utilities. I imagine the foundation's going to grow quite a lot in the years to come.

Yeah. There's definitely a lot of work to do.

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So as we, uh, as we start, uh, getting to the point where we can, I'd love to dive into, uh, kind of maybe get a high-level overview of the— of each of those projects that you described a little while ago, just kind of what it is for people that aren't familiar with them. And then maybe dive into like where some of this stuff is going, you know, based on where you're at. I know that you and I have talked about MCP and other things. So I'd love to go wherever you wanna go in terms of some technical deep dives, 'cause we love that here.

Yeah, sure. So we have AgentsMD. So this was a standard created by OpenAI. And this one standardizes like how projects, meaning like code bases, communicate their operating instructions, right, to agents. And then there's Goose, which is one of the first open source AI agents. And this one essentially provides like this agentic runtime. And then there's MCP, of course, which connects agents to tools and systems. Agent Gateway, this one was— oh, and I didn't tell you, Goose came from Block and MCP came from Anthropic. Agent Gateway came from a company called Solo. And Solo created Agent Gateway to mediate traffic. So essentially like MCP traffic, A2A, as you're using this stuff in enterprise, you kind of want some controls around what the agent is doing and what has access to, you want to, you know, make that observable and stuff like that. So Agent Gateway is an open source project that does that. And then A2A is the newest addition. This one is from Google. A2A is the agent-to-agent protocol. And this one is really cool in that this is how agents can coordinate with other agents. So you can delegate work to another agent or, you know, Uh, just be able to collaborate on a given task, right?

That's pretty cool. The, I, I'm curious, you know, one of the things that, that I know in, in my world that I've been, uh, exploring is, you know, if you look at late last year and people would just have an agent, you know, to do something. Yeah. And then, you know, we kind of moved into this year and people are doing multiple agents that are starting to collaborate a bit, and you're starting to have that, uh, kind of crosstalk and collaboration. And then now we, uh, you know, as we record this, we're in August, uh, and, um, and we're talking like it's gone up orders of magnitude in terms of—

Yeah.

It's, it's some, and like, that's not everybody. This is some use cases where you're talking tens of thousands of agents sometimes, or hundreds of thousands or even millions, uh, are now coming into play. And as you're, I'm curious, like, as you're looking, that, that has to be a challenge to some degree from a scalability standpoint, because, you know, going back to what you were saying We are racing along so fast, much faster. And I'm old enough to remember, like, I won't— I'm old and therefore I remember before internet was even a thing and I was even an adult at that point, sadly. And so, like, I know the velocity that things happened across each of the various technological revolutions that we've had. Over the past lifetime, if you will. And so, this is going so much faster than any of those others. And all of those have had processes, that's why the Linux Foundation itself came into being, and lots of others where these technologies came, there was the need to get collaboration across competitors and across global concerns and needs. But, but you've stepped into the fastest moving area ever. And, and it's gone in just a space of months from like singles to millions. And as we, like, how does, how does a working group, and especially considering that these are at the end of the day, all competitors, these different members, you know, in their, in their own businesses, how do you manage that? Because by the time working groups, like historically, by the time a working group would arrive at something.

Yeah.

Like sometimes you were so far past that. So how does, is there anything, you know, like, are there any expected timescales or anything on how to get these things together so that it stays relevant? Because the speed of relevance is just unimaginable now.

Right.

So in terms of how fast that is, like, how does this new foundation address these kinds of problems that we've We've had it to a lesser extent, but never like today. How do you do that?

Yeah, we knew going in when we were standing the foundation up. In fact, that was a question. Do we, do we do a foundation? Foundations are slow, you know?

Yeah, I guess that's how people think of it.

This is not slow. This is not a slow-moving space. Um, do we do this right? Um, we, we realized we have to do this because you just have to have these neutral homes for things that are this Critical, but we cannot move at the pace that your traditional foundation will move at. So we move a lot faster. And I think it really helps that the folks on a working group, these are not volunteers doing this in their spare time. Like, it is part of their job, you know, to innovate the future. And if you know, hey, we, we are trying to build a product on this standard, then you're gonna speed everybody else up. We have to come to some conclusions here, right? Um, and I think that really helps is that the innovators themselves are the members of these working groups. And so they have, uh, uh, basically they, they, they have to move much faster. They have an incentive to, uh, not drag this along, you know?

Yeah, it does. I'm curious, when you see, when they come into the working group, is it, and you're talking, what are, to the rest of us on the outside, very fierce competitors, OpenAI and Claude.

Yeah.

They're hammering it out and Google's in there and there's a whole slew of them. And is the tenor of the conversation a little bit, is it, I guess, because A, they gotta get stuff done. As you said, you gotta get stuff done because everyone's waiting to move on. Does that collaboration, do they kind of just drop? I'm just, it's just as a sheer curiosity, do they just drop kind of that external competitive behavior and just get in and say, yeah, let's just get it done? And is that—

Yes, they do. It's fascinating. They actually are very friendly with each other, right? And so, like, sure, like, yeah, our companies just got into this heated Twitter war or something, but you and I got to figure this standard out. You know what I mean? And so—

You can let those crazy people talk, but we got to get this thing done right now.

Yes. So we're super collaborative, which is amazing. It's amazing to watch. Uh, I tell people, like, if you want to see the beauty of like open source and people working together from competing companies, all you got to do is go in the MCP Discord server. It's a thing of beauty. There's just like dozens of these working groups within that protocol itself. And you have members from everywhere, every company that's like doing real work, not just throwing ideas out, but like doing real work to add new features or figure out like how, how to do this other new thing or solve this problem because their companies depend on those things, right? And so it's beautiful.

It's actually really beautiful to So as we start winding up here, I'm curious, like, it's just kind of, you know, just the speed of operation is a little bit mind-boggling. As you're looking ahead and you guys have put together this foundation and you're having those really productive and rapid conversations to get stuff done so that this world continues at the velocity it's at, like, where, what are some of the things that you might expect to see having come along the path? Because I think you're in a bit of a unique position to say, you've built into this, you're one of the people that set the foundation up, you've seen it start to work and you've seen it working well. And with those kinds of relationships forming and everything, how do you see things moving forward? And you could be a little speculative. It's fine to be wrong. I say things on the show all the time that are wrong. Um, uh, Daniel and I are like, oh, oh, you got that one wrong. But, but we try, but, but it's fun to, it's fun to try to think, well, you know, where, where might things go? Um, what, what are your thoughts? Like, where do you think, where do you think we're heading, uh, into this brave new world where everyone's trying to figure out not just like what, like no one knows 10 years, but like, like people are trying to figure out where's it going to be in 6 months?

Yeah. Yeah.

Um, what are your thoughts around that?

If we look at like you and I, you know, and probably everyone listening to this podcast, we kind of live in this bubble where, you know, we're likely very exposed to AI. We're working with it daily. That is not the case across the world. Like, you know, the general population, like you said, one, they hate AI. And 2, they probably are not using it like in their day-to-day, right? Maybe they've asked Chat GPT a question. You know, they searched from something for Google and a little summary came up, and that's pretty much the extent of what they've done. So outside of our bubble, like we have so many challenges right now just amongst like the tech community. Outside of that, when you start looking at how like the everyday person is going to utilize agents for all sorts of things. Right, within their lives, you just start seeing all of these other things that we need to figure out. And so I think that there's a lot we have to figure out. I think that'll consume us for the next couple of years. I do think that AI will become a part of, you know, the everyday person's day-to-day, just like mobile phones and internet have, I think the same will exist. I hope, I don't know, but I hope we're not just giving it all to the agents and we become workers for the agents. You know what I mean? I hope that we kind of get to that middle ground where we find, even though it's capable, this is not a good use of it. And these are the ways that we should be deploying it.

Fantastic.

That's some, I share that with you. I think we need to find the, you know, Where does the human fit into the equation and where, where is AI a tremendous utility? Angie, thank you very much for coming on the show. This was a great conversation. Really appreciate it. Gave me a lot to think about. I plan to dive into, into some of those projects myself and learn a bit more about them. And thanks for coming on the show. Hope to have you back sometime.

Thanks so much, Chris. I enjoyed it.

All right, that's our show for this week. If you haven't checked out our website, head to practicalai.fm and be sure to connect with us on LinkedIn, X, or Bluesky. You'll see us posting insights related to the latest AI developments, and we would love for you to join the conversation. Thanks to our partner Prediction Guard for providing operational support for the show. Check them out at predictionguard.com. Also, thanks to BrakeMaster Cylinder for the beats, and to you for listening. That's all for now, but you'll hear from us again next week.