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0:00:45 Welcome to First Time Founders.
0:00:47 I’m Ed Elson.
0:00:56 In the U.S., about 240 million 911 calls are made each year, and 85% of them come from mobile phones.
0:01:01 However, the underlying technology is still designed for a world of landlines.
0:01:05 My next guest is working to modernize that outdated system.
0:01:12 His company created a platform that uses AI-powered tools to help 911 responders improve outcomes in emergencies,
0:01:15 with features like real-time translation for non-English calls,
0:01:19 and two-way audio and text communication between callers and dispatchers.
0:01:28 Since launching in 2019, the company has raised $145 million, including a recent Series C round led by General Catalyst.
0:01:32 With partnerships spanning nearly 1,000 public safety agencies in 49 states,
0:01:38 this company is quickly transforming how 911 responders manage emergencies in America.
0:01:44 This is my conversation with Michael Chime, CEO and co-founder of Prepared.
0:01:47 Michael, good to have you on.
0:01:48 Good to be here. Thanks, Ed.
0:01:53 So, I learned about you through a friend of mine, Christine Choi, who’s an investor at M13,
0:01:57 and they’re one of your early investors.
0:02:05 And we were just sort of having this conversation about how AI can make real, tangible impacts on society.
0:02:08 We keep on hearing about how AI is going to change everything.
0:02:18 And she mentioned this company in the portfolio that is basically using AI to save lives in the world of first responders.
0:02:21 And that company is your company, Prepared.
0:02:24 So, start us off here.
0:02:28 Tell us what is Prepared, and what does Prepared do?
0:02:29 Well, we do exactly that, Ed.
0:02:38 You can think of us as an AI-assistive layer on top of 911 calls that are processed across the country every single day.
0:02:47 And the role we’re trying to play is help those operators who are managing those calls process them better and faster.
0:02:48 We do that in a couple ways.
0:02:56 So, one is taking the mundane load off of their plate so they can focus on the most important portions of that job.
0:03:01 And then fill in, like, a lot of AI opportunities, limitations that we as people have.
0:03:06 So, some, like, practical examples would be like, hey, does a person really need to spend time on a noise complaint?
0:03:09 When waiting on the line, there’s a cardiac arrest call.
0:03:11 We can reprioritize those calls.
0:03:14 Or, you know, a human can’t speak 30 languages.
0:03:15 Well, technology can.
0:03:18 We can translate calls in real time and fill in those gaps.
0:03:22 And so, we sit next to those operators and help them process calls faster, better.
0:03:24 It seems so obvious.
0:03:27 And this is what I love about this company and what you’re doing.
0:03:31 It’s just, like, the most obvious improvement ever.
0:03:38 But tell me more about the problems with the traditional 911 system.
0:03:41 Like, what are the main issues that these call centers are dealing with?
0:03:46 I know I said they’re outdated, but what exactly makes them so outdated?
0:03:49 I think there’s two core challenges that centers are facing.
0:03:55 The first is that you have undue burden placed on the human.
0:04:01 And when I say the human, I’m certainly the caller is going through the worst situation of their life.
0:04:08 But also, that operator that’s taking the call behind the scenes that you often don’t think about is managing a lot, right?
0:04:16 And a trend that, you know, often you don’t think about is more 911 calls are going into our cities than ever.
0:04:23 Yet, the amount of operators, 911 operators, staffing levels is going down, right?
0:04:25 So, you have more emergencies and less people.
0:04:26 And that’s creating tax.
0:04:28 That’s creating burden.
0:04:32 So, that’s one challenge that we’re facing is how do we manage that volume?
0:04:40 The second is those operators are tasked to do that already incredibly difficult job with a lot of fragmented tools.
0:04:47 They’ll have systems that handle the actual delivery and routing of the call and answering of that call.
0:04:49 They’ll have separate systems that they need to punch the information in.
0:04:54 They’ll have a separate system that has location for them, a separate system that says their protocols.
0:05:02 It’s not uncommon to walk into a center and see in front of that operator eight to ten screens that they’re utilizing and all managing at once.
0:05:11 And so, again, already difficult to manage that situation, but they’re tasked with tools that often work against that goal as opposed to for that goal.
0:05:17 And so, I think technology and something like AI could be very powerful on both fronts.
0:05:21 One, it can come in and help take the load off folks.
0:05:28 And then, you know, what it’s really good at is mass, you know, aggregation and real-time kind of processing of a lot of data.
0:05:32 Can we bring those fragmented tools together and actually make them more powerful as a result?
0:05:37 Yeah, one of the questions I was going to ask you is how well-resourced are these call centers?
0:05:47 And my assumption would be based on what I’ve seen when I go to the airport and I’m going through TSA or when I go renew my driver’s license, the DMV.
0:05:59 Basically, any government-operated anything, I have found from a consumer experience, there is just a feeling of incredible lack of resource.
0:06:00 It’s understaffed.
0:06:02 It’s overwhelmed.
0:06:10 And you said something there that sounds pretty concerning to me, which is that you mentioned, one, that crime’s going up.
0:06:11 That’s bad.
0:06:17 But two, that the actual number of people that are working at these call centers is going down.
0:06:19 Why is that happening?
0:06:22 There’s a couple of interesting things there that are non-obvious.
0:06:26 One is, on the first point, call volume is going up.
0:06:31 Doesn’t necessarily mean in every community that emergencies or crime is going up.
0:06:39 That 240 million calls that you talked about in your intro that go into centers every year, people don’t think about it.
0:06:40 They think of it as just emergencies.
0:06:44 Roughly 60% of that traffic is non-emergent.
0:06:51 It’s things like noise complaints, parking tickets, where 911 becomes this dumping ground or catch-all for really any requests in a city.
0:06:52 And that bogs down operators.
0:06:53 And that bogs down operators.
0:06:55 So it’s interesting.
0:06:57 It’s not linear that you’d see call volume rise in a community.
0:06:59 There’s certainly places where crime is going up.
0:07:04 But you’re seeing call volume rise in doesn’t necessarily mean crime is.
0:07:15 I actually think that’s such an opportunity for technology to help process situations better by just simply reallocating resource and being thoughtful of the prioritization of the call queue.
0:07:18 So that’s one interesting trend.
0:07:28 I would also say that this one’s probably more obvious, is that it’s just like you’re seeing the same staffing challenge in law enforcement and response broadly.
0:07:40 I think that, you know, post defund the police, the nobility of that job has just changed, right, in our society in a way that maybe is changing right now.
0:07:47 But I think there was certainly this like, hey, you know, when I was a kid in third grade classes, you know, people would say, what do you want to be when you grow up?
0:07:50 And a lot of hands would go up and say like, hey, you know, I want to be a cop.
0:07:56 And I think that the third grade class of today, many hands would go up, but not many would say.
0:07:57 They want to be a YouTuber now.
0:07:58 Yeah, exactly.
0:07:59 They want to be Jake Paul.
0:08:02 So I think that has shifted.
0:08:13 And I think that, you know, that job, I would say response broadly, emergency services broadly, when it suddenly becomes thankless, is not super exciting slash attractive, right?
0:08:16 It’s like very, very hard and arduous job.
0:08:17 You’re responding to emergencies.
0:08:21 Or if you’re a call taker, you know, and have a ton of empathy for it, that’s why we’re building for them.
0:08:27 It’s just like you’re consistently all day listening to people’s worst moments, right?
0:08:29 And you couple that with it being thankless.
0:08:34 You couple that with, you know, it being low paying in a lot of places.
0:08:43 They don’t, we can talk about this, but they, dispatchers in a lot of areas, and this is, we’re pushing for this and others are pushing for this to change, but they aren’t classified as first responders.
0:08:47 So they get separate benefits, they get separate pay than a first responder does.
0:08:49 In a lot of places, they’re treated like clerical workers.
0:08:59 So if you’re doing, you know, if you’re getting paid like clerical worker and the benefits of a clerical worker, but you got to listen to people’s worst moments, not a lot of people sign up for that gig.
0:09:03 And so you face it, it’s very, very hard to go and hire a dispatcher today.
0:09:05 Guys, it’s just so concerning to hear.
0:09:07 Just like a government level.
0:09:12 I mean, who actually runs 9-1-1?
0:09:15 Like what is this 9-1-1 entity?
0:09:16 I assume it’s not the federal government.
0:09:19 I’d assume it’s more state or more local.
0:09:22 But what actually is 9-1-1 and who are those people?
0:09:25 So the entity is called what they’ll call PSAPs or ECCs.
0:09:29 So public safety answering points, emergency communication centers.
0:09:31 That varies in each city.
0:09:42 So you’ll have some centers that the director of that 9-1-1 center reports directly up to the mayor in the same way the chief of police or the fire chief would.
0:09:44 And those folks are peers, right?
0:09:48 And they’re making decisions at that altitude together collectively, et cetera.
0:09:49 And 9-1-1 is kind of up-leveled.
0:09:56 You’ll have some areas where the 9-1-1 center or comm center will report up to the chief of police.
0:09:58 And then the chief will report up to the mayor.
0:10:01 Those are like the normal local settings.
0:10:06 You will have some scenarios where the state will be what’s called the primary PSAP.
0:10:11 And all 9-1-1 calls will go to that primary answering point.
0:10:12 It’s fairly rare.
0:10:13 There’s only a few states that have this.
0:10:18 And then every individual city will be a secondary PSAP.
0:10:26 So they’ll figure out kind of roughly what is the location of the emergency details and then triage that down to a secondary, more local PSAP.
0:10:32 So I’d say by and large, the structure is local cities are running 9-1-1 centers.
0:10:34 There are some states.
0:10:39 And then there’s a growing trend of consolidation where you will have regional 9-1-1 centers.
0:10:48 So like my hometown right outside of Cleveland, the 30 suburbs of Cleveland all regionalized to this one 9-1-1 center called Chagrin Valley Dispatch.
0:10:56 And there’ll be 30 police departments, roughly, that all centralize their one dispatch center and pool their money to do that collectively.
0:11:07 When I hear about how these call centers, these PSAPs are underfunded, I mean, you mentioned the part where there’s sort of the cultural shift where these jobs aren’t considered as noble as they used to be.
0:11:11 But I would assume that also there’s a level of the job doesn’t pay well enough.
0:11:13 And that’s why people aren’t taking the job.
0:11:21 And the sense that I get is these programs are either underfunded or the management isn’t good enough.
0:11:27 So I guess my question is sort of a dumb but provocative question.
0:11:36 It’s like, who do we blame for the fact that these 9-1-1 call centers are so behind the time, so behind on technology?
0:11:40 Meanwhile, AI is having the biggest moment in history.
0:11:44 Like, who’s to blame for this lack of innovation and this lack of investment?
0:11:45 There’s a lot there.
0:11:49 I think maybe we just tackle them one by one.
0:12:03 I think certainly funding is a part of this where, and there’s been federal pushes, things like NextGen 9-1-1, where the lion’s share of the funds would upgrade both the networks and the technology for 9-1-1 centers.
0:12:09 And there’s a consistent push by folks like us and folks in the community to have that move along, right?
0:12:14 I think that funding is a consistently hard thing for a lot of communities.
0:12:16 And so funding is a part of it.
0:12:19 I would say the second thing is, like, it’s very easy.
0:12:23 I was a total outsider when we first started, and I knew nothing about 9-1-1.
0:12:27 And it’s very easy, I think, to have that perspective of, like, oh, my goodness, how could this be this way?
0:12:28 It’s so wrong.
0:12:36 I think there’s a lot of, I’d say even most centers, very hardworking, want better, they want better technology.
0:12:39 But it is a tough trade.
0:12:45 So, like, if you’re a 9-1-1 center and you’re thinking about switching your core technology, that’s a big bet.
0:12:48 You can’t get that wrong as a director.
0:12:52 And if you do, people are going to be like, wait, you missed a 9-1-1 call?
0:12:55 You can’t move, false, and break things like you can in the other tech worlds.
0:12:56 Exactly.
0:13:04 And so, like, is, you know, I’m sitting, I’m like, if I’m sitting in that seat and I’m a director, would I trust the new startup to go and do this thing?
0:13:08 Or would I trust the system that I’ve used for 50 years that has never gone down?
0:13:15 So, some of that stagnation, I think, is for good reasons, is really well-intentioned that they are skeptical of new technology.
0:13:27 Not because they’re like, oh, I don’t want better for the community, but because, you know, redundancy and things, having a ton of fail-safes and me being, me believing the company’s really credible, causes me to be skeptical.
0:13:39 It’s such a good point, because it’s, I feel like we spend a lot of time ragging on government for not innovating, and we sort of assume that everyone in government, they’re just sort of stale and they don’t know what they’re doing or they’re lazy.
0:13:48 But you raise a really important point, which is, actually, government is so systemic to so many people’s lives, the stakes are higher.
0:13:58 So, if you try to innovate, you’re taking a way larger risk by trying to innovate at a call center than you would be for trying to create, like, another app on the app store.
0:14:01 The stakes are just so completely in different worlds.
0:14:02 It’s a really good point.
0:14:06 Can you talk more about the technology?
0:14:12 What does Prepared offer that these call centers didn’t have before?
0:14:14 You mentioned the translation.
0:14:17 Talk more about the actual technology that you’re providing.
0:14:18 It might be helpful, Ed, too.
0:14:22 I can give quick background on the story and how we got there, and I think it’ll hit the, is that useful?
0:14:22 Please.
0:14:27 I started working on the company about five years ago, which feels odd to say.
0:14:29 It feels like 15 years and five minutes all rolled into one.
0:14:32 But I was an undergrad at Yale, of all things.
0:14:34 So, people are like, why are you building in public safety out of school?
0:14:35 That’s a unique life choice.
0:14:36 So, I start there.
0:14:37 I tell the story.
0:14:41 I was always passionate about this industry, not knowing it’d be a company.
0:14:46 So, I grew up in a town right outside of where there was an active shooter event in 2012.
0:14:48 2012, I was 13 years old.
0:14:50 My town was very small, kind of blue collar.
0:14:53 And so, you just knew everyone and saw how it rippled.
0:14:58 And it was certainly the catalyst for me with college, thinking about those types of problems.
0:15:00 There was an active shooter event at your school.
0:15:02 I grew up in the community around it.
0:15:06 So, I was in elementary school, and the high school was, like, 10 miles away.
0:15:07 Yeah, I was in Jordan, Ohio.
0:15:11 And so, like, thankfully, I was not in the school.
0:15:17 But it was, like, you know, huge news nationally, and it was, like, certainly had an impact in the community.
0:15:24 And I would say, like, a kind of undertone, too, as I was growing up, I think my generation thought more about emergencies when they were going to school.
0:15:27 You know, like, when I was in high school, March for Our Lives was at its peak.
0:15:36 And so, when I got to college, there was just this groundswell, me and my classmates that turned out to be co-founders later, this groundswell, like, let’s just do something, right?
0:15:41 And that’s where we started, building technology for public safety use cases.
0:15:45 Our initial product was this simple app schools would use in emergencies.
0:15:53 And the idea was, you know, as opposed to a school when they had a threat using a walkie-talkie or PA system and trying to coordinate through that, they’d just use their phone.
0:15:55 You know, you press a button, everyone would get notified.
0:15:59 And to make a long story short, that’s how we got into 911.
0:16:02 And I think it’s a good segue into some of the problems that we saw.
0:16:07 Schools would come to me and say, hey, Mike, if I had an emergency, how would 911 get the data?
0:16:09 In the beginning, I’m like, I don’t know, just call them.
0:16:11 Wouldn’t anybody just call 911?
0:16:14 And they’re like, well, no, there’s all this data here I can’t verbalize.
0:16:17 Things like pictures, videos, text, what would I do?
0:16:18 And I was like, fascinating.
0:16:19 You’re right, that’s kind of interesting.
0:16:22 And if that is true, you’re unable to share that.
0:16:27 Wait, does that mean I’m better equipped to communicate to my friend right now in this moment than I would to 911?
0:16:28 That’s my worst moment.
0:16:29 Like, why is that the way of the world?
0:16:32 Because I started knocking on the door of 911 center.
0:16:33 And I started asking them.
0:16:35 And I’d say, like, hey, I have all this data.
0:16:37 I’d love to just give it to you.
0:16:38 I think it’s life-saving.
0:16:39 What’s the next step?
0:16:40 And they kind of sit me down.
0:16:43 I’m like, look, kid, I think you are pulling life-saving data, but I can’t take it in.
0:16:44 I’m like, what do you mean?
0:16:45 You just said it’s life-saving data.
0:16:46 Isn’t that the job, to save lives?
0:16:48 And then they walked me through the center.
0:16:53 And, you know, and I’ll start to get into the product and where it tries to address some of these things.
0:16:56 But I think this sets the stage for, like, what’s the status quo today?
0:16:56 And I walked through the center.
0:16:58 And I think part of it was a me thing.
0:17:01 I naively thought that I was going to see the best technology in the world.
0:17:03 That, you know, I was going to walk in.
0:17:05 It was going to be, like, military-classified stuff.
0:17:06 Yeah, the CIA.
0:17:06 Yeah.
0:17:07 Yeah, exactly.
0:17:10 Because, like, and it’s, like, a little bit of a tangent for me.
0:17:15 But I just believe, like, okay, like, government’s job at its core is to help people when they need help.
0:17:16 Like, I think that’s why.
0:17:20 Ultimately, you know, if done right, that’s where all taxpayer dollars should go.
0:17:24 Like, I need a package, UPS does it for me, like, that I need help, help me.
0:17:26 The epitome of that is I call 911.
0:17:28 I need the most help, you know, I’ve ever needed.
0:17:30 Please help me, right?
0:17:37 So I thought we just do that super well and come to find out that there’s decades-old tools there, right?
0:17:40 And so that was a real shift for me.
0:17:42 And I’m like, okay, we need to build better.
0:17:55 When we first started the products, now to get into your question, we just wanted to square up the fact that there was this device in our pockets that was very powerful in communicating that we weren’t using, right?
0:18:06 And so, like, when we started, the systems that 911 calls, and it’s still, by and large, the status quo today, the systems that, you know, 91130’s uses were built on the assumption the calls were landlines.
0:18:09 And so you would only get audio as an input, right?
0:18:16 So anything like picture, video, majority of places, text, was unable to be shared or received.
0:18:21 And so our initial product just allowed people to gather that info, right?
0:18:25 You would, as an operator, be able to send a link to a caller over text.
0:18:29 They’d click on that link, it’d open their browser, they’d hit a button, and you could be streaming.
0:18:37 You could see live through the person’s camera in real time instead of having to describe every single detail over the phone.
0:18:49 I think the best example of this was this use case that I read about that you guys explained, where there was a person on the other end of the call who, I think someone was choking, and they needed to do CPR.
0:18:56 And the person on the other end of the call is, like, trying to tell them how to administer CPR, but they’re only hearing it through the audio.
0:19:00 And then instead, they suddenly just like, oh, let’s just live stream it with the prepared product.
0:19:05 And they actually saw visually what they were doing wrong, and it saved their life.
0:19:09 Yeah, there’s examples of that that are happening every day.
0:19:14 And, like, the old adage, a picture is worth a thousand words, like, rings true.
0:19:19 And you don’t even have a thousand words when you’re in an emergency that you can articulate, and so it’s very valuable.
0:19:24 And then, like, 10x that with a video where you can see live what’s happening.
0:19:29 And so, you know, when you’re trying to administer CPR, that’s super valuable.
0:19:39 If there’s a structure of fire, right, I think oftentimes if you’ve never seen a fire before, and I’ve only seen a few, thankfully, so you’ve never seen a fire before, that’s going to be the biggest fire of your life.
0:19:41 You’re going to say, please send the entire department.
0:19:43 There’s a fire here in my kitchen.
0:19:48 And then people get there, and they, like, put it out very, very quickly, and they send the whole department.
0:19:57 Whereas if I had a video, I, as somebody that does that for a living, understand how to right-size a fire, I know what resources to actually send.
0:20:01 And if it’s huge, then I need to get a lot of people together and send that.
0:20:03 So it just allows you to better understand what’s happening on the other side.
0:20:05 Stay with us.
0:20:21 This is advertiser content from Mercury.
0:20:24 Miles Cole and I go way back.
0:20:32 This sounds made up, but when we lived together in college, he actually used to carry around a notebook with thousands of ideas for new businesses, classic entrepreneur.
0:20:39 These days, he is CEO and co-founder of MassLink, a company that wants to update how mass-taught lawsuits are financed.
0:20:40 I’ll let Miles explain.
0:20:47 A lot of times, lawyers will represent plaintiffs at no upfront costs on a contingency fee basis, saying, you don’t need to pay us anything.
0:20:48 We’re going to represent you.
0:20:52 But if we end up winning, we get a percentage of the settlement as our compensation.
0:20:56 But in order for that to happen, lawyers actually need financing to win the case.
0:20:58 And so that is the industry that we work in.
0:21:02 Yeah, I mean, you’re essentially trying to change how an entire industry is financed.
0:21:07 So I assume you think a lot about how to move money and how to move it efficiently.
0:21:11 And you bank with Mercury, which is sponsoring this podcast.
0:21:14 What has been your experience with banking as a startup?
0:21:17 We collect a lot of payments and we send out a lot of payments.
0:21:21 It’s sort of a pretty cash-flowing business compared to other startups.
0:21:25 And there’s this feature on Mercury I really like, which is just sending payment links.
0:21:32 It’s like two steps of just typing in the amount we need, typing in the vendor we need, and it creates this link that I can then just text or email to the vendor.
0:21:33 And they click on it and they can pay right away.
0:21:37 And so in my experience, using Mercury just makes life much easier.
0:21:41 Mercury wants to make it easier to pay and get paid.
0:21:43 See how at mercury.com.
0:21:45 Mercury, banking that does more.
0:21:49 Mercury is a financial technology company, not a bank.
0:21:53 Banking services provided by Choice Financial Group, Column N.A., and Evolve Bank & Trust.
0:21:54 Members FDIC.
0:22:07 We’re back with First Time Founders.
0:22:14 So, upgrading from audio to both audio to video, that’s been a big piece.
0:22:21 Translation, a lot of people calling and not being able to speak English and translating that.
0:22:24 What other technology are you implementing?
0:22:28 And my other big question here is, where is the AI part?
0:22:36 In what sense is this an AI company versus just a regular technology company, which is always sort of a murky distinction these days?
0:22:38 I’ll try to clean it up, get away at work.
0:22:43 I think about it as we sit atop this entire call flow.
0:22:48 And I’ll zoom out and say, our mission is that every emergency gets the best possible response.
0:22:56 So, we ask ourselves the counter question, what keeps us from best possible, and then we try to apply technology to do that better and faster.
0:23:10 So, the first thing, and this is a very clear application of AI that we think makes a lot of sense, is before we even think about handling that call, we should ask ourselves the question, what resource should handle that call?
0:23:17 So, roughly 60% of changes in certain areas of the 240 million calls are non-emergent.
0:23:42 We have now recently launched an AI agent that can handle those calls via voice, end-to-end, all the way from you have a dedicated 10-digit line, or we can take on an entire 311 number where our agent answers that call, handles the request in a low-latent and hopefully empathetic and thoughtful way.
0:23:57 All the way through the solutioning of that, whether that means entering it into their systems, filing a police report, and then it’ll send a message to the citizen in that center telling them what happens and where to follow up and track.
0:24:08 So, all the way from answering to solutioning that request, end-to-end, that alone, again, could offload 40-60% of traffic in cities, right?
0:24:16 And I think just if we were able to do that across the nation, we would provide much better service to callers and much better outcomes.
0:24:28 You would have, where I think humans make a ton of sense, a very empathetic resource on every single emergency call, fully focused, and we’re offloading a lot of the non-emergent catch-all traffic that often 911 becomes.
0:24:31 And so, that’s a product that we’ve launched, seen a ton of success with.
0:24:41 I would say that roughly the way we think about AI is, and I’d even put non-emergency in this category, is where are the automation opportunities?
0:24:44 Where is the mundane work happening that AI might be able to do?
0:24:51 And then on the very important emergency work, where can we be a co-pilot and fill in limitation gaps to make man and machine better?
0:24:54 Then either one would be a part.
0:24:56 So, I put non-emergency in that automation bucket.
0:25:02 I’ll give you another example of mundane work that bogs down centers that you wouldn’t think about that we think AI could really help with.
0:25:06 So, you know, one of our large cities is the city of Baltimore, right?
0:25:07 And I’ll tell this story.
0:25:11 Their director, Wayne, or deputy director, Wayne, is a close friend.
0:25:13 I ask him a lot of questions on what we could do, and he gives me his thoughts.
0:25:16 We had deployed our AI in the center.
0:25:18 It’s processing every single call, right?
0:25:22 And he had very, very intelligently started putting keyword trackers on any call.
0:25:26 And any time the word CPR was mentioned, Wayne would get a notification.
0:25:28 And I’d be like, Wayne, why are you watching CPR?
0:25:30 What does that mean for you?
0:25:31 And he goes, well, I’m trying to QA them.
0:25:33 I was like, well, what’s QA?
0:25:34 Well, we have to do quality assurance.
0:25:36 We want to see, do our operators.
0:25:40 We have a lot of new trainees that have just started because it’s hard to hire folks.
0:25:41 A lot of them are new.
0:25:43 Are they aligning with our protocols?
0:25:45 Are they saying the things that we need them to say?
0:25:46 Nine-one’s very protocolized.
0:25:48 And I was like, well, how do you do that today?
0:25:49 And this was their process, Ed.
0:25:54 They would go into their system, call the logger or recorder, which you have to have.
0:25:57 Their team would pull out a random sample of calls.
0:25:58 They would listen to them live.
0:25:59 And then they’d piece of paper.
0:26:00 They’d check the boxes.
0:26:02 Did I verify the location?
0:26:04 Did I verify the phone number going through the thing?
0:26:07 And he’d be like, and yeah, we get to 3% of calls a year.
0:26:12 Really excited about that because that was a great outcome with a very small team that was
0:26:12 randomizing it.
0:26:15 With AI, you can get to 100% overnight.
0:26:18 So not a very hard technical problem.
0:26:20 It is what is being said.
0:26:21 We look at the transcript of the calls.
0:26:24 What ought to be said, which is the protocol.
0:26:25 And did it happen, right?
0:26:30 And if we look at that now, instantly, 100% of calls are QA’d.
0:26:33 And we know, are they aligning with the city’s protocols?
0:26:39 And we can train those folks that aren’t doing so great against them to do better next time
0:26:41 and provide better service.
0:26:44 I think that should be totally human out of the loop, done in the background.
0:26:47 And it’ll be less resources focused on it.
0:26:49 We’ll actually get better outcomes.
0:26:51 So it’s another automation opportunity.
0:26:56 Hitt said something that I think is so important about AI and that I think is applicable not
0:26:59 just in this sector, but to basically every sector.
0:27:07 And this is what I believe the true value of AI is, is that AI should exist to do all the
0:27:12 things that are the mundane, menial tasks that you don’t really care about.
0:27:18 And I would go a step further to say what it really is, there are certain types of work
0:27:23 and sometimes the things we do in everyday life where your heart is in it.
0:27:28 We say that phrase, your heart’s in it, but I think it’s an important because that’s what
0:27:29 makes it human.
0:27:35 When you have someone who is suffering from a potentially life-threatening experience and
0:27:40 you’re their point of contact, your heart is in that interaction.
0:27:44 You care deeply as a human what happens.
0:27:48 And I find it so interesting that what you’re finding is that actually we need to
0:27:54 put the humans in those situations and then we need to bring in the AI for all the other
0:27:59 situations, all of the dumb, menial, rote bullshit where our hearts are not in it.
0:28:04 And I think that’s something that Mark Zuckerberg, as an example, doesn’t understand when he says
0:28:07 that we need AI to replace friends.
0:28:11 Friendship is an example where your heart is in it and where you need to be a human.
0:28:17 I think what you’re showing is, yeah, AI, it’s not going to be good at trying to coach
0:28:19 another human through a life-threatened experience.
0:28:21 A human should do that.
0:28:24 But what the AI is very good at is talking through a parking ticket.
0:28:26 I totally agree with that.
0:28:33 I, I, so like my potentially hot AI take is that the kind of general heuristics that we have
0:28:40 use to, uh, apply technology in our lives today won’t be that different in an AI future.
0:28:45 Like, so, so that doesn’t mean that there is an infinite amount of opportunity now, and that
0:28:48 this isn’t the best time to be a builder because you can think about things first, principally,
0:28:51 the technology is fundamentally different than anything else.
0:28:55 But I think the categories of things we will apply the technology to are very, very similar.
0:29:00 And so I use silly, silly examples on purpose just to like get your brain to think about it.
0:29:02 It’s like, I think it’d be two buckets.
0:29:06 I think it is things that humans ought not to do or should not do.
0:29:11 Uh, and that is like my silly example there is like, I probably could clean my clothes,
0:29:14 but I’m going to use like, yeah, I’m going to use a washer and dryer.
0:29:17 It makes a lot more sense for technology roughly to go and do that thing.
0:29:21 And there’s this class of things that are just mundane that don’t make sense for me to do.
0:29:25 And I can free up my time to focus on the things I’m probably really good at.
0:29:32 And then there is this second category of, um, use cases of technology that do fill in gaps
0:29:33 that humans just could never do.
0:29:38 And so like my silly example there is I’ll never be able to run 60 miles an hour.
0:29:41 I wish I could, you know, I like would love to be in the Olympics.
0:29:42 I’d win all the gold medals.
0:29:43 Um, but I won’t.
0:29:49 And so I use a car and in this scenario, me and a machine are just better together than
0:29:49 they would have been apart.
0:29:52 Um, and for us, that’s the language translation, right?
0:29:57 Like I go to cities across the country and they have this discussion of like, where does
0:29:57 AI make sense?
0:30:01 Worry about, okay, if we apply AI, is that threatening to me?
0:30:03 And I’ll say, okay, quickly show of hands.
0:30:04 How many of us speak 30 languages?
0:30:10 And I have not gone to a room where someone has put their hand up and because they, we just
0:30:11 can’t do it.
0:30:14 Uh, and technology makes a lot of sense there.
0:30:18 And if we fill in those gaps, again, we’re going to be better at performing the core
0:30:18 job.
0:30:23 And so I think the AI opportunities of today are those two buckets.
0:30:27 It is, where can we address mundane things that I should not be doing, right?
0:30:30 And some of those things, those mundane things were not technically solvable.
0:30:34 You know, there was not low latent voice and high performing voice that could do those
0:30:35 jobs.
0:30:40 Um, that’s really exciting, but there’s also a ton of things we just couldn’t do even
0:30:40 if we wanted to.
0:30:43 Um, and I think AI makes a lot of sense there as well.
0:30:47 It’s also important that the buckets are sort of being reflected in your business where you
0:30:53 found that the really human important jobs, AI is there to be a co-pilot and then the bullshit
0:30:56 menial jobs, AI is there to replace the human.
0:30:59 Those are the two categories that we’re working with here.
0:31:04 Um, I’d like to hear more from you about the business model.
0:31:05 What is the business model?
0:31:06 How does this make money?
0:31:10 So we, um, we have a couple of different products.
0:31:16 So, uh, you can go and if, you know, like your city has a specific challenge, like, you know,
0:31:18 we have a high density of non-emergency calls.
0:31:23 You can just get that agent from us and, um, use it.
0:31:24 And it’s a function of volume.
0:31:26 So call volume, right?
0:31:28 How, how many calls are going through the system?
0:31:29 Is it managing?
0:31:34 Um, and that right size, the large city is going to have larger volume and pay more versus
0:31:37 a small city who has lower volume and pay less.
0:31:44 Um, we have other products that sit next to the call taker and help them do the job better.
0:31:45 So things like language translation.
0:31:51 So now if we’re talking about that, you know, roughly 30, 40% of calls that are emergent, again,
0:31:52 it fluctuates city by city.
0:31:55 Um, we sit next to that person.
0:31:58 We, every single call that comes in, we would transcribe it.
0:32:03 If it’s non-English, we translate it and then we create this instant summary of the interaction.
0:32:06 And that summary takes the load off people.
0:32:09 They don’t have to take notes of every single call and try to catch every single detail.
0:32:11 And that’s what gets sent to the responder.
0:32:16 And so, um, that’s based on how many emergency calls that we would be processing.
0:32:21 Um, and so we try to understand how many calls the system would process.
0:32:22 And then we base the pricing off that.
0:32:27 It’s almost like a enterprise contract, like where it’s usually per seat, but this would
0:32:28 be sort of per call.
0:32:29 Exactly right.
0:32:35 You’ve seen this company grow a lot, um, from you and your co-founders.
0:32:40 I know you were playing football at Yale and then you got the Teal Fellowship.
0:32:47 You dropped out, um, you’ve grown this thing from, you’ve, you were knocking on doors to
0:32:49 now a company of over a hundred employees.
0:32:52 Um, what has that experience been like?
0:32:57 And what have you learned about being an entrepreneur and also a manager of people?
0:33:01 I always start by saying it’s good to acknowledge, like I certainly did not come out of a dorm
0:33:02 room ready to build the company.
0:33:06 So you just had to learn really, really fast along the way.
0:33:11 I think there’s three distinct phases that a founder has to go through and like, I’m in
0:33:12 the middle of them.
0:33:13 So I’m still trying to learn them as we go.
0:33:17 But like the first is you are doing the thing, right?
0:33:23 And that thing is building the product, talking to customers, selling the product.
0:33:27 There’s nobody else, but you, uh, all of those disciplines are crafts.
0:33:29 You got to get good at like sales is hard.
0:33:31 It’s especially hard in government.
0:33:34 There’s a lot of oops and things to jump through.
0:33:35 Building a product is hard.
0:33:38 Understanding users, what do they really need, right?
0:33:41 And what are they, what are the biggest problems and how to sequence those things?
0:33:47 Like you are doing that task very quickly as you grow, it becomes, can I manage people who
0:33:50 are very good at that discipline to do the thing, right?
0:33:54 And so you have small teams, you may have a couple reps that are going out and talking to
0:33:57 customers and are they going to say it exactly?
0:34:02 Like you, no, but can you like talk about this thing in a very, very, or in a consolidated
0:34:04 way or in a way that’s cohesive and makes sense?
0:34:06 Um, so management.
0:34:08 And then I think like, we’re starting to go into this filter now where you have middle
0:34:09 management.
0:34:16 It’s really about culture and what is the, um, operating system, the set of, again, values
0:34:21 or things that you all share that are going to be, um, how you make decisions, the framework
0:34:23 of, of how you make decisions.
0:34:26 And so very, all three of those very different muscles.
0:34:30 And so, um, it sucks because especially when you’re growing fast because you’re like, oh,
0:34:31 I just got good at that thing.
0:34:33 You know, I was so good at sales.
0:34:34 And it’s like, oh, right now I got to be a manager.
0:34:35 I got to learn that thing.
0:34:36 You start to get good at that.
0:34:37 You’re like, oh, that’s useful.
0:34:40 But now it’s like multiple people got to be managing.
0:34:42 Am I a small company manager?
0:34:44 Am I a large company manager now?
0:34:44 Yeah.
0:34:44 Yeah, exactly.
0:34:49 And so, um, those are the things that were, you know, that I’m thinking a lot about.
0:34:54 And so today as we were getting close to a hundred people, it is about what are the frameworks
0:35:00 that we set up where we have a very, you know, diverse set of folks, diversity in where they
0:35:00 live.
0:35:03 They’re across the country and they all have different backgrounds.
0:35:04 What do we share?
0:35:08 Like one, like what are the goals of the company?
0:35:09 What are we trying to do?
0:35:10 And is that clear?
0:35:14 And then secondly, what are the set of like cultural norms that we’re all going to say,
0:35:15 yes, we’re going to hold the line.
0:35:18 And these are going to be the things that, that we stand on and do.
0:35:23 Um, and how do we, you know, meetings that I’m not in make the types of decisions that
0:35:25 are going to continue to move us forward in a similar way that we’ve seen over the last
0:35:25 few years.
0:35:28 We’ll be right back.
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0:36:47 We’re back with First Time Founders.
0:36:50 What is the investor pitch?
0:36:56 The big question that I would have if I were an investor is, how much money can you get in
0:37:02 revenue from these underfunded local government call centers?
0:37:06 So like, what’s the pitch in terms of shareholder value?
0:37:09 Like, how valuable can a company like this actually be?
0:37:14 This company that primarily works for local under-resourced governments.
0:37:21 When big tech inflections happen, often startups or companies undershoot the potential.
0:37:22 Don’t overshoot.
0:37:25 And so you think of a company like Figma.
0:37:30 When the internet, now this happened over a long-term time horizon, but when the internet
0:37:35 first came out, people would not have thought that the browser was going to be so good that
0:37:39 you could build a $20 billion company, that people are going to collaborate in real time.
0:37:43 It was provocative and controversial for Bill Gates to say there’s going to be a computer
0:37:45 on every desk when the internet came out.
0:37:49 So that was the kind of provocative, controversial statement at the time.
0:37:51 Nobody was thinking Figma, right?
0:37:56 And so it’s important to step back and say when these inflections happen, not only are they
0:38:02 rare, not only are they big, but often people undershoot what ultimately they’ll become.
0:38:06 And so I think AI is similar in that it’s an iceberg and that they’re like 10% above the
0:38:09 surface and like 90% is still not.
0:38:12 And there’s some like general rules or things, common ground people are starting to find around
0:38:15 opportunities like voice could be big and underlying models would be interesting.
0:38:19 But like even that not fully known, is it going to be open source?
0:38:19 Is it not?
0:38:22 Like that stuff is still to be determined.
0:38:25 And so we are at one of those times, right?
0:38:30 Where everything in an industry not only can, but ought to be rethought first principally.
0:38:33 Why do we use the technology that we use?
0:38:38 Why, what jobs should a person do versus not do?
0:38:39 And so like that’s a macro trend.
0:38:44 When you take that lens to emergency services, a ton of opportunity.
0:38:46 I think they’re along these two buckets.
0:38:50 One is you have this fragmented set of tools, right?
0:38:52 Today you have eight or 10 screens.
0:38:57 I think in an AI future, those systems converge and that they come together.
0:39:01 And that, you know, not only will that be a convenience thing.
0:39:06 Yes, it’s inconvenient that I have 10 screens as an operator that I can’t think of a better
0:39:09 word, but I think if it does not change, it would be a dangerous thing.
0:39:13 It, you know, you think about what is AI really good at?
0:39:19 It’s really good at taking a lot of valuable information, aggregating it together, pulling
0:39:22 insights quickly, and then, okay, now we can go and use that.
0:39:28 If I have 10 different systems in 9-1-1 that I’m using, and all of them are from different
0:39:34 vendors and the data is siloed, you’re never going to be able to capture the, all of those
0:39:36 data sources and get the intelligence from it.
0:39:39 And so to just give you some of the rough systems, it’s like today you have a call come in and
0:39:42 that 9-1-1 call is in one system.
0:39:44 The data we punch in out to the responder in another system.
0:39:47 The radio is happening in another system.
0:39:48 The protocols are in another systems.
0:39:52 What you would really want is an intelligent system that knows all those things happening
0:39:58 in real time and can suggest what responders should we send based on a bunch of different
0:39:58 factors.
0:40:00 Isn’t that a hardware product though?
0:40:04 I mean, the thing that comes to mind is what Sam Altman and Johnny Ive are secretly building,
0:40:09 which is one product, a piece of hardware replaces the screen.
0:40:15 It’s not going to be glosses that you put on, but it’s something that just consolidates all
0:40:18 of the data, whether it be audio or text or video.
0:40:20 Isn’t that what you’re kind of describing?
0:40:20 I don’t think so.
0:40:22 I think it’ll be still software.
0:40:29 I think, I think that, um, the hardware will be the computer, the desktop and that, um, it’ll
0:40:35 more be that like all of these disparate systems come together and that the AI layer on top of
0:40:38 it will now have all of the data intelligence.
0:40:40 So I’ll give you an example that maybe we’ll bring it in front.
0:40:47 So like if you have an emergency today, right, um, call comes in and, you know, like we find
0:40:50 out that there’s weapons on scene and that a person on scene has autism.
0:40:51 This is a real example of this app.
0:40:56 So person on scene has autism, you know, we’re trying to make a decision on who we should
0:40:56 send.
0:40:58 Here’s a couple of failure modes.
0:41:02 One is because that is a small detail in the broader thing, there’s weapons on scene.
0:41:06 We could just miss that there’s somebody on scene with autism that could not even find
0:41:07 its way into the notes out to responder.
0:41:09 Um, we catch that, right?
0:41:10 We put that in.
0:41:14 If you have a transcript of the entire call, the AI would not miss that, right?
0:41:15 And so that’s one failure mode.
0:41:19 But then the second thing is now we have to make a decision who is the best person to send
0:41:21 to this situation, right?
0:41:25 Let’s say there’s one responder two minutes away and there’s one responder one minute
0:41:25 away, right?
0:41:30 Today, because that’s such a fast moving event and emergency, we are going to send the person
0:41:31 one minute away.
0:41:35 You can’t take in account that many factors other than proximity, right?
0:41:40 But if I knew the records of that, both of those responders and the guy two minutes away,
0:41:43 I saw in his report that he has an autistic kid at home.
0:41:44 This is his life.
0:41:45 He’s done this every day.
0:41:47 Yeah, he knows exactly how to handle this.
0:41:49 And the other guy one minute away, it’s his first day on the job.
0:41:53 He’s never responded to emergency ever, let alone something as complicated as this.
0:41:54 Who would you send, Ed?
0:41:56 Okay, you know all this information.
0:41:59 You might send the guy two minutes away, right?
0:42:03 AI can look at all of that data, can search through the last 12 months of calls and what
0:42:07 this person has sent and provide, hey, maybe we should send this guy.
0:42:10 Now, the human will still make the decision, okay, I have all the information in front of
0:42:10 me.
0:42:11 Who am I going to send?
0:42:14 But AI can go and hunt all of that information and pop it up.
0:42:19 I think you will have that single intelligence system that takes into account all of that
0:42:20 data and helps us make more informed decisions.
0:42:22 I totally agree.
0:42:27 However, the AI must receive access to that data.
0:42:31 And that’s probably the other question that I need to address here, which is what you’re
0:42:37 describing is a world where we know a lot about the personal lives of citizens.
0:42:44 And, you know, I’m sure China’s crime rates are a lot lower and I would bet their call centers
0:42:45 are better than the US.
0:42:51 But I think that’s partly because they are down to put cameras everywhere and to have
0:42:54 a profile on everyone and know all of the personal details on everyone.
0:42:57 We are less willing to do that in America.
0:43:04 And I would imagine that governments are especially less willing to do that when a private tech company
0:43:09 backed by VCs comes in and says, hey, we need to get access to this data if you want to improve
0:43:10 your product.
0:43:16 To be clear, I’m not obsessed with the data issue, but I know a lot of people are.
0:43:22 So what would be your response to them, to people who are concerned that if we go your
0:43:25 route, we’re sort of going into a big brother type situation?
0:43:29 I think you should be concerned about the data route.
0:43:31 It’s a thing that we think a lot about.
0:43:34 Here’s how we approach it, right?
0:43:39 So I think, and I can give you practical examples, is on something like video, right?
0:43:41 Like, let’s say we’re going to capture a video.
0:43:44 You are, a number of things are happening.
0:43:47 One, you have a call taker who’s deciding to send that out.
0:43:49 They’re saying, hey, I want to send this out.
0:43:54 There’s a person who gets a link, the caller, and says, yes, I want to turn on my video.
0:43:59 And they have multiple permissions where they can say yes to doing that.
0:44:02 And so only if they say yes, are they doing that.
0:44:07 So any data collection from the caller is happening in a permission-based way.
0:44:13 Now, on our side, right, when we are transcribing or introducing translation, et cetera, an important
0:44:17 thing to remember is this person called 911, right?
0:44:22 So they’re calling 911 on, so it’s not like we are just putting up cameras.
0:44:25 And as they’re walking down the street, we are capturing that interaction.
0:44:27 This is somebody who’s saying, hey, I need help, help me.
0:44:32 So that data already, the transcript of that call, in every city in the U.S. is being
0:44:32 stored.
0:44:36 It’s actually a mandate nationally that you have to store the calls.
0:44:37 And I think it’s actually a good thing.
0:44:41 Citizens can FOIA request them and say, I want to review the call.
0:44:43 And they come up in court all the time.
0:44:45 And so that data is already being stored.
0:44:50 I would say we are just taking information that’s already being stored and now using it
0:44:53 for the full potential of serving citizens, right?
0:44:58 So there’s no new data, to put it, shortly, unless a caller is saying, yes, absolutely,
0:45:00 I want to share this information.
0:45:04 We’re taking existing data sources that are being stored already when somebody calls 911
0:45:06 and making them useful in real time.
0:45:09 This has been very informative.
0:45:11 I’m just going to begin to wrap us up here.
0:45:16 You are using AI to transform 911.
0:45:18 And I’m in.
0:45:21 I don’t know if the audience is in, but I certainly am.
0:45:30 What are some other government, public sectors, public services that need an AI makeover?
0:45:33 I’m most up to speed on the ones adjacent to us.
0:45:39 I think the first is just quality of life issues.
0:45:45 You’re seeing New York talk a lot about this in that how can law enforcement not just be
0:45:49 reactive to crime and, you know, large scale emergencies?
0:45:54 How can we get more proactive about the things that most people face every single day?
0:45:56 These are services like 3-1-1.
0:45:57 What is that?
0:45:58 I don’t know what that is.
0:45:58 Yeah.
0:45:59 3-1-1.
0:46:01 So you call 911 for an emergency.
0:46:03 It doesn’t always work that way, but you should call 911 for an emergency.
0:46:07 You call 3-1-1 for things like a pothole, right?
0:46:11 Or, you know, like I have a question about a parking ticket I got, right?
0:46:14 So that 3-1-1 is normally the line that you call for that.
0:46:22 In most cities, because they’re already having trouble staffing 911, those lines just are
0:46:24 pretty poor, right?
0:46:25 They face really long hold times.
0:46:28 It’s very hard to get an operator.
0:46:32 So a place like LA right now, if you call the non-verdency line, you’ll have a 47-minute
0:46:32 wait, right?
0:46:34 You’re just going to be on hold.
0:46:36 So most times it doesn’t get answered.
0:46:39 And I think that’s the storefront of our cities.
0:46:43 You know, I think of like an Amazon public company that does really, really well, or, you know,
0:46:45 one of the most valuable companies ever.
0:46:50 Bezos was calling the support line because he wanted to know what the service was like.
0:46:52 And again, that’s the service line of our cities.
0:46:55 And I think we can do a lot better.
0:46:58 How do we promote more innovation?
0:46:58 Because I agree.
0:47:05 Like, I would love if I were clued into local government and if I were interacting with it
0:47:09 at a product level on a semi-consistent basis and was happy with the product.
0:47:12 How do we just promote more innovation?
0:47:17 Is this an issue with VCs that they’re too obsessed with private sector products?
0:47:20 Like, why is there so little innovation here?
0:47:23 I think there’s a lot of variables there, too.
0:47:24 I think you hit on one.
0:47:25 Part of it is funding.
0:47:30 And we’re doing the hard work to try to convince people to say, let’s put it in new places.
0:47:34 And that we actually think there’s a ton of opportunity because a lot of stones have gone
0:47:39 unturned from a technology perspective in spaces like emergency services.
0:47:41 So it is a funding issue.
0:47:43 I think that’s changing, which is really, really exciting.
0:47:47 I think the second thing is it’s a talent issue.
0:47:51 It is not a lot of folks with our background are jumping into public safety.
0:47:55 I remember when we first started the company, a lot of people told me I should not do it.
0:47:57 And it was not obvious that it was going to be successful.
0:48:00 When I first did the Teal Fellowship, it was like, okay, I’m going to live in a New Haven
0:48:01 apartment.
0:48:04 I’m going to talk to public safety who’s really slow at moving.
0:48:06 And like, that’s going to work.
0:48:09 And a lot of people were like, why are you doing that?
0:48:10 That’s never going to make up.
0:48:12 So like, there’s a lot of scrutiny around it.
0:48:17 And what my counter to that is, like, at, you know, startups going to be hard regardless.
0:48:18 Maybe this is harder.
0:48:23 When you have an inflection technology, you likely are going to do your life’s best work.
0:48:26 You can make the most impact ever now.
0:48:30 You should be very deliberate in the decision you make and the things that you work on.
0:48:35 You know, like Uber Eats is cool, but if you get somebody their food faster, is that really
0:48:35 important?
0:48:39 Whereas here we have the opportunity to save lives and you should do it.
0:48:43 And so I think it’s a talent issue and we’re doing a lot of work to try to convince the
0:48:44 best people to do it.
0:48:48 And then I also think the space, like we talked about for, yeah, I think logical, thoughtful
0:48:51 reasons is less receptive to new things.
0:48:56 But if you get companies that really do it well, that take it very, very seriously, that
0:49:01 are going to build things over a long-term time horizon, we’re going to change hearts and
0:49:01 minds.
0:49:05 And they’re going to see the new companies as credible.
0:49:06 But that all reinforced itself.
0:49:09 You get the funding, you get the talent, then you’re going to become off, you’re going
0:49:11 to come off more credible.
0:49:13 But I’m confident we can fix it, man.
0:49:14 And I think it’s very, very important that we do.
0:49:17 But I think those three things will really move it.
0:49:26 Finally, any advice that you would give for any young entrepreneurs or wannabe entrepreneurs
0:49:28 who are listening to this podcast?
0:49:31 Maybe people that want to follow in your footsteps.
0:49:34 What would your advice be to young people listening to this?
0:49:36 We are at a very, very important inflection.
0:49:38 And it doesn’t happen often.
0:49:42 And, you know, regret happens retrospectively.
0:49:44 You don’t often see it in the moment.
0:49:49 I would nudge people to say, in 10 years, 20 years, name your time horizon.
0:49:50 What do I wish I did today?
0:49:52 And do that thing.
0:49:58 Where that’s led me is to make sure that, okay, if now every industry can be thought about
0:49:58 first principally.
0:49:59 We talked about that earlier.
0:50:02 What industry actually matters if we rethink it?
0:50:07 And then make sure that every ounce of your work is going towards something you actually
0:50:08 care about it changing.
0:50:10 For me, that is life-saving work.
0:50:16 I get excited and I’m inspired by the fact that every line of code we write can be led
0:50:18 up to somebody getting better help.
0:50:24 And so I would just nudge any founder or early employee to be very, very thoughtful of that
0:50:26 decision, especially in the time that they’re in.
0:50:28 Work on something that matters, man.
0:50:30 And it might be harder.
0:50:31 It might be more arduous.
0:50:33 But it’ll matter a lot more.
0:50:34 And you will not regret it.
0:50:36 Make sure you spend time on things that really matter.
0:50:40 Michael Chime is the CEO and co-founder of Prepared.
0:50:41 Work on something that matters.
0:50:44 I love that as a closing line.
0:50:46 Michael, thank you very much for joining us on the show.
0:50:46 Thanks, Ed.
0:50:47 Appreciate it.
0:50:52 Our producer is Claire Miller.
0:50:54 Our associate producer is Alison Weiss.
0:50:56 And our engineer is Benjamin Spencer.
0:51:00 Thank you for listening to First Time Founders from the Vox Media Podcast Network.
0:51:03 Tune in tomorrow for Prof G Markets.
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