Transforming Home Care: Seth Sternberg on Honor, Home Instead, and the “Logistics” of Aging | The AgeTech Podcast S5E15
If you work in our industry, there is one phrase you hear constantly: “caregiver shortage.” It feels like the ultimate bottleneck to scaling home care services. But what if we’ve been diagnosing the problem wrong this whole time? In this episode, I’m joined by Seth Sternberg, the Co-Founder and CEO of Honor, who argues that what we perceive as a supply crisis is actually a logistics failure—one that big data is uniquely positioned to solve . We dig deep into Honor’s massive acquisition of Home Instead and how they are marrying Silicon Valley tech with the world’s largest home care franchise network. Seth also shares some fascinating insights on how machine learning can predict employee churn before it happens and why he believes the future of aging in place will inevitably involve robotics.
Catch the full conversation on Youtube, Spotify, Apple Podcasts, or scroll down for the transcript (auto-generated, so pardon any oddities – the bots are still learning!)
Keren Etkin: Seth welcome to the show.
Seth Sternberg: Thanks for having me, Karen. Good to see you.
Keren Etkin: Good to see you too. So with your permission, I’d like to start us off with a trip down memory lane. So the year is 2014. and your co-founders slash CTO from your previous startup, which was acquired by Google, had just finished two years at Google, and you
Seth Sternberg: Yep.
Keren Etkin: to embark on a new adventure.
Seth Sternberg: Yep.
Keren Etkin: I assume at that time you could have raised funding for any startup came to mind and you land on elder care.
Seth Sternberg: Yeah,
Keren Etkin: Why.
Seth Sternberg: great question. We had three worlds and we spent 18 months figuring out what we would work on that fit in those three world worlds. It was, look a human in the eye and know you’d make their life fundamentally better. Millions of humans, not just one. Had to be a hard problem. And the reason for hard problem was that we were second time founders, so we knew how to hire people, raise money, kind of build a company.
We didn’t have to relearn that stuff so we could kind of take that energy and put it into a hard problem. Right. And that meant that we could solve something that maybe would be harder to be solved otherwise. So that was, that was the thinking at the time We. Had actually when during the 18 months when we were thinking about what to do, we put kind of like, you know, older their adults on the list of a bajillion things that we were kind of thinking through.
And then I just ran into an issue with my mother that was really small, but I’m kind of one of those like paranoid people. I guess only the paranoid child survive according to Andy Grove. And I kind of went into what does this mean for the future? Right? If this is happening today with my mother, what happens in five years or 10 years?
And that’s when we got a lot more serious about what could we do to really help with the kind of aging dynamic worldwide.
Keren Etkin: Wonderful. And at that time honor, you decided to, you were going to run basically a tech enabled home care company, and you actually opened a home care agency in the Bay Area.
Seth Sternberg: Yeah, I mean, here’s the thing. When you’re a founder, you usually go into a space that you have some kind of passion about individually. Like it has to be a problem that you actually care about or you don’t do a good job, but you usually know very little about it. For real because you’re going into it with kind of a set of skills, which theoretically can be applied to a problem, but you haven’t operated in that space.
You just say like, look, I think things could be better there. And you have to go learn from first principles and there’s no better first principle way to do it than to just go launch the thing. What I think. People don’t always realize, especially with these more operational style startups, is you don’t build a lot of technology first.
Rather, you just build like the service because you need to see what kind of technology you actually should build to make the world a better place. So you launch you, you know? You know, for example, when you launch a home care company, well, home care companies do not skip. You don’t. Actually know why You only have kind of done your research or seen how other people have done it.
But until you experience it yourself, you don’t really know the nut, the little nitty gritty stuff about why it actually doesn’t scale. And then, you know, once you’ve done it, you can say, oh, okay, it’s like these three things. This is the problem I have to solve. And then that’s where you start building the technology to address that like very clear thing that you find.
Keren Etkin: And what were some of the counterintuitive things that you learned along the way that that surprised you?
Seth Sternberg: Yeah, I mean there were a lot of ’em. I, I think one thing that is, I say this to people all the time and they still don’t believe me and they don’t, I actually do not think people understand what I’m about to say. But I.
Keren Etkin: But
Seth Sternberg: There is, in home care, people tend to say there’s a supply problem. Like what they love to say is, I cannot grow because I cannot find enough care pros.
And that is just not correct. Like that is just patently wrong. And it’s wrong because no one in this industry has more than like. 10% of their local market, which means that for every 10 people that they employ, let’s say they’re the biggest one in their market, and they have 10% for every 10 people they employ, someone else employs 90 people.
And there’s no job market on like Planet Earth where you don’t have to kind of find employees from competitors and convince ’em to work for you. So to say, oh, well I can’t find care pros. It’s just like patently absurd. Okay, because no one is dominant in this space. It’s so hyper fragmented. So then you have to ask yourself, why does everybody think they’re supply constraint?
The answer is that they’re actually logistics constraint. They, if you think about a kind of classic two-sided marketplace, which at the end of the day home care is you have people who need care, you have people who supply care. How do we bring them together? What people miss is that, well, there’s this third part of the two-sided marketplace, which is the logistics that bring them together.
And it is those logistics that actually stop people’s ability to scale. They perceive them as. I can’t find enough supply, but it’s actually a logistics and operations challenge.
Keren Etkin: And that logistics and operations challenge is what you try to solve with technology in the backend.
Seth Sternberg: Well, once we saw, oh wow. Like that’s the problem. Then we started to build technology around how do you make those logistics work? And. To, if you create the technology that makes the logistics work, you end up getting better care, you end up getting better jobs. So like solving that logistics challenge creates a better human experience for both the employee and for the client.
But that is where we ended up kind of focusing the dominant, you know, kind of technology effort that we had. There are lots of other surprise, like, so you know, within that, let’s now talk logistics for a second. In there. There’s lots and lots and lots of surprising stuff, which should not surprise you, so to speak, because that is the thing that’s broken.
So for example, you know, we launch and we discover that, you know, a Care Pro will say today I would love to take that jump. Then tomorrow they bail on the job and like they do it again and again and again. Like all the care pros, not one. It’s like a systemic thing and you have to figure out why do so many people say, I want a job, but then tomorrow they bail on the job.
Like they never wanted it, even though the day before they said, yes, I really want it. That was a surprising thing to me. Right? Like and, and that is actually a logistics challenge. Note that that would make you feel like you were supply constrained, right? If you hired these people who then said, S Psych, I don’t really wanna take that job.
That would make you feel supply constrained. But the problem is actually logistics in there, not supply constraints.
Keren Etkin: How do you solve that with technology? I mean, it’s a, sounds to me like a very human problem that people just don’t, don’t show up for work.
Seth Sternberg: Right. So we’ve been doing AI for like, literally since the beginning. And when I say AI, there, I kind of mean what people refer to as ML machine learning. So it’s like big data. Kind of used data from the past to predict the future. And one of the things you can do with that is to figure out what are the patterns When someone says I want it, and bails versus what are the patterns when someone says, I want it and don’t bail.
And then you can say, okay, I think that I can recognize the pattern because I’ve got like, you know, 10,000 examples, you know, 5,000 in which they bailed, 5,000 in which they didn’t bail. And I can see these, you know, you can look at one nice thing about machines is they can see lots of things humans cannot see.
They can see, you know, time on. You know, patterns in how they applied you know, uh, how far do they scroll a screen, like there’s all this data that you can get that humans just literally cannot interpret. And the machine can kind of say, Hey, you know, I think that in the cases where people bail, this is the pattern.
And in the people where they don’t bail, that’s the pattern. And so then you can say, okay, well if I now know the pattern where they don’t bail, and I know the pattern where they do bail. I can try to replicate into the future the pattern where they don’t bail, and I can know that this one looks like they are gonna bail and I can proactively stop it or redirect it into a pattern where they will actually stick.
So that, that, that is effectively what you do with ml, right? You do with ai to create like this better human outcome. Uh, and that worked, right? Like we, we were able to identify a whole bunch of patterns and then kind of goal seek into the patterns that, uh, had successful outcomes.
Keren Etkin: It sounds to me like you had to accumulate a significant amount of data in order to, to make these predictions.
Seth Sternberg: That’s like,
Keren Etkin: were you able to, to collect all that data?
Seth Sternberg: yeah. So, so you know, when we were. I think we were doing like $10 million in revenue. This is probably a month, like 18 or something. And no, it was like month 12 and. I at the, I was like, wow, this is really messy. And at the time I was calling it a non-tech tractable problem, I was like, this is actually probably just screwed.
Because the heterogeneity and the variability in the system that we were seeing was just so wild. It was like, wow, this may thing may be forever a non-scalable business. And.
And.
We said at that point, so when we had about 10 million in revenue, you know, we have enough data, this ML thing is starting to happen.
Maybe we could apply ML to the data. Maybe it’ll work. Like, maybe it’ll help us see the patterns. So that’s, it was because we started the agency and ran the agency ourselves, kind of by human. But importantly, we had structured the data flows such that we could capture. And process the data flows that enabled us to then start to see some of the patterns and start to change the way that we had designed the system for staffing.
But you know, your question’s important. I know you know your question’s important, but it’s really important for the audience, which is, um, you know, you can have a lot of data in your system, but if you are not properly capturing it, then it’s just all going to waste. Like you can’t do anything with it. So you need to architect your company and your systems and your processes such that you’re capturing every tiny little bit of data that you put, even if you think it’s data that is useless.
Because it may actually be that that data is useful when you stick it into kind of a massive regression data regression and you see, oh, hey, like there’s actually signal in that one. I never would’ve thought it, but, but there is. So that, that data captures like really, really important.
Keren Etkin: Would you say that being a second time founder helped you structure everything properly? Foundationally
Seth Sternberg: Oh yeah. I mean like, you know, you hear stories if you’re in, you know, tech land, you hear stories all the time about companies that just don’t actually have the right data structures from kind of day one. And that’s just torture because then you have to build it in like, you know, example, when we bought home instead.
Home instead was not structured as a tech company at all. So we had to go retroactively, like retrofit, you know, tech level data aggregation and capture onto something that was not built in tech, you know, from the ground up to be kind of, you know, properly kind of. Organized in data land. So yeah, no, it’s like, it’s it, people make that mistake in, in tech land and entrepreneur land.
Uh, as a second time entrepreneur, we knew to be able to honor the right way, day one. And this is, you know, this is one of the things that makes it very hard for legacy companies to become tech companies, right? Because they may have a lot of volume and a lot of scale. But if they’re not capturing the benefit of that volume and scale then it’s hard to actually use it to kind of morph and adapt and create better product.
Innovate.
Keren Etkin: Absolutely. Well, in all fairness, when companies like Home instead were first founded, no one had thought that Big Data and ML would become a thing,
Seth Sternberg: Totally. I.
Keren Etkin: think about it in advance. Well, speaking about the, the Home instead acquisition, was that something that you had. Set out to do. When you were just starting out, did you and Sandy sit in a room and say, one day we will become the biggest home care franchise the country, possibly globally?
Seth Sternberg: yeah, so I mean we, we sat in a room and we said, actually said. Home care is a way to solve, change the way society cares for older adults because you, you spend a long time in people’s homes and you really help them live their lives. And if we could create a system where you can programmatically get.
Trained individuals into the homes of older adults who can work with them. There’s so much you could do. So the aspiration was not kinda like a scale aspiration. The aspiration was a change, a societal problem, aspiration. And I think that like nuance is important because if you’re, I, I really do think that at the end of the day, the companies that have a founder and then a leadership team built around that founder.
That truly come at it from caring about a mission they outperform, right? Because they’re just all about that mission. And the mission usually is around some, like, personal problem. It’s not around some kind of like, I gotta make money or I gotta like, achieve a certain number of scale or whatever. The,
The.
the other thing that kind of flows from that.
Mission orientation is that you understand what is within scope. And you understand what is the right move versus the wrong move. So when we bought home instead, it was super obvious to us. It was like, Hey, we’ve got this AI platform. It works. It can provide home care at scale. We want now we want things we don’t have, like we want a single brand.
We want a lot more scale as fast as we can. We want to embed that AI into that more scale. So home instead made a lot of sense. It was the biggest network at the time, still is the biggest network by far. It was the best run network. Like we did lots of diligence on it. When we bought it, it was, you know, by far the best kind of run, hard li largest revenue per location.
Like all these metrics kind of corroborated into this is like the best one. So if you’re going to buy one, buy the best one. And that’s what exactly what we did. And it was just, you know. A significant accelerant on the mission. Um, there is now this thing happening in Silicon Valley. In fact, I just got invited to a dinner, like about it in, in a couple of weeks.
There’s this, since we did that, we did that four years ago. Silicon Valley is now completely caught on to this approach. So there’s this basically P-A-I-P-E uh, symbiosis that. Silicon Valley writ large is now realized in many industries where people are saying, oh, we could accelerate the rate of change if we marry up AI capabilities with things that already have scale.
And then you can take that thing that already has scale and get it a lot more scale by making it more efficient and improving its product using ai. So that’s happening right now across multiple industries, accounting software, um, a. Home Owner Association software, you know, like multiple of them. you’ve now got kind of VCs basically going out and funding the AI platform and funding the purchase of the kind of old world business and marrying the two, uh, in order to make change faster, basically.
Keren Etkin: Speaking of marrying the two four years ago, honor had a few hundred employees and home instead had hundreds of thousands. was it like merging these two companies with very two different cultures and very, very different companies?
Seth Sternberg: Yeah. Yeah, so actually in their HQs oddly they actually had about the same number of employees. So Home instead was structured as a franchise. So it had kind of a corporate HQ staff, and then it had, you know, we had our kind of corp staff and then they had their franchise network and we had our care pros who, who were our W twos in the field, which they actually did not have.
So what was interesting is we were an operating company at the time, running Home Care, and they were a franchiser. Running a franchise network, but the franchise network ran the home care. So there was this ironic difference in, um, kind of, the ways in which we understood the world because home instead, the franchisor had done a very good job of creating a brand around home care and then a system for their franchisees to use to go out and become the best franchisees in the market.
Then we had created this AI system that was literally the only one that could actually scale non-medical, private pay, home care using technology kind of centrally via ai. So marrying those up when you kind of went through what are they strong at, what are we strong at, what are they, we get, you know, what are we weak at?
It was almost a perfect match of like where they were strong, we were weak. Like we had never focused on brand. They really had focused on brand. You know, so great. We inherited. They’re focused on brand, they had not focused on operations, we had focused on operations. So when you go through and you kind of marry it up and you say, wow, the things we’re good at, they’re bad at the things they’re good at, we’re bad at, that’s like a perfect match.
And then, so that’s kind of piece one, piece two, which is, you know, unique is, uh, bringing together a team that is fundamentally kind of from. Tech land, right? Like startup land, tech land and approaches things from that perspective versus a team that really fundamentally came from kind of franchising.
Like that was the core of the homestead team. That was pretty different, right? Like that was the place where we really had to learn, you know, how do you marry those two and how do you understand the two and what are the best practices of each? Um, but I do think that there’s this, you know. There’s this reality that as we go forward in the world, I don’t think there is a single company on planet earth at this point that of scale that isn’t either today a tech company or won’t become a tech company.
And the reason I say that is when you look at the power of this stuff that we are now building in technology, there’s just no product. Where if you apply technology to it the right way, it won’t become both better and cheaper. And that means that the companies that do not themselves know how to, let’s call it tech eyes, their product just definitionally will be beaten by the company that figures out how to do it.
And in a lot of ways, AI was the last great. Kind of like unlock to that. And, and the reason, and this is a thing that people, you know, especially in healthcare and any kind of care thing, I think need to understand and don’t fully completely at core understand yet is so, so old world pre ai, pre ml. So pre what we were doing 10 years ago, technology was not good at heterogeneity.
Technology was good at, I need to do the same thing again and again and again, and everything’s a widget and everything’s the same widget, and I can do that a bajillion times. And people in kind of healthcare and home care love to say, well, this is a human service. And humans are heterogeneous and humans are unique.
And so you can’t treat them like widgets. And that is true. But what is also true is that AI is better at personalization than humans are because the, the unique unlock with ai. Is that we now have a way to train the machine to learn who the care pro is, to learn who the client is at a level that a human could never do, because it will look for all these signals that the human would just kind of miss, or the human wouldn’t be able to keep 10,000 humans in their head.
Maybe they could do it for 10 humans, but not 10,000 humans, and then that person will simply break. So the advent of AI is kind of, it’s really like the last great unlock, I think for tech ing kind of any given product or service in in the world because it allows for this one last thing that we really couldn’t do before in technology, which is like this deep personalization that we now can.
Keren Etkin: Amazing. And how does that personalization meet the care pros and the care recipients at home when they have like a one-on-one interaction? How do they experience home instead, honor differently than if they were to work slash receive the service from any other home care company.
Seth Sternberg: So what they should feel is that the service is simply better and they’re not gonna quite understand why. And that that is totally fine. Like when people get in a car and drive a car, they don’t think about it as like a big technology like project. But that’s what a car is like. You can’t build a car. I can’t build a car.
A few people can but a car. Is technology made to be so, so, so simple that essentially anyone can operate one. And when you look at the experience of one of our care pros today, what we want is for them to feel like holy crap, honor is just by far the best employer for me possible. And if I can possibly get any job in home care, I want it to be, you know, on honors platform via homestead.
Like that is what we want ’em to think. And the way that. Happens for them is basically the AI is looking at what is it that you actually want from your job. It knows all these different things that, like in the past, people who are care pros have wanted from their job, and it can kind of say, Ooh, like I think you’re probably one of these people who wants these very particular things.
I am gonna work really hard to give you those things. So your job becomes hyper-personalized to you, and that makes it the best job possible for you. And then that means you’re going to do a better job with your clients. Then for clients, what you want them to feel is like, wow, this is just an freaking amazing Care Pro.
And the reason they feel like it’s a fricking amazing Care Pro is basically two things. One is that Care Pro is a good place because of what I just described, right? We have cared for the Care Pro, and they now feel like they have a job that really suits them. And the technology has looked at that client and said, you know, you look a lot like these other clients I’ve seen in the past.
When you start, I wanna learn about you as you use a service, but I am pretty sure that this is the stuff that you really want as you know, client and I can uniquely tailor the service to you and your desires. And, and that is where, you know, people, both sides, the clients and the care pros just win.
Keren Etkin: Wonderful. And when you speak to franchise owners, if I understand correctly, the way that the franchise system works is. They can use their own discretion as to what software they use.
Seth Sternberg: Mm-hmm.
Keren Etkin: I assume you’ve implemented honor software across a franchise. Do you, how much convincing do you have to do
Seth Sternberg: Mm-hmm.
Keren Etkin: to get
Seth Sternberg: Yeah.
Keren Etkin: to adopt it
Seth Sternberg: Inside the franchise system, we have different levels of software. So we have from. Kind of demand software, business intelligence software. We’ve actually built multiple different tools for the franchisees. All the way through. They can go into what we call care platform is, which is when the AI system is really running the, you know, the day-to-day operations for them.
And it really depends on that franchisee, what they want to do. Some of them really early on, like the moment we got there we’re like, yep, I want it. I’m all in on the ai. Let’s go. Others who were. More like wait and see. They come on today. Others who are, might be in markets where we haven’t, like, we actually have not yet built the full AI system to be able to work in lots of kinds of markets.
They’re using demand software, they’re using business intelligence software. They’re using a bunch of other tools that we have given them. Um, we have this really cool tool we actually are releasing to network right now. But. But they’re not having their logistics run for them, but they are getting a lot of business intelligence from some other kind of data gathering that we’ve given them.
So, it just depends on that franchisee and, you know, where they are in their journey. But all of them benefit from some kinds of software in one way or another. And the big, you know, like I said before, the big, big thing that we did. When we bought home instead, day one was start to get the data collection right.
And this is like really a, you know, if we were gonna harp on anything for people to learn is kind of coming outta, this is data collection. Because. If you don’t know what’s actually happening in your system at like a very fundamental level, you just don’t know what to do next. So,
Keren Etkin: Sorry.
Seth Sternberg: know, early on when we bought home, instead there were tons of requests from the owners, can you build this?
Can we you build that and blah, blah, blah. And we’re like, we don’t know if we should, like, we, we don’t know what we should do until we have actually done this first step, which is let’s get all the data actually flowing so we can actually understand what’s happening in the system. Uh. Then start using that data to make informed decisions about what to build next.
Keren Etkin: When you got this data from the franchise at scale, which I
Seth Sternberg: Mm-hmm.
Keren Etkin: huge amount of data, much more than you’ve previously had,
Seth Sternberg: Mm-hmm.
Keren Etkin: it, were there some things that surprised you that you decided to build that you never would’ve expected?
Seth Sternberg: yeah, I mean, one of the things that I think was pretty interesting, i’m gonna say this in kind of a provocative way, intentionally, is people did not know why their businesses were trajectory the way they were. So what I mean by that is, uh. Home care is a very hard, there are lots of reasons why home care agencies do not scale, like individual agencies do not scale.
And one of them is that they are so complicated. There’s so many actors in the system from the, you know, people receiving care to their loved ones, right? The sons and daughters and neighbors, or whoever. The care pros, the office staff. How does this, what does this staffer do versus what does that staffer do?
The recruiting of the care pros, it, you know, the, the kind of healthcare ecosystem that you’re acting with. It is so unbelievably complicated. And let’s say that you make a change today in how you operate. You don’t even know the effect of that change for months because you’ve got a bunch of clients who are just with you, who are gonna stay with you.
You know, kind of the rate at which new clients join is not super fast. It’s just, it’s a very high LTV thing, but it’s not super fast. So you don’t know the, like, you don’t actually know if what you’re doing today is going traject to trajectory eyes. You kind of like up. Flat or down from where you currently are.
And so literally just the act of knowing the direction of travel of your business for real, not the history, but where you’re going to go, was a very hard thing to solve for people and unsolved in this space. But now at least for our franchisees fully solved.
Keren Etkin: That is awesome. well, the last question I wanna ask you is sort of about pricing of home care. There’s a lot of discussion about the home care prices going up every year and it not being sustainable for many people. And since this is a, a service business at the end of the day, but if you marry AI, then. In theory, you could somehow make it more affordable. What’s your take on this? Is that ever going to be a thing or is the trajectory of home care prices simply gonna go up
Seth Sternberg: Yeah.
Keren Etkin: until
Seth Sternberg: So what’s clearly gonna go up for forever is people’s wages, right? Like the wages of everyone, right? Just inflation go up. And so what you’re not going to be able to do is take a 100% human service where someone, you know, kind of needs X number of hours and magically make that hour cheaper if it’s being served completely by a human.
Um. I’ve been pretty vocal about that, I think. I think the future. Is marrying kind of devices and humans together in order to make it such that you can have a little bit less human in the equation of how you’re getting cared for at your home. But then the combination of the two can mean that many, many, many more people can afford to actually stay in their home.
Uh, that’s really hard to actually accomplish. So, you know, the first thing you need. Is a network that’s controlled by machine. Oh, wait a second. I know someone who has that. And the next thing that you need is you need the right kind of data intelligence and devices in the home that let you know when those humans need to be there what they need to do, et cetera.
So. I do think the direction of travel is actually going to be prices coming down, but not because you’re gonna magically pay people less. You’re not, you’re actually gonna pay people more, um, but rather because you’re gonna do this really great job. Of marrying human capability with machine capability to make the price point at which people can stay in their homes substantially lower.
And that, I think, you know, we accomplished that. I’ll be satisfied. I’m, I’m like the never satisfied personality. So, so we get that one done. I’ll feel pretty good about what we’ve done.
Keren Etkin: It sounds like a win-win situation and care pros are getting paid more and the clients have to pay less. I mean,
Seth Sternberg: That would be a win-win for everybody. Yep.
Keren Etkin: that would be a win-win. And we definitely know that, like here at Pros deserve be compensated for the work that they do because it does really benefit society and it’s like one of the hardest jobs there is.
It’s not just very high demand, it’s just really, really difficult physically, emotionally,
Seth Sternberg: Yeah, it’s really fun when you go talk to, when you go to a home where there’s a care pro. When you talk to care pros, meet them, like they’re really, really awesome people. Like they’re doing this really, really hard job. Um. Like as recently at our, our UK convention for example, and they just, in the uk we do just like we do in America Care Pro of the Year, and the finalists come and you get to see them and it’s, it’s just so much fun.
So yes, they definitely deserve all the rewards that we can give them.
Keren Etkin: Absolutely. So that was actually my last question. Is there anything that we didn’t talk about that you’d like to add? Any call to action to people in the audience?
Seth Sternberg: no. I mean, you know, I think we talked about a lot of good stuff. I think the, you know, like I said, if I’m gonna give anybody, I think there’s like. Two things that I, just as I observe people in this space in general, you know, one is just fundamentally get, get on top of your data. It really matters. Uh, you can’t use third party software if you don’t have your data collection, right?
Because, like, it, it’ll be gar, what we’ve called garbage in, garbage out. It won’t do anything for you. And then I think the, you know, the other one is, is just to be on top of the. Tech game in general. I, I just still hear in this industry so much of, well, technology’s not really come, gonna come for this one and it’s just not true.
It’s just wrong. You know, I was in a room a little while ago and, uh, it was a room of a lot of the leaders, I guess, of home care. And I asked people, how many of you have you seen the video of the Tesla bot? And I thought everybody would raise their hand and like, like maybe 2% of the room raised their hand.
And like Tesla has put out a video of that robot folding clothes. I mean, if, if there is anything that would seem on the surface to be a threat to home care, it would be a video of a robot folding clothes. so. You know, people really need to, leaders in this space need to understand that technology is, is real and coming.
And they need to take advantage of it. Like they can win with technology or they can be replaced with technology. So it, it, you, I would argue you probably wanna be on the side of winning with technology. So, that might be the call to action. I would give people.
Keren Etkin: Absolutely, and I believe that robotics is a huge, huge opportunity for the home care industry. I mean,
Seth Sternberg: Yeah, without any doubt.
Keren Etkin: Seth, thank you so much for joining me on the show today. It was an absolute pleasure chatting with you.
Seth Sternberg: Cool, Karen, thanks for having me. Appreciate it.