94. The Truth About AI in Senior Living with George Netscher

In this episode of Raising Tech, host Matt Reiners is joined by George Netscher, CEO of SafelyYou, the leading ambient care platform using AI to transform senior care. George shares his journey from top-tier AI research labs to launching a mission-driven company rooted in his family’s personal story. From fall detection to staffing analytics, hear how SafelyYou is redefining what's possible in senior living through ethical, outcomes-driven artificial intelligence.

What You’ll Learn:

• George’s unique background in AI and how it inspired SafelyYou
• What AI really means in senior living
• How SafelyYou is helping communities improve fall rates, reduce ER visits, and optimize staffing
• What operators should ask when evaluating AI vendors
• The balance between automation and the human touch in care
• How senior living can leapfrog into innovation 

Resources Mentioned:

• SafelyYou Website
• Follow George on LinkedIn 
• Follow SafelyYou on LinkedIn

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Transcript

Matt Reiners: [00:00:00] Welcome back to another episode of Raising Tech. Today I am joined by my friend George Netcher, the CEO of Safely You. If you're not familiar with safely, you they are the leading ambient care platform and george, this is not our first time talking ever. I've known you now pre pandemic. We met together at a, provider in uh, the great state of Tennessee.

I don't wanna name them, but It's just been awesome to see all the awesome stuff you've been able to do in that short period of time. All the impact you guys have been able to have on the industry. And I think when I think of like leading technology vendor companies like you guys are. Top three easily, if not the first one.

So it's been [00:01:00] awesome getting to know you. I'm excited to jump into some things today, but uh, thank you for making some time here today. 

George Netscher: Thank you for having me. Yeah, I think we go back. You really develop a special relationship when you're doing the conference circuit together and we were both building our companies and working hard and, doing everything we could to do something important.

And so definitely enjoyed just getting to know you over the years. 

Matt Reiners: And like unlike you, I sold out. But I'm glad to see you guys are still going after it, so that's awesome. That's awesome. And so George, I always find it really interesting 'cause you have a deep background in ai and I like even thinking back to my own, I.

Conference circuit, attending, all these things. Like you're the first person, I think I heard speaking about it well before 2020. And I know you came from like one of the top research labs in the country and around ai. My first question for you is here, can you just share a bit about your journey and how your expertise shaped your entrepreneurial journey?

And I'm gonna give you the okay to brag a little bit here. 'cause I know you're very modest when it comes to this sort of stuff. 

George Netscher: Yeah, thank [00:02:00] you. I'll try to do the humble brag maybe. So I had good fortune of getting accepted everywhere back in the day for computer science, PhD, and turned down Stanford, MIT Harvard, and went to Berkeley, which not everybody knows, is tied for number one.

It's really Berkeley, Stanford, MIT, our number one AI research labs. And so I joined the Berkeley AI Research lab in 2014 really with just the goal of Hey, all the women on my mom's side about Alzheimer's disease. My mom's, we know she got the test. We know she was genetically predisposed. I've got this 10 year window to try to build what I want for my mom.

And so it's just like there's gotta be some way and it might not have made it into the senior living circles yet, but in 2014 we basically passed this big inflection point in 2012. And so in 2014. In the AI community, everybody was looking at oh wow, we are really on a new level now.

And so things like self-driving cars are potentially possible, like all these different, we've got this shiny new tool. In deep learning was basically the thing that changed everything. [00:03:00] Back in 2012, people saw how powerful deep learning would be and so now they're applying in all sorts of different ways and see Amazon Alexa with speech and all these different ways where we're trying to recognize patterns and do things that we might've thought were human before.

Wow. 

Matt Reiners: Yeah it's pretty cool to know that. And I don't think a lot of people can say that they turned down the uh, the places that you mentioned. I think those are just some small community colleges around the country. Something like that. Great schools. Yeah. Yeah. And it's not a surprise to you or me or anybody listening to this that has any familiarity with technology within senior living, AI is a major buzzword right now, I think we would agree that not all AI is created equal.

And I'm wondering from like your perspective, what truly defines artificial intelligence in senior living versus just smart automation or marketing fluff. 

George Netscher: Honestly at this point, even if you ask like the Berkeley faculty, that the term AI is really hard to bucket, so don't feel bad if you're lost.

I think the term AI is marketing fluff at this point, [00:04:00] and it's really hard to, like is it just, can you call basic like trending and analytics AI at this point? We used to call AI like what is now called. A GI like artificial general intelligence, trying to actually give like common sense to machines.

But what I would basically call AI at this point is like anything that you like would've 10 years ago thought you needed a human to do. So like recognizing speech and now on the generative side, creating things like job descriptions or things like that. So that's my hand wavy definition, but that's the one that I use.

Matt Reiners: Yeah, it's one of those buckets that's got so many buckets within the bucket and probably even more buckets and uh, pools associated with it. So it's hard to just, there's so many times I talk to even some of our clients that say we wanna use ai. And I'm like, what do you wanna use AI for?

And they just don't have that answer quite yet. So it's interesting to see how it's really overtaken, not even just this industry, but I think everywhere as a whole. 

George Netscher: What is Gen AI versus what is ai? I think people don't necessarily know hey is chat GBT and what's the difference between some of these [00:05:00] things which obviously have come a long way since we had the chat GBT moment a few years ago.

Matt Reiners: Yeah, it's been amazing. I'm doing a talk at a few conferences about AI and walking through how, there's been a lot of stuff with AI over the last even like 50 plus years, right? And like it's all of this to build up to be an overnight sensation and like what that can do for us and like how we're using that, right? I think, if I were to say that senior living was a laggard when it came to technology, you would not be surprised at that comment. But I'm wondering like in your thoughts, even within Safely, outside of Safely, like why do you see implementing AI as a necessity for senior living?

George Netscher: There's just a ton of opportunity. Like it's, it is a necessity for every industry right now, but particularly those industries where labor is the big challenge, right? If we just don't have enough people, we need to help people be more efficient and effective. One of the the big opportunity with AI right now though, I will say, is more on the like knowledge workers, white collar worker side, because the way all these tools work is like from.

Training on [00:06:00] data, you can produce outputs and it's really hard to have enough data in places that are like physical work. So it's hard for us to I think we'll have stuff in robotics in the future and things like that. But the tools we're getting right now are really more about helping level up the back office, the corporate office things around marketing, things around accounting where kind of I think the big opportunity that providers and operators should be thinking about today is like, anytime.

You should never be creating something from scratch. Like the workflow of today is that you should be editing, not creating. So like you should, if it's a job description, you should have something. Create it for you first and then fine tune it. If it's marketing content, if it's like any one of these things, if you're working in an Excel sheet, instead of creating the formula yourself, you should be able to work with a co-pilot and say Hey, create me a formula that does, X, Y, Z thing, and that should be your workflow.

And then you should be able to be much more efficient and effective in that way. 

Matt Reiners: Yeah, that's what I see too. As like the short term opportunity for AI is like, how does an individual just get that much more [00:07:00] productive, right? Like, how are you using that? I love how the analogy you use of you're not creating anything from scratch, right?

Like even just looking at my own workflows, I was, I have to present on it and was doing the time saved and like, each week in terms of like meeting notes, marketing collateral random ideas. I'm saving like 20 to 25 hours per week. So I'm, automating the mundane, elevating the meaningful, which is definitely a thing I got from Matt GPT that I've been building up here over the last uh, year or so.

But it's really amazing to see of what that can do and how that can just be, make me be that much more impactful as an individual. I think like the, at the enterprise level and right, and like working across multiple people with varying. Appetites for things like this is, that's where like somewhere we're trying to figure some of this stuff out.

But it's interesting to see of how that's taking place and changing weekly almost. And totally. And 

George Netscher: I would say that senior living may have been a bit behind, but for that, there's an opportunity to leapfrog, where it's like if you think about hospitals or other parts of the care continuum.

And [00:08:00] technologies like Safetyyou, they may have gone and like implemented systems that weren't able to bring the level of intelligence that we can bring today. And so I think like they, those systems weren't necessarily designed with a as AI first, and now they're trying to strap it on afterwards versus companies like Safe for you were built.

AI first, which means that it's the idea was there from the beginning that we could recognize all of these complex patterns and give insights to a senior housing operator that they would've never had before. And there is just like a limitless amount of opportunities there from starting with falls and, when somebody went to the ground and doing it with really high accuracy to doing a lot more than that.

And I think that it's almost like the imagination is the limit. And then being able to ask the questions in the right way, which I do think we have a way to go in this industry in terms of like how we vet technology and talk about outcomes. And the way we go about thinking about AI is just different from the way that we might think about other technology.

I. 

Matt Reiners: Yeah, it's a good call. And I [00:09:00] know we'll get into that here in a little bit of like how to evaluate some of those AI solutions. We've mentioned Safely u you know, a couple times here and you know, I know many people know Safely U for fall prevention. But you've expanded far beyond that.

And I'm wondering like what are some of the other impactful ways AI is enhancing senior care today? 

George Netscher: Yeah. So one of the big opportunities around staffing so we have a product called Clarity which is basically from the same platform. We can do things to support immediate resident safety needs, like whether they fell and provide the clinical support to actually know, okay, what happened here?

What can we change and really reduce risk for individuals. The kind of next big product we launched is around staffing and being able to have a lot more insights around staffing. And what we realized we could do is from that same system tell exactly how much time staff we're spending in every room without requiring them to wear a device or manually input data or anything like that.

And so now we can really level up things like. Hey, is this person still in the right level of care? And how do we have that conversation with a family member where now [00:10:00] an ED can actually have data and say, oh, we can see we're spending 20% more time getting dad up and rest in the morning. So I think around the immediate safety needs, there's an opportunity to have all the things you would want to be like, oh, somebody just left and we didn't know about it, or somebody just fell.

And all of those all from the same platform that also gives you all these, this intelligence around what staff are doing in the room. And then from the same platform that gives you all this intelligence around like resident wellness and isolation and things like that. So imagine all the things you just could do if you had, a fly on.

If you could be a fly on the wall in every apartment, but also have some intelligence behind it that can say, Hey, I see there's something different happening here that we should be thinking about responding to. 

Matt Reiners: Yeah. And it, it makes so much sense, right? Because I, when I look at ai, like it's, there's a few different components to it.

Again, it depends on what bucket you're looking at, but like incorporating a few of those, right? So like you're able to do data collection that's accurate every single time, get that into a database and then you can [00:11:00] start to look at trends over time and start to make some of these conclusions. Which I think I would actually be 

George Netscher: really careful with that language. Okay. Like Every single time. 'cause I think that's the exact. Like paradigm shifts, how you should think about these technologies is that no AI solution will be accurate every single time. And so it's like that's the world of moving from. When in previous platform shifts with, like mobile for instance, we're used to thinking deterministically.

I press a button and something happens every single time. The AI thing is that like I press a button and there's some probability that something will happen, or I ask chat GPT and sometimes it responds in this way. Sometimes it responds in that other way based on like slight different tweaks in the way you said it.

And sometimes it gets it right and sometimes it gets it wrong. And so the way folks should. Think about these technologies and ask questions about them. It is, and needs to be much more like outcomes driven. So like how often does it get it right and how do you tell that? And make sure that you're asking, thinking more in terms of statistics than in terms of that this is gonna happen the [00:12:00] same way every time.

Matt Reiners: And that's a good call out, George. 'cause I'm even thinking about, when I was using chat EBT even just a few months ago, and I'd asked for like a summary of me and it told me that I got my MBA from Stanford. And I was like, that couldn't be further from the truth. But I was like, yeah, you could keep telling people that's okay.

But like I think it's, I think it's good to call out, right? And especially as we're in this infancy. Stage with some of these technologies, like we can't assume it's gonna be right a hundred percent of the time, and we need to make sure that we're putting the right parameters and processes, systems in place to making sure that we're in this process of battered data.

But we can't say it's a hundred percent perfect every single time. 

George Netscher: And that is what the AI community's really scared of today. We're not scared of Skynet coming or things like that. What we're scared of is people trusting these systems too much too soon. So you get in a self-driving car before it's ready and people die or we're at a point where you can do things like generate care plans, for instance.

But how often is that accurate? And may we need to be really thoughtful with that. Someone isn't just [00:13:00] copying and pasting a care plan from an ai, they're actually like need to be reviewing it and making sure it's correct and things like that. So we've been very intentional and thoughtful with that kind of design where we don't just provide the technology.

We also provide clinicians that work with every one of our communities because we need to have somebody that's actually like verifying that here's what the next step we should be taking is, and putting real thought and expertise in. Because as you see, the output may not be exactly correct each time with ai.

Matt Reiners: Yeah. And I want to take this in a direction. You had mentioned Skynet, and for those of you that don't know that reference Terminator basically. I think when I talk to a lot of people that aren't really AI curious, I would use that terminology. There's this fear of these humanoid robots coming and taking everybody's jobs and all that sort of stuff.

And I'm wondering like. What's that balance, right? Because I think this industry is so important 'cause that human touch, the time spent one-on-one with, residents is just at our core why we work in this industry. But I'm wondering like, what's that [00:14:00] balance between automation and really maintaining that human touch here in senior living and in care.

George Netscher: Yeah, I think the goal of a lot of this stuff is to take off all the work that people don't like doing, right? So if you think about our platform in the room, the vision we have long term with clarity is that the staff should just never need to document anything. You go into a room, you spend time with a resident, you do your job and you don't need to spend time manually inputting data or anything like that.

And then you have something that prompts you when it's oh, I forgot to give someone their meds when I was supposed to, or things like that, right? So it can be much more like proactive and instead of this industry can be so after the fact and punitive and things like that. 'cause something didn't happen that was supposed to, now we can have like tools that ride alongside people and can help them at, various stages in their journey.

So I think the right balance is don't force it, but there are opportunities and lean in and all of that. Because there are ways that we will just keep being more efficient and effective. But also just make sure that you're [00:15:00] asking the right questions when you're heading thinking about outcomes and, 'cause there's a lot of stuff that can 'cause of the challenges in AI is it is really easy to like cherry pick results.

Like I can create something. A weekend that can like, make it look like I can do something, but it's I might have been able to do it once and I can create a video that looks like, it works, but can I, does it happen consistently? And I think that's a lot of the battle here.

Matt Reiners: Yeah. So we talk about evaluating those AI solutions. I'm wondering like senior living operators that are looking at some of these, right? What are the questions they should be asking to cut through that noise and identify true innovation? 

George Netscher: Yeah, so first I would like. I think one of the things you and I both know is that because this industry doesn't have like CTOs or CIOs for the most part we in this industry have a very kind of vendor driven innovation model.

But not every innovation that this industry needs necessarily supports the whole company. And so I, I think the first thing I would say is start with the problem you're trying to solve, because it's really easy to go to a conference and be like, wow, there's all [00:16:00] these amazing things.

But I. I think whatever a good operator does, it's okay, what are really my priorities for the year and what are the things that I'm focused on? And then there's tools that can provide that in various ways and all of that. The other thing I would say is that the, is to basically you need to evaluate based on outcomes.

I. So in the AI world, you move from evaluating based on inputs to evaluating based on outputs. So you shouldn't be looking at okay, what are all the features that something has because it's not that hard to add a feature but have it not work that consistently. And then you're setting your team up for disappointment and frustration and all of that.

So you really need to evaluate based on outcomes, and you need to think about what's the appropriate sample size. To have you feel conviction. So if you need something to happen correctly, 99% of the time, then you need at least a hundred samples to see that it happens 99 out of a hundred times.

So we'll do things like when we pilot, have a, hey, what are we trying to prove here? And maybe we need at least a hundred residents involved for this period so that we can see this kind of outcome [00:17:00] data. 

Matt Reiners: Yeah, it makes sense 

George Netscher: to think in that way. Which to the credit, I think most folks in the senior living industry.

Do already think in that kind of way of I like to pilot things and make sure that we're seeing all the things we want and things like that. 

Matt Reiners: Yeah, and I think to your earlier point about a lot of these provider groups not having that C-I-O-C-T-O and it's even been interesting in my own journey where now at Parasol, like that is definitely something that we do.

And it's funny from my perspective to come from this tech vendor side and I could tell in the providers I talk to in terms of like their tech strategy, whether if. As a salesperson, if I could dictate that for them and almost give them that golden spoon opportunity syndrome, whatever you want to call it.

Or if they were more the providers that had a problem and then were seeking vendors for that and being very intentional about how they were going through that. And I think a lot of the time, to your point, they go to these conferences, they get wind and dined by charismatic awesome people such as myself, but there's funny how that acts and I think to your point of figuring out what that can look like and. Solving for those [00:18:00] problems and what are those outcomes will really set them up for success. And we're talking about those outcomes like. I think a lot of the times for a lot of these AI solutions, when you're talking to providers, again, their specialty is care and experience.

Some of the technology verbiage is going over their heads. But like when you think of those outputs or those outcomes, like how can providers really assess the effectiveness of some of these AI solutions and, are there any metrics they should be looking for in terms of this?

George Netscher: Yeah, so I, I think it's a great point. And how many times have you and I seen, companies get burned, right? Where like you invested in a solution and then three years later they've pivoted out of the industry or it didn't work in the way you expected or the company didn't work out, or things like that.

And so it is really tough to see that happen. So I think the first thing I would say is just work with trusted partners. If you're gonna make a big investment here and. You want to be able to see that investment grow over time. And so working with the right kind of partner that you're not just [00:19:00] getting, the solution as it is today, but they have a proven track record of being able to like.

Continue to work with customers for the long term. So I would ask, on the like business side, I would ask things like about their customer retention, how much churn they have when they lost customers, why was it, and see if you can talk to references that, left that company and why they did, whether it was around reliability or whatever else.

And then when you're upfront, I would try to run like a structured process where. Here's the things that we need to see to believe that this is, really moving the needle for us. So I need to see if you're evaluating a program like Safely, you, I need to see this level of like clinical outcomes.

I need to see how it ties back to the NOI in this way. And so I'm gonna, I'm gonna set up an experiment effectively and measure outcomes. And then I'm, work with that partner on. Look, it's gonna take 60 days to see clinical outcomes. It's gonna take, six months to see the NOI impact.

And we're specifically looking at NOI through things like length of stay. We'll show an average four [00:20:00] month increase in length of stay. And so we want to go every time, show that for our customers that it's you're gonna see these outcomes and you should hold us accountable to it. Because we would never do something that like, my mom is getting the program right now and it's like we would never release something that wasn't like something we wanted to use personally.

And we're gonna hold ourselves to that standard that if we miss a fall, that could be, my mom and I like spend time training our teams of that video could be my mom that's on the other side of the video. And so set the outcomes, measures you care about and then. Hold us accountable to it.

We all should be delivering on this 'cause this is really important work. 

Matt Reiners: Yeah. And you talk about some of those outcomes and even some of the stuff safely you has done. And I'm wondering, and I've seen like some awesome case studies you guys have put out recently. Is there any, whether you wanna highlight one of those case studies or some of those real world examples of how safely you AI is really transforming an operator's ability to improve care or operational efficiency.

George Netscher: Yeah. So I think a [00:21:00] lot of our journey was going from having, just amazing fall outcomes, which we will show again and again, that we will reduce falls by about 50% on average, sometimes a lot more. And we will reduce emergency room usage by about an order of magnitude on average.

And it's important to say again, like use that language on average. 'cause there'll be obviously some variants. And I think that's a lot of what can be lost. And a lot of where I've seen, companies that we used to compete with fail is that they would market their like best case result, and then at you're setting every customer up for disappointment, right? That then they they try that, they're not seeing the best case. Surprise. And then they're like, this thing doesn't work as well. And most of our business comes from current customers expanding the system and we're all about earning the right, and it's like, those are the kinds of questions you should be asking of does most of your revenue come from, new logos or does it come from current logos, expanding the service and how many places are you fully rolled out with?

And things like that. 'cause that shows that it's like they tried, it, had conviction. Screw over time. So coming back to your point a lot of our journey [00:22:00] was in the first 60 days, we would show those clinical outcomes really clearly. But then if you're thinking about putting this across your portfolio and not just taking it to the places where you think, you can afford it or things like that, you have to be able to show really hard r ROI associated with it.

Because it's like there's a lot of other things people could be spending hard earned dollars on and we, care a lot about accessibility and affordability and. And so if you're gonna be spending money on this, it has to very clearly move the business forward. And so we looked at a lot of different things of, we know people move in for the system, but it can be hard to track that and really get attribution for it because, did they move in for us or did they move in 'cause the salesperson was great.

All that stuff. We know folks get liability premium reductions from our work. One of the res we were with told us they got, they carry the GLPL for their operators and they get told us they got a 19% reduction in their liability premiums, which is amazing and it takes a lot of time showing outcomes to, to get that data.

But really where we found is like we could get, drive clear NOI impact [00:23:00] and make sure that it's like actually us that we're actually like, again, proving it to ourselves that we're really moving the needle was around customer retention. And so what we will show just again and again is a very clear impact in length of stay for residents that use our program compared to those that didn't opt in and compare to a baseline period.

As well as one of the things that, like I really hope every operator is looking at is, will show a big impact on your 90 day retention for residents. And that should be something you could see fairly quickly. And that not enough operators are breaking down their data to look at okay, what's my longer term length of stay?

But also just like I look at my staff, what's my 90 day retention rate? I should be looking at, how many of my residents move out in the first 90 days? I. Because it's a lot higher than you think it is. I can almost guarantee, and it very well may influence how you think about things like a Place for Mom and things like that.

If people, if a large number of your residents are moving out in the first 90 days, then you can start to figure out, okay, how do I better retain customers? Am I losing them [00:24:00] upfront for this reason? Is it health and safety? Is it because the cost is higher than they anticipated? Is it. And often if it's if we can keep them past 90 days, it like becomes somebody's home.

Once it's your home, it's a much bigger switching cost, just like having somebody move out of their own home and into a community. 

Matt Reiners: Yeah. Yeah, it makes sense. And so important and I love, talking to people like you where technology's obviously working right. And like you can see that clear ROI and some of those clear outcomes and it's just, i'm a big fan as you can tell. I'll say I, 

George Netscher: it is such a privilege candidly, of like being able to just like. If somebody's not seeing the outcomes and the data, it's wait, we must be analyzing the data wrong. It's like we have that much confidence around the product and like the team that's delivering on it.

And it is the whole, it's not just the technology, it is the clinical component, hand in hand that we are there making sure. That like we're delivering outcomes on a day-to-day basis at the community level. And so yeah, having it is a real privilege to just know that the system does work really well and then be [00:25:00] able to ask the questions from there of okay, then how do we make sure we're showing NOI or how do we take the next step to support not just falls, but staffing and, all the other things we can do.

Yeah, I love that. And then my 

Matt Reiners: last question here for you, George, it's a two-parter, so we'll be ready for that. But, looking ahead, where do you see AI making the biggest impact in senior living over the next say, five years? And then if you could debunk one of the big misconceptions about AI in senior living, what would it be?

George Netscher: Okay. Ooh, I'm gonna fire from the hip a little here. So you said in the next five years, the two places where AI will have the biggest impact. Okay. The first one is gonna be indirect. Okay. I think that AI is actually going to displace a lot of jobs in other industries. Okay. So I do think, for instance, I live in California.

I love, high speed rail. I have a hundred percent. I'm a hundred percent sure, and you can play this recording in the future. We're gonna have full self-driving cars before we have high speed rail in California. [00:26:00] Unfortunately. And so maybe all of that investment will look back and be like, wow, why did we spend all of that money?

So the what are some of the implications of that? What happens if we don't have all the people that are driving for Uber or truck drivers or things like that? And most of them probably don't want to be caregivers. But there's, I think we're gonna have a fraction of them that do. And I think that a lot of the labor challenges that this space faces are actually the jobs that are like the hardest to replace, where you're like, high degree of like social connection high degree of like hands-on, personal touch.

And so I think that AI is gonna impact a lot of other industries more than it impacts the direct care in this industry, which will mean that actually there's labor that's coming from the outside and thinking about how we can, there's so much opportunity for fulfillment in this industry, creating pathways for retraining and all of that from non-traditional sources.

I think it's gonna be really important over the next five years. I think the other place will [00:27:00] be obviously the things that I've spent time just obsessing about is like companies like safely you, where it's I do think we're doing so much driving in the rear view mirror today that we're gonna look back in five years and be like, wait, what?

I didn't have a way to know like where my staff are spending their time. In a day, like something I see in this industry is we have no closed feedback loops. We like do training and then we hope it's happening, but we don't have a way to like actually get feedback and get data in real time proactively to make leading, like leading indicators that can help us make decisions we find out after the fact and then it can often be like much harder to support it.

So things like getting in front of staff turnover, there's like a ton of opportunity there and I think we'll look back. In the same way, I look back at what we did with Falls and I think our customers today would tell you like, one of the reasons we get expanded for instance, is they have people that we're working in a community that use safely and move to one of their other communities.

And they're like, wait, what? This is [00:28:00] how we do things. Like we still, when we find a resident on the ground, we send them to the emergency room and it feels like really antiquated and like unethical, candidly. We're gonna feel that way with I think a lot of other things. In five years. In the same way, by the way, I believe that was self-driving cars to keep using that as an analogy.

We, like 50 years from now, will look back and be like, wait, we let people drive. Do you know people do you know how much they were like drinking and texting and couldn't we see all the statistics around deaths? And it's yeah. But it was convenient. Like we needed to get to work.

Like we're gonna, we're gonna look back and not believe that we actually let people drive. And I think we'll have a lot of those look backs in this industry. I can't believe we did it that way. Wow. 

Matt Reiners: I love that. Your second question was second. Yeah.

If you could debunk one of the biggest misconceptions about AI and senior living, what would it be?

George Netscher: I would actually say that this is just me firing from the hip again, but I think that the, I think it's a misconception to say that senior living is behind. 

Like we might [00:29:00] have historically thought of senior living as being like, slow to adopt or whatever, but we are totally proving that this is actually the industry that is, has the most ability to buy and interest in buying and like where you can actually grow for solutions that really do work, that you can grow at venture backable rates in this space.

And when I was first starting the company, I would always have investors ask me like, why aren't you supporting home care? Like clearly the total addressable market is much bigger there and, but no one could figure out how you can actually like scale at Venable back venture backable rates in home care where you need to be growing at a certain pace.

And what we've shown is that this industry totally will, if you have something that really helps start in a couple communities. Go portfolio wide, things like that, that there is a pathway here. And the more and more of us that the fact that we've now raised over a hundred million dollars is crazy to say out loud, but it creates a pathway for capital and for entrepreneurs.

And the more of us that push that boundary further and further, the more capital [00:30:00] will be attracted to oh. If you want to support the aging population, there's clearly, massive need there, but it was always how do you actually figure out the go-to market to support that need? And we're showing that through senior living is actually the path.

And I think the further we go there, I do think it's this is gonna be the place, like senior living's gonna be the place where aging innovation comes and then might go other places and blah, blah, blah. But I think it's a really golden time for the industry over the next few years. 

Matt Reiners: I love that. And then I guess I lied.

Last question. If people wanna learn more about you or safely you, George, where would you direct them to checkout? 

George Netscher: You can always follow me on LinkedIn or send me on LinkedIn connection. I'm always not the fastest at responding to that kind of thing. But we keep our website up to date. I would follow the company on LinkedIn and we're always pushing updates there.

And then keep an eye out for us at conferences and events. Come say hi. We always love connecting with folks and especially these kind of conversations are really fun to have at the conferences where you can brainstorm a little bit together. I love it. Awesome. George, keep 

Matt Reiners: up the awesome work.

It'll be great to continue to see you [00:31:00] on this entrepreneurial path, just improving quality of life. I can't think of anything better of, building companies and helping people along the way. So kudos to you and your team. Keep it up, my friend. 

George Netscher: Yeah, thank you.