自己複製するAI、医師のチャットボット、ディナーに現れたAIエージェント

So, for instance, you have an agent that's local in your home that's reading your email. An attacker sends you an email, and then that email lands in the agent, and they take control of the agent, and they start to do things within your home. Now, that, to me, is the more immediate threat. All that and more on today's Mixture of Experts. Hello, and welcome to Mixture of Experts, a podcast from IBM Think. Each week, we bring together a handful of experts from IBM to talk about the week in AI news. We've got a full show today. Well, first, let me introduce our guests. We have Ailee McConnan, who is an IBM ThinkTech news writer. We have Sandhya Ayer, an associate partner in the AI practice at IBM Consulting. And we have Jacob Repp, principal engineer, automation software, also at IBM. So we're going to talk about three stories today. We're going to speak about what happens when your doctor starts relying on an AI chatbot.

We're going to talk about what happens when your AI agent starts networking. But I think we'll begin with a story about AI as a virus or rather a worm. Eileen, I think you first brought my attention to this story in our internal Slack. Maybe you could tell us a little bit about this story and really help contextualize kind of what is new and what isn't here. But firstly, what did Anthropic demonstrate? Sure. They came out with some interesting new research, basically, that they had found that small models are, you know, within months, pretty close to being able to replicate themselves. These are small models that you might find on, you know, IoT devices, you know, smart ovens. they use some cheap Android TV boxes, but that they'd be able to replicate themselves. And the key point was without connection to a data center.

You know, so my mind immediately went to, it wasn't exactly the same thing, but there was an AWS outage last year that resulted in smart mattresses getting really, really hot or, you know, getting stuck upright. But, you know, it touches on this sort of notion of, you know, AI gone amok, but with everyday items. And, you know, I think the first thing that struck me is that, you know, often we think of powerful AI needing these massive data centers, expensive chips. And, you know, in this case, instead, it was a, you know, capable model, but they didn't have to run it on specialized AI hardware. They spread the work across 15 pretty cheap Android TV boxes, you know, and they're able to show that it's, you know, very close to self-replicating. So that's already sort of kind of caught my attention. But, you know, I think in terms of your question of, like, what is new here? Because, you know, computer worms that spread from device to device, that, you know, that isn't new. But I think it's this idea that the models can kind of adapt, can figure out how to run on unfamiliar hardware, you know,

but that, yeah, that they can independently sort of figure this out and spread. and they had like a great line in the research paper where researchers were describing it as a kind of slow distributed and adaptable digital slime mold, which was a good turn of phrase. And so, you know, I think that, yeah, it raises all sorts of questions, but it, you know, I think this kind of adaptable worms is a provocative topic. topic. And, you know, of course, I'm interested in what my other panelists here, you know, think about how seriously we should take this, you know, threat today. It's not quite here, but, you know, what should we be doing to prepare for what it does derive? Growing up, one of my favorite movies was The Brave Little Toaster. So the notion that all my appliances might, you know, have brains and be conspiring with one another, kind of scary, kind of cute. But, Sanja, what were you reacting to here? I gather that, as Ailee mentioned, self-replicating viruses are not entirely new.

That's called a worm. That's a category you've known of for a long time. What is different when it is the model itself that is the payload or that might be kind of like the threat? Biggest threat or even the thing that I got curious about is like them self-adapting in an unknown environment, which is what makes it super interesting and also scary, right? It's right out of the sci-fi movies. So that is the most interesting part, and which means that it's probably time for us to secure these or focus on the security and guardrails around these devices as well, because you never know how things may change over the course of time. But it just means that we need to make sure that the environments are safe and secure. And we focus more on that as the team in general in terms of today's, you know, agent workforce, workflows, etc.

And that makes me wonder, like, is that harder when the environments are so distributed? You know, when it's like you think of, you know, securing, like, how many Android TV boxes or, you know, whatever the device is. Is it sort of building in the guardrails at the point of manufacturing? Or guardrails make sense in a kind of corporate enterprise setting, but in terms of these more commercial devices, how do you do that? Probably the user's authentication, like permissions as to what you can do in those devices are some of the things that you could do. But, I mean, we don't know. We will learn more. Jacob, who do you think was the intended audience of this post, of this exploit that was demonstrated? Is it the IoT industry itself? Because I gather that one of the fears is that your mattress might suddenly heat up and try to kill you. But I also gather that the main reason the IoT of it, the Internet of Things, these connected devices, why that was important is because this is a notoriously unsecured industry, right?

Like data centers are pretty darn secure and well monitored, but these mass produced IoT devices less so. I think security as a whole is a concern. I think that is kind of what is the light is shining on here. And we've seen acceleration in the ability of people to reverse engineer and people at home just experimenting with hacking their devices with Claude and with installing models and doing, you know, like we found that Claude is just or, you know, any of these large models, they're great at training smaller models. They're just really good at it. You know, whether that's because they themselves are also now training themselves recursively, but we're starting to see this kind of models training models. And I think there is an ounce of skepticism I bring to this as well, where, you know, I've seen firsthand how hard it can be to get certain model architectures running on different types of hardware. so this isn't something that I'm like

red flags in my own home but I do think a lot about security in my own home and what happens when I'm unleashing maybe not a small model but a larger model and giving it an objective to work on a device or to work in my home and then it finding these devices that are just not very well secured and what happens from there so I think that is a really interesting angle to take on this I do think I will never look at my microwave quite the same way again. But again, it would be helpful for people like me at the back of the class, less technical people, to try to – I talk about managing my anxiety, but maybe focusing it in the right place. Is the worry that the worm makes everyone's microwaves explode at the same time? Or is the worry something else? Because I read about botnets and I know just enough about like DDoS attacks to know that sometimes the thing you're really scared of is a zombie army just kind of lying in wait. And then massive amounts of computation swarming a high value target, like a government target, something like that.

So what is exactly the or do we not know yet? Like what exactly is the sort of the threat that we are worried about if this worm were to replicate? Like an external source, being able to control all the devices in my home and my data gets access to all of that. That is the threat here, at least what I think. There's another positive angle, but we will talk about it once we hear from everyone else. No, tell me about the benefits of digital slime mold. I want to know. I was very curious. The minute I read data centers, like it's an alternative to data centers, I actually started thinking more around distributed inference So it means that in a different way I could if I very worried about my data going out of my house maybe the model that I want to use for my personal use should be using all the compute from my own source. So it's a more sovereign setup for myself. And yeah, that's one way of looking at it. And the other is, I'm not, yeah, more sustainable solution

around the inferencing. Right. Why do you need a data center at all if cumulatively across your 100 set-top boxes that you have, like, in your drawers in your home, you have a data center in your home. Yeah. Like, you know, it's slime mold, but it's sovereign slime mold, so. Or in an office using all the CPUs that exist, right? Jacob, what do you think about that? I mean, just the little bit that I know, for instance, about blockchain, right, involves resiliency across millions of lower intensity devices, right, a distributed ledger across thousands of nodes in a network. Is there something here? Is there a positive side to this era of small models running across many devices? I'm really excited to be able to tap into more compute around my home. I mean, I have, you know, these incredibly powerful devices and, you know, floating around, you know, even just in the phones, right? I think we're a long ways off, at least I feel, from this being a serious threat because of physics.

I think that one of the problems is just bandwidth, you know, like to do anything serious. This is why you generally have data centers is because you need power and you need bandwidth to really do stuff at, you know, threats at scale. and yeah there's a lot of things you'd have to jump through to get to something where it poses like a specific threat I forget what the terminology is for that but a threat to you or your family or maybe even your thing I'd probably be worried more about is like privacy or even just maintaining the control over my devices and And yeah, I think having something where you have the sovereign data center that runs internally. I mean, this is kind of how my home runs now. It's like it's all behind its own VPN and I have a few GPUs and I have local agents that do tasks for me and I try and sandbox it.

But I think those are the ones I think about more. It's like, OK, what can that agent do and what can get into that agent? So if you have a model that's running, how are you controlling the inputs to it? And I think that that becomes the thing that really, as we get more and more models running in our homes, that we need to really think about how are we controlling the inputs to those models? Because that's where the real threat is, I feel. So, for instance, you have an agent that's local in your home that's reading your email. And an attacker sends you an email. And then that email lands in the agent and they take control of the agent and they start to do things within your home. Now, that to me is the more immediate threat. but in the future, could that agent then jump off and do a more widespread attack within your home or embed parts of itself into various devices? Probably, because a lot of devices we run in our homes are just not very secure right now. Just as a last question on this, Ailee, Jacob talks about the plausibility of this threat being imminent and I'm wondering if you responded to some of the data points

in the story about the slowness of this slime mold, right? Could you share with us some of the figures about how slowly the self-replication would take. And therefore, what do they point to? Because they're pointing to sort of the trends in how much faster over time, like what might this look like in three or six months as opposed to now? Yeah, no, I think that, Jacob, you made a good point where, you know, there's a reason we often need data centers to do things efficiently. Like the, you know, the speed at which this digital slime mold could potentially move is, you know, is quite slow right now. And, you know, I think that introduces an interesting, you know, from a security perspective, almost, you know, does it make it offer us an additional layer of observability? You know, the fact that it's slow, you know, we can see how it's spreading. that, you know, at least for a little while provide some advance warning or, you know, we have a chance to sort of understand, you know, as these models potentially, you know, self-replicate, we're not quite there yet, you know, where the specific risks are highest.

You know, I think of like some of the headlines where they've been these very large models that have gone rogue. The problem is they've already gone and deleted, you know, a whole season of a television show or, you know, kind of at least if it's moving slowly, we can study and perhaps get some lessons, you know, from how or yeah, from the potential dangers or privacy or security violations. I don't know how much my anxiety was managed. It has a different quality now. It feels like a slightly more informed terror. So thank you for that. In a moment, I'm going to move us on to our next story, which is about doctors and chatbots. Before we do that, I'll just mention, if you're watching this on YouTube in particular, if you could hit like or subscribe or comment, that really helps the show to grow. So thank you for that. For our second story, this is your doctor on AI. So basically, it turns out, according to a study in the prestigious journal JAMA that came out this week,

I believe that stands for the Journal of the American Medical Association, and it was co-authored with some employees of a tool called Open Evidence. It turns out that Open Evidence is a chatbot that is free to use for verified clinicians. It's actually paid for by drug ads, something maybe we'll bring up later. It's valued at about $12 billion. But basically, a huge adoption, huge adoption of this tool. About half of the U.S. doctors have kind of tuned into this app. The other thing that it does is it's plugged into a vast database of medical literature. So it's not, hopefully, just hallucinating, you know, ideas about what to do about your gangrenous limb or whatever. It's actually, like, researching stuff and giving citations and so on. And I think maybe I'll begin with you again as our journalist to sort of give us some high-level context. What were some of the findings that stood out to you in this study, some of the numbers or takeaways that you felt were really the lead of the story here?

Thanks, David. That's a good question. There are several findings, you know, that caught my interest. I think at a high level, I was certainly interested that, you know, I think it's 51.9% of the queries were related to decision-making, kind of treatment management. you know, versus a much smaller number, I think it's 13.9, were about actually diagnosis. So it's sort of, you know, the question, you know, we know patients, I use AI to diagnose myself, but, you know, kind of starting to read the article, it's like, you know, what are the doctors using it for? And it's, I thought it was interesting, it's less about, you know, what does this patient have, but, you know, what should I as the doctor do about it? But, you know, and I think the other thing that I was sort of fascinated by was within the specialties, you know, which specialties were using it more. And, you know, they had, you know, I think general internists, you know, these are doctors who have to, you know, have a good sense of a lot of different medical fields in order to route, you know, their patients.

and, you know, and it was, I think, underrepresented in, I think, radiologists, for example. But, you know, for me, that raised an interesting question of, you know, the use specific specialties, does that give us any, shed any light about, you know, how it's being used or how will AI will continue to transform other sort of knowledge work AI? You know, is it those areas that in jobs where people have to synthesize a huge amount of information, And a lot of it, you know, outside of their field, you know, are those the ones who are really sort of transformed first? But yeah, I'll pause there because those are some of the sort of findings that for me caught my attention. I was also struck by those, I think, to focus on this theme of the generalism versus specializing kind of spectrum within doctors, right? Because there's not just one class of doctors. Some doctors become GPs or ER doctors, which where they see a wide range of cases and their job is routing and triage like you said other doctors are

highly specialized they're radiologists they're cancer doctors so for instance there's this big finding about the percentages at which certain types of doctors use the chatbot for information outside their specialty and for cancer doctors it was only 35% of their queries I think but for ER doctors it was 88 of the queries coming from ER doctors were questions outside of the realm of emergency medicine So I do think what you seeing is well very very different use by sort of what segment of doctor you in But it would seem that broadly, my gloss, my intuition is that broadly, those in the more generalist side of the spectrum, especially ER doctors, are using this to help with triage and with the routing. Sandhya, what was your impression? And if that is true, if that hypothesis is true, is this a good thing? Is it a bad thing? Do we not yet know? What is your hunch? Yeah, I think especially in terms of the ER doctors, it might be like they don't have the time to reach out to all the specialists when there are cases coming in.

And so from a positive side, that makes a lot of sense to me as to them reaching out to this chatbot. The other thing that I would be worried about, or I would say that it shouldn't, I would imagine that they're not using ChatGPT or any of the models as is how we use it, but they go through this platform that is catered to, you know, it has all of the knowledge base of all the documentation, et cetera. And so that gives them the right information. And then also ensuring that it's built in a way that it's at least giving 95 percent confidence in the answers that are retrieved because they are not the experts. So if they're going to this system, then it needs to be relevant enough for them to take any decisions. Those are the things that came to my mind, especially in terms of ER. Okay. Jacob, we were chatting earlier and you mentioned that you did feel like this was a story about cross-disciplinary acceleration, that you're even seeing this on your own team, even though you're not running a hospital. Can you talk a little bit about what you're seeing on your team and how this feels like you felt like you were getting echoes of that when you read this study?

Yeah, I mean, one thing is that just these new tools like what are called like embeddings or the ability to sort of take knowledge and put it into this reasoning space that's clustering essentially. It allows you to get really good access to a broad set of information and dig in. And it feels like it gives you superpowers in some ways. And I think it cuts both ways. So like on our team, we have a designer who's been doing a lot of UX implementation. And we also have been working in a field within IBM on Z mainframes that none of us really know much about. And so what we've done is we've augmented our development environment with the ability to get information about Z mainframe details. So even non-technical designer artists essentially have this rich wealth of information about this highly technical topic. and they can build demos and actually modify the UX.

Now, there's downstream implications of this when these changes get to experts. Or say you're doing triage or you're doing some first pass thing, and then it gets to an actual UX developer. They're probably going to have some things to say about it. But I feel like in the medical field, you have that kind of, what we're talking about is triage or quick decision-making to get into a better place faster. And I think it also just plays to all the doctors that I know. They're just some of the most curious people that I've met. They are glens for information. And so having tools like this that's just beyond just Google search that can give them access to information in this format probably just feels amazing to them because they can just explore all these details of the medical field. And there was one other thing that popped in my mind. I remember in radiology, when they were first introducing image classification to radiology, there was a lot of pushback.

And the radiologists were like, we don't, there's even like these stories about, oh, this is going to remove like radiology as a field, like don't go into radiology. But it turns out that we actually need more radiologists and it actually helps them do their job better. And it is kind of this focusing thing. It allows, you know, a lot quicker detection and more insight. and it actually improves the field as a whole. So that was one thing. I don't know if it's fully relevant here, but it feels in a similar vein. I have a personal anecdote to add to that. So my friend is a radiologist, and the minute she saw these come out, and she was like, oh, my God, like my job is like a trip. But no, like it only – I've seen that it has only – I mean, she's mentioned this. So it has only made her be more efficient at understanding the image better. But eventually her expertise is still needed to make that decision. So it's the acceleration, I believe. Maybe, I'm presuming the average cost of a radiology scan maybe went down during this period of time of image recognition.

And I'm not an economist, but if it's a certain kind of commodity, that just means you're going to order more of it because the doctor would like more of that information and more patients can afford it, perhaps. I don't know. But the reason I raise this is because I just think, like, in the medical field and so many fields, and just for me, just processing all the information I do, I feel like I'm in this swimming in this new environment where I have access to so much more information so much more quickly. And it is a little bit less vetted, but I'm still vetting it to some degree, right, because I'm clicking through on the rag generation and I'm fact checking and I'm sampling the fact checking. So I feel like I am swimming in more information. I'm getting to answers quicker. Those answers are maybe a certain percentage less likely to be accurate than the average answer I got access to 10 years ago. But I'm getting so many more answers that I'm taking the trade for For now, we'll see what my clients and my employer thinks in the long run.

I feel like I'm imagining these doctors sort of swimming in similar waters. They're like, wow, this answer that I couldn't get, that I could only get from a specialist on the order of a week, now I can get it in minutes. The confidence level is lower. My confidence level, what to do with it, a little bit lower. But, boy, it's better than nothing. I don't know. Eileen, were you thinking something similar? Because you're probably, as a journalist, just drinking from the fire hose of information at an even more guzzling rate than we were 10 years ago. Yeah, certainly. And it's like that balance of, like, where can we use AI to synthesize knowledge? And then when does the sort of, yeah, when does the kind of judgment, you know, decision-making sort of, you know, come into the process? And I feel like every profession is probably kind of wrestling with that. Because even I was just thinking like a, you know, an ER doctor, you know, or a GP that is, you know, getting so much more information at their fingertips. They still have and, you know, while they're not the, you know, oncology expert, you know, they still have regular checks and instincts sort of kicking in such that, like, even if it's, you know, adjacent or outside their field, they're going to still make better judgment, of course, on all of that than, you know, the patient who's just reading, you know, X, Y, Z.

And so, yeah, I think, you know, for me, it's that tension and that balance between, you know, how much information is actually useful and then when are the human kind of checks needed in that process just to ensure that, yeah, you know, kind of it's accurate enough. Like you're saying, the percentage is probably a bit lower than what it was. But, like, does that matter for many things? Probably not. And like, as Sandra pointed out, you know, in an ER context, actually like having, you know, relevant information on a timely manner, that's, you know, super important. And so I'd rather be like, you know, much more quickly available and pretty close to, you know, what it might have been, you know, versus losing that time when treatment, you know, and the patient's condition is at risk. There's a saying that a little bit of knowledge is a dangerous thing, and one can see the risks of just sort of getting a little bit of information. But there are also risks to having not enough information, of course,

and you can get sepsis and die in an ER environment. So hopefully, one hopes that in this profession, as in so many professions, people are making the right trades and are learning as they go. All right, let's move on to our third and final story today. This one was kind of a mixture of cute and insufferable to me. I'll be here to see what my fellow panelists think about it. But this is a story in The New York Times. 22 founders went to a kind of networking you know tech founders went to a networking event in Manhattan but there was a shtick involved which is that they could only get invited to the event if they had an AI assistant an AI agent basically book them for the event And so then they went to the center and then while the humans were networking and having their cute dinner, their AI agents who had booked the thing were also in a chat room doing their own kind of version of

networking. I have a lot of opinions of this, but Sanja, starting with you, what was your reaction? when you saw this? What's good? What's bad here? What were you thinking? Yeah, I wanted to know what did they actually talk about? I don't think there's as much information on this. I would love to see the overall report of what did they talk about. I guess my first reaction was, yeah, it was cute. Now that we have Muse and Dot available, I would love to see how that works. I mean, I have tried booking, using an agent, booking a reservation, but I've never let my agent go and talk to other agents, which is interesting. What struck me, I mean, because I'm also a skeptical AI person, I always think about, okay, what does that mean? I guess talking is okay, but like, and first thing is to figure out or, yeah, to control the conversations so that it is not like an AI sloth or things like that.

But beyond that, are we letting them take any actions? Like, am I letting my assistant go and commit to something else with the other, with Ailee's assistant without talking to me? That's scary, for sure. The answer in this case was no. Basically, they really scoped the permissions on these AIs. They called them agents, but they had no agentic tool calling capabilities. I think actually a lot of the New York Times article was about the ambiguity of whether we should even be using the term agents and what that meant and whether you should put names on AIs anyway. But I think the thing I responded to is like I've gone to the sort of like speed dating networking events where you're kind of doing two jobs at once where you're meeting the person socially and getting a vibe check of whether you could work both together. and you're also disclosing information about your CV to sort of seeing, there's kind of this utility, there's this social component and there's this utilitarian component. And I'm having to do both at these networking events. But I kind of would like the idea that what if I could just be like,

who are you? Where did you come from? Like, you know, what matters in your life today? Say nothing about my work and let that utilitarian aspect be unfolding on this side So that if we had a good vibe interpersonally, later I can go home to a report that said, by the way, the very Machiavellian side of you, as represented by this agent that was just carrying around your CV, found a good match. So not only did you vibe socially, there was a good match. And then I thought that would be a cool use case. It sounds like that's not exactly what happened, but that was, to me, where my imagination went with it. Eileen, what do you think? What was your reaction to this story and what, if anything, might be good in a world where our agents are kind of doing our business deals off to the side while we talk? I like kind of just being able to focus on, you know, what poetry people are reading at the moment and, you know, kind of cut out the, as you say, the Machiavellian. And, I mean, for me, I think it, you know, it opened the lid on the fact that, you know, we've many, especially in an enterprise setting, are aware of, you know, setting and kind of rules about how AI, you know, interacts with humans.

And, you know, of course, there's a whole additional, you know, kind of rules and guardrails, you know, like when AI is, you know, interacting with other AI and other agents. And that, you know, that's a whole huge, you know, area that, you know, like, again, there are a variety of approaches to. You know, I think of, I believe Anthropic has sort of principles that kind of its AI or agents will follow, you know, as opposed to setting every rule, you know, out at the beginning. because, you know, you want it to be adaptable because, like, again, you can sort of say that, you know, Sonja's and Ailey's agent can't go and, you know, they can't go and have coffee without, you know, telling us. But there are going to be situations that come up, you know, that, of course, you can't predict and control. So, you know, how do you set those, like, agent-to-agent rules? And then when they actually, you know, when does this become useful? I think it's, like, when they can actually take actions and do things. And then I think, like, for me, the interesting balance is, you know, clearly the human has to stay in the loop at some point, but, you know, how much is helpful,

you know, for safety security purposes. But if, you know, the human keeps having to check in as well, then it's sort of like, what's the point of having the agent, you know, to, it's not increasing kind of the speed at which you get something done if you're always being, you know, kind of asked to approve things. So I think the, you know, yeah, the new, the set of that, new sides of that balance that this introduces, you know, are kind of fun to think about. and you'll be forced to think about it as they actually do, you know, the next piece, the follow-up in the New York Times, you know, maybe they will be enabled to actually do something, these dinner party agents. Jacob, if my agent falls madly in love with your agent, do they have to report it to IBM HR? If we share manager, they probably do. That was my instinct as well. But you were saying, actually, Jacob, that you're increasingly using a coordination layer for your agents, you've noticed you're actually really playing in the weeds. Me, when I read agent-to-agent communication, I listen to agent-to-agent podcasts, and I read a

few lines of Maltbook to prepare for this episode, and I just want that time of, I want those five minutes of my life back, because when I read agent-to-agent communication, I just get so sad for everyone involved, for no one involved, right, because there's no one home. But Jacob, you're seeing interesting things happen when you coordinate agents, I hear. Yeah, well, let me get to that. First, so I come from video games. So this is like a fascinating, I'm like, so this is like, I was kind of geeking out about the story. And I was just thinking like, you know, what is the, putting these agents into like a mud or kind of like a text-based adventure, you know? So it's like you're in a dinner party, but your agents are in like an escape room or they're in some D&D adventure, right? And there's objects and stuff and, you know, maybe there's wind conditions and special party favors and things. Now that, that's kind of where I might take the, if they want to do another party and they want to invite me, I'll, I'll set that up for them. But in my own personal work, like here at IBM, I've been doing agent coordination since February. And what I saw back in February is kind of like a very lackluster capability in the agents to work together.

They're like, oh, I don't know. I don't know how to do this. They're like kind of awkward nerds. These latest models, I think. So I have a theory that because most of the harnesses now, they have what are called like sub-agent capabilities. I don't know if you guys have tried this, but you can do agent swarming within Codex or Claude. You just say, hey, go do this with some sub-agents, and it'll fire off however many you want. So I think that the models are actually being trained on this. So they know how to coordinate now, at least internally. And I've noticed that when I put them in my coordination layer where they can coordinate between Codex with Claude, with our own internal agents and whatnot, they're really good now. they're like they they they you can see them kind of communicating sending messages back and forth like they coordinate around problems they sort of do what we call like leader election where they kind of like pick a leader um and so sometimes now i have to basically say i have to choose a leader i'm like i have to be like no you're the leader you tell everyone else to do when i add everyone

else and i'm like no no you're not a leader you're just gonna like like whatever the leader says you're going to kind of do that, you know, and then there's a lot of interesting things here, like this intersection of, you know, games, for me in my mind, games, distributed systems, and this new, like, agent and agent communication. I love watching them work. Like, it's fascinating to me. Like, I actually have been trying to get more, what I call, like, legible surfaces where I can actually see kind of at a 5,000-foot view what's going on. And then, you know, I just sit there and, like, wow, that's fascinating that you're You're just so good at working together now when, you know, like six months ago, you guys were just completely awkward. So this is this is really an interesting time we live in. Now I'm imagining entire tribal societies of digital slime mold electing leaders and so much, so much fodder for my nightmares this evening. Well, that's all the time we have for this week. Thank you so much, Ailey, Jacob, Sandhya. And thanks for tuning in. We'll see you on next week's Mixture of Experts.

Thank you.