Government benefits are broken. Is AI the fix?

Hey folks, Jeff Berman here, co-host of Masters of Scale, I am thrilled to share some of the new names who will be joining us at this year's Masters of Scale Summit. They are the leaders driving the most pressing conversations in AI, Repplet Founder and CEO Amjab Masad, Cloudflowers Matthew Prince, Signal President, Meredith Whitaker, and many, many more who will take the stage this October 20th through 22nd in San Francisco. We want you there with us too. Join us at MastersofScale.com/painiers. That's MastersofScale.com/painiers. The business world is moving faster than ever, and when change hits, we need to learn in real time. When rapid response, you'll hear candid conversations with CEOs and leaders making tough calls about AI, human talent, responsibility, and the bottom line. How they navigate uncertainty, pressure, and high stakes moments. Umbob Safian, former Editor-in-Chief of Fast Company, and I'll be your host as each episode breaks down what you need to know right now. You can find rapid response wherever you get your podcasts. I think the new rules of our society are going to be written by technologists. How the society operates will be written by code. It will not be written by people who pass laws. I think that AI can be transformative, and I'm seeing programs work quicker, programs work better for people that rely on them the most. We spend more in this country on health and social services than we do on defense. Medicare alone is $1.1 trillion, so we should want these programs to both work well and be managed well. AI is either going to happen to you, or it is going to happen with you, or it's going to happen for you, and our goal is to have it happen for you. When people stand in line for hours to get their driver's license, or hear about hundreds of thousands of people who wrongfully denied government food assistance, they rarely think I can fix this. But Fedra Ellis-Lampkins thinks, "I can fix this. I can make this better." She's the CEO of Promise, a software company whose mission, in her own words, is to move money in and out of government to the people who rely on it. Promise is already running programs in Mississippi, Pennsylvania, Florida, and more, making them faster and cheaper to administer. Without losing sight of the low income families, the system is supposed to serve. In my conversation with Fedra, we get into how she's making all of that possible with AI. I'm Rhonda Elcalyubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution. Hi Fedra, welcome to Pioneers of AI. Thank you so much. I'm so happy to be here. I'm so excited for our conversation. So I saw you present at Masters of Scale Summit last year, which was great. How was that experience like for you? It was great. I mean, it's hard not to enjoy when some of your favorite people are in a place talking about some of their biggest ideas. So it's just an amazing, amazing experience to be there. I would love for you to ground us in the work that Promise is solving. It does seem like navigating the government system is a real hot mess. And so can you describe for us America's system of public benefits and how does that work? And what is it like as a user or a citizen navigating that system? Yeah. So Promise focuses on essentially making government work for the people who rely on it. And that means it should work well. It should be inexpensive. We should be able to measure its impact because my fundamental belief, I think our company's belief, is that a society is only as strong as its ability to govern itself and operate. Everyone has a nightmare story of going to the DMV and it taking hours and hours or having an appointment and not being seen. And most systems, I think, that we have seen, we're clear that they are not operating in the most effective way possible. At the same time, it is not just the public sector. We see whole cottage industries of consulting firms getting paid $300 to $500 million to do a modernization that barely is off of cobalt, which is an aging system for people who use for programming, my parents programmed in cobalt, yes, yes, yes, it still exists in many places. And so the fact that we need folks who understand cobalt or that information is still stored under someone's desk on a server that is not the best that we should be able to offer a society in 2026. So my parents are over 70 and they got their green card a couple of years ago. And we've been trying to get them health insurance. And it is an absolute nightmare and it's so frustrating and it's creating so much tension within the family because it's like I'm responsible for getting this figured out, but I'm really struggling. Yeah. Like where to go on the website? Who to talk to? Yeah. I don't know if you have any thoughts for us. I have a lot of thoughts and so many thoughts. One is a lot of why we exist is because of what's happening with your parents, which is one, the law changes a lot. And so right now for example, your parents would get probably Medicare or Medicaid depending on their income. And when you look at what is required, there are things like work requirements. So even if you're over 70, you have to have work requirements. You have to. They haven't worked in the US ever. Right. So the way it works in the United States right now is they would be required to report volunteer hours or that they had tried to volunteer. And so part of what's hard about these programs is you have state laws, you have federal laws, and you have changing laws and changing policies and procedures. If your parents were in the state of Florida, I could help solve this for you in two seconds because they're a client. And the reason is because we have a system that they use through AI that actually can manage policy change and that can audit and say, hey, this doesn't work. This is right. And then actually change the system in real time. And so part of what we're talking about is you might go to a website and the website, the law changed in January, but the website didn't update the new policy change. So you're still going to be allowed to apply, even though your parents aren't actually eligible with three things that need to be met. And that is the fundamental problem. We have not updated systems to recognize that laws change policy changes and that the system should move as quickly as we pass those policies. And it should get easier to use, not harder to use. Can you give us a sense of how many people are using these systems and how much money is flowing through? Yeah. Medicare alone is $1.1 trillion. So when you hear the defense department saying, we're trying to become a trillion dollars, just think, oh, that's really just Medicare. So we spend a lot of money. So we should want these programs to both work well and be managed well. A lot of the funding happens at a federal level, but then goes to the states to implement. And so part of what I feel so excited about is we work with the state of Florida. As an example, we've integrated with the utilities and with other social programs. And so if you, for example, apply in California, you come into an office, you bring a copy of your bill, you might do a form online, you bring your income, and in 2026, we should not run programs like that. So what you would do in Florida is we're integrated with the utilities. So we know what the bill is. And you have the information. Right. You have the information. And as we think about, it's more effective for us to integrate. It's also more accurate and it decreases the risk for fraud. And that is what we think about. If the bill exists, your employer reported your quarterly wages. Why do we want a human to bring us one? It doesn't make sense. I think part of what's hard about health and social services is it's largely dominated not by software companies but by consulting firms. What is the difference between a tech company doing this work versus a consulting company? Yeah. It's a good question. I think the difference is a tech company is measured based on outcomes and a consulting firm is measured based on hours and a number of people that work on it. So if I'm a consulting firm, I want a big contract. I get a big contract because I'm going to have a lot of hours and a lot of people working on it. If I'm a tech company, I want high margins, which means I want to get it as quickly as possible as efficiently as possible because I measured based on outcome. And so as a society and government, we should want outcome measurements. We shouldn't want people to be paid by the hour. These aren't lawyers, right? This is a project manager. Why would we want to pay those people by hours? It's the wrong incentives. And so it doesn't align the incentives with getting the work done and being the most efficient. Yeah. Doesn't make sense. I'm going to talk about what exactly promise does, but for that, I want to kind of wind the clock back to how you got started. Sure. So I started my company out of MIT and one of the applications of the technology was helping kids on the autism spectrum. So we spent a lot of time debating whether we should do a for-profit or a nonprofit. And we ultimately went for a for-profit organization. We felt like that would be more sustainable. So I'm curious about kind of when you were starting promised, did you have to think about that? And also what led you to start promise? Like a little bit maybe about your backstory. - Sure, I had a really different life experience in most people who found companies, which is I'd run a nonprofit. I ran a labor federation. I was an elected leader of a labor federation, representing unions. I worked in music. I worked with the musician Prince and then understood the impact of technology. I was just like, wow, this technology wasn't good for workers. It wasn't good for the environment. It isn't good for musicians. So I wanted to understand it. Thought about going to business school, got offered a job working with an investor. And then I went to work at a company called Honor, which is now the largest home care agency in the country, a technology firm. And so for me, starting promise was really about how do you build a company that in the same way we've done for the defense sector, that you expect innovation to be centered in health and social services, since it's the most amount of money we spend as a society. And it is the place that your parents, that our children will depend on kind of what we think the future looks like, especially in a AI world. Yeah. I have to ask you about your experience with Prince. I know you have this question a lot, but how did you end up with that kind of relationship and connection? And what did you learn the most? It was really interesting. I have a friend, Van Jones, who if you've ever watched CNN is on CNN. And he introduced me to Prince. And I was pregnant. And he asked me to work on a project with him and that project went well. And then Prince called me and said, can I be your client? And I was like, I don't have clients. Like, what do you mean, that would go on? And I ended up working with him. And it was really incredible for a couple of reasons. One, I spent a lot of my time working on kind of justice issues, and what became very clear to me is that people liked doing fun things more than they liked doing hard things. Like when you're having a rally, you're like, come do this. And what I discovered is when you invite people to a concert, they want to come. Oh, if it's fun, people want to do it. And I was like, oh, so I learned to like, oh, we're asking people to do hard all the time. The other thing that was so interesting to me about Prince is that there is a boldness that I had experienced, but not at the level of his boldness. Like ambition? It isn't just ambition. Maybe I'll give you an example, which is, he was frustrated one day. And so he kicked everyone out, and he sound checked every instrument himself. And so you realize when someone has taken that time and that talent, where they are good at everything, so they can control and understand and create, it was a discipline that I had not seen before, and it made him bold because he could tell you what to do because he could probably do it better. And so I just was like, there is an incredible commitment to excellence. That's why when people tell me I have a daughter, she's like, I don't want to practice. I was like, Prince practiced every day, for sure you can practice. Oh, okay, for sure you can do it, you can do it. (upbeat music) Don't go anywhere. We'll be right back after this short break. (upbeat music) (bell dings) Hi, I'm Molly Graham, host of WorkLife, a podcast from Ted. The most important lessons about work usually aren't the ones anyone teaches you. They come from experience, from uncertainty, from figuring things out as you go. On this show, my expert, Gess and I, explore how careers really unfold, through change, through doubt, through the decisions that shape who you want to learn. Change, through doubt, through the decisions that shape who you become over time. Because those moments aren't the exception, they are the work. Listen to WorkLife wherever you get your podcasts. - On Master's of Scale, iconic leaders reveal how they've beaten the odds. - Asking really strong questions is a superpower. - You want to show up with something radically different and how they've grown companies to incredible heights. The greatest reward always come from the greatest risks. - That's hit the gas. Here being B, Zillow, Microsoft, liquid death and more. Here from the founders who've changed the game, it's anything but business as usual. Find Master's of Scale on Apple Podcasts, Spotify, YouTube, or wherever else you get podcasts. (bell dings) - I sold my company and I'm now an investor in early stage startups and we see a fair amount of what we would call Gough Tech companies, like companies serving the government sector. And I often have two big questions/concerns. One is how big is this opportunity? And you're addressing that this is actually pretty big. And two, it just sounds like a nightmare to even get into the system as a supplier. Especially my sense is a lot of government, they don't want to do the wrong thing. - Yeah, so curious about your experience there. I think the market's huge. It is a hard entry point, right? We've been doing this for a while, and we just signed our first federal contract this year. And I think what's harder than other markets is you have forces consistently working against that have a lot of infrastructure. So an example might be, I'm a big consulting firm. I probably have a third of my staff who came out of the administration that you're trying to work with as a startup. And so they can call someone, they can talk to someone. We already have a contracting vehicle. There's actually a firm that has a law that means you don't have to go through procurement if you work with them. I think the other part is, I think that the Defense Department has largely made a decision that it is okay to innovate. No one would expect the Defense Department to build its own plane. Wouldn't even occur to you. And I think it's largely because in defense, people have made the recognition that intelligence is valuable, that the way that you manage information is really critical. And I think on the health and social service side, it's a little harder because everyone is scared and remembers a story where someone hired a startup. And there's a saying, no one gets fired for hiring Deloitte, right? Even if it doesn't work, it's like, that's Deloitte. It's not your responsibility. Government is not a system that rewards innovation in a lot of places. - Can you walk us through kind of a couple of examples of what life was like before Protest existed and then you came along and but it does look like now. - Maybe I'll give an example, the state of Mississippi, just based on thinking about your parents and we work on work requirements there, which for folks who don't know, is some states had some work requirements. There was a new law passed that has very specific work requirements. And in a lot of places in state of Arizona as an example, there's been almost like a half cut in food stamps. The number of people on food stamps, a third of which are children. And so we know less children will have access to food stamps. And the state of Mississippi, they want to support what the Trump administration is doing, but they want to make sure in line with that, that they are not wasting money, that there is not fraud and the system is working well. And so we actually executed a program there that makes, think of it as like an automated work requirements reporting. And so what we'll do is we will reach out to you to first make sure you're aware of work requirements. What are they? What is it require? And we'll do that by text. Then we're gonna allow you to do that reporting through us by text. - Cool. - And then what we're gonna do is have a subscription service where, for example, if you have wages, we can pull them once we get permission so that you don't have to keep providing the information, but you give us the ability to access it, we ask you ahead of time, and then we're able to pull that information. And so in Mississippi, Mississippi had almost a 2% decrease in their fraud rate, or their SNAP error rate. And why that matters a lot is because basically, what the administration said is, we're not giving you state's money to pay for your food stamp administration. If you don't decrease the fraud or error rate. And so what we saw is Mississippi did great when we looked at other states. They brought their error rate down, they're killing it. And our basic premise, this is, we're talking to someone this week, is if you fix the problem before it enters the system, that's the goal, right? That should be the goal. You want to train the person, give maxist information, and stop it from being a after-fraud problem. - Can you clarify a little bit, definitely for me, but also for our listeners who are not familiar with how these systems work? What is the relationship between work requirements and health insurance or work requirements? And SNAP benefits. - Like why are these things linked? - It's a great question. So for most of these programs, you have an income requirement, right? Think of it as someone who's making less than $13,000 a year, right? So you're looking at an income requirement, health care are the same things. Food stamps as an example, there's a work requirement between that, in addition to having that income less than that, you have to be able to prove that you are trying to find work, that you have work or that you're volunteering, there are rules to be able to receive that benefit. - So these are all like the qualifying criteria. - Exactly, and they continue. So it is not just that application, you have to continue. So the work we're doing in work requirements is because you have to keep doing it, to keep the benefit. - Yeah, so you basically you apply, and there's all these criteria, including work requirements, and then what is really magical about what promise does is that you're able to, almost like not auto-populate, but connect to the systems that has the information, so you're able to continue to pull this information? - Yes, an easy way to think about it is first it's education, because for example, your kid might go to college And they've turned 18 so they no longer should be under your food stamps and it might that occurred to you. It's June. My 18 year old is graduating. You don't think immediately. I have to go announce to someone that now the number of people in my household has shifted. So the first thing you do is you want to keep reminding people, these are the rules. Did anything change? And then they can do a change with you. Okay. Oh, yep. It changed. So that's the first thing. Okay. We're going to tell them. The second thing is we're going to say, can we pull your income so that we can ask you, has anything shifted? Okay. Great. Can you take a photo as there's something we need to do? It makes it easier for the person who relies on those benefits, but it increases your source material because now I'm pulling it directly from the employer. We're always trying to think about how to get the highest quality data because that's ultimately how you reduce the most fraud. And so the other systems are designed like once you find fraud, let's audit it, let's do something about it. Where promise is like, how do you make sure people have information? How do you get higher source material? And how do you stop it before the payment goes out? That is our model. So let's talk about the role of technology and AI specifically and promise. When did you decide to incorporate AI into the product and actually what kind of AI are you using? So I think for us, it's important to probably make a distinction where, where is their AI? Where is their machine learning just to like nerd out for a second? Love it. Love nerding out. A little nerding out. And I would say we're more machine learning than anything else. And the difference might be for the folks who aren't nerds is that what you want to do is you really want to train something to be as smart as possible about the specific things it's going to encounter. And then basically training agents, we have so much access to data. What we're trying to do is train it to be able to understand and recognize patterns and know what to do when those patterns exist. Because for us, artificial intelligence is really about outcomes. The analogy we try to think about internally is Weimo, right? First, you started with humans driving. Then it drove with the humans in the car. Then it like you need those kind of quality controls. But for us, we feel confident in our agents making decisions. But we launch with humans running alongside agents at any time. There might be 70 agents working on one case, one agent to do text messaging. One agent is looking at blurry photos. Another agent is responding because you didn't send something. But we wouldn't expect there to be in the one kind of generic agent that's able to do it. Right. Because I would imagine every agent slash machine learning algorithm is trained on a very specific for a specific task on its relevant data. Exactly. Exactly. So maybe you're applying for something that requires paywall records. But it can only be these specific dates. So we have an agent that only looks to make sure if that wage information is in with a very specific range. Because if you're doing it without promise, you submit it, you wait for a person to send you a letter or call you. Whereas we can do it in 22 minutes because the agent says wrong dates and then another agent sends you a text message that says reply here. So it's like the layers of agents. I think is much more likely to continue to succeed. We can make a decision in 22 minutes. Now I'm going to play a devil's advocate for a second. I can imagine somebody listening to this and they're thinking, oh my god, now AI is going to make a decision on whether I'm going to get the snap benefit or this social service. What would you respond to that? Well, I wish that were true because I think you would probably get a better decision. Let's talk about bias in AI versus human bias. I guess totally. Let's talk about it. We see human bias based on their own experiences. We see human bias based on deserving or not deserving. When you look at the impact of like high caseloads, I guess what I would say to the person who would be concerned and which most of America is now apprehensive of AI is I would say it is coming. It is like protesting the automation of cars. It is like it is coming. So the real question is does it happen to you or do you make it work for you and does it make a better government or does it destroy the humans? And so it's coming. And the reason I think people should root for companies like Promise is because we don't launch without QA and we've pulled agents back. So I think people are right to be concerned. But the world is competing for the future of artificial intelligence and we should want it to work well. We should have rigor around it. I appreciate you saying this because I think it's so important to have a high bar of what can get shipped and kind of doing quality assurance around data and algorithmic bias is really important. But I will also say to your point, I meet a lot of founders who are not thinking about this at all. So I appreciate that you guys are kind of taking that seriously. We really are. And part of it is maybe I'll give you a couple of examples language, right? When people use Google translate, I'm always like, oh, and especially for things like social services, because what I worry about is someone does fraud because there's been bad translation and it asks for something different, right? And so you need to actually translate something. I think people don't think about those things. And so people should want companies who want the systems to succeed, right? You should want, and you want the people to succeed. And so I think bias is real because part of it is people just have had different life experiences. Like even here, I'll give you an example, we had someone on our team super smart. And so we're having a conversation about paychecks. And I was like, oh, you got to pull money in the morning when they get paid. And from an engineering perspective, it doesn't make sense to pull something in the morning. It makes sense to pull it at the exact time someone took it. So like if you make a payment at 1059 PM, it makes sense to do it 30 days later at 1059 PM. And I was like, no, no, take it in the morning. They're like, why? It's like, oh, because it's paid as like, but the paycheck is going to be gone by the end of the day. And they're like, how could someone's paycheck be gone the day they get paid? And you realize if that's who's building technology, right? Because their experiences is a kind amazing human being that their paycheck wasn't gone the day they got paid. Whereas for a lot of people in America and other countries, your paycheck is gone before you get paid. And so you got to, you know, you got to go figure out those things. Yeah. How do you bring that perspective, like that diversity of lived experiences, which is going to be so crucial in making the product work? It's a really important point. One, I think the group that often gets less out are taxpayers who want to make sure money isn't wasted, which we should value. That is a fair and good thing to value. The second thing is the people who do the work, who are like government workers who often get vilified, but a lot of people who want to do really well. So we should honor that they want to do that. And I think the way that we think about it, because we're nerds, is we try to give metrics to it. So one of the things which most folks think is crazy until you do it, is we introduce customer service metrics or CSAT scores. And so for every person that fills out a form or has an experience with us to rate us as a customer service experience one through five. And so, and then we report that to our clients, which is the government. And say, here's our average score. The second thing we do is we have a text field. So you can write whatever you want, whatever it is to tell us what your experience was like. And the thing that's been so remarkable for us is, we're talking about Mississippi is one of the quotes from, because we asked the, and we asked people to work for the government and the people that are getting the benefit. And they said, this is the best part of my job. I was like, the best part of my job. Done. Done. Done. Done here. And like in Mississippi, our average score is a 4.8 out of five. And, and so then it changes where the system says, we should have these scores in other places. And you're like, absolutely. You want someone to succeed in the system, right? So like if we were designing a product for consumers, we would say, you don't want so much content, because we know every time we had content or out of next screen, people drop off. So we should be trying to think about the least amount of drop off, the most amount of accurate information, the best experience. How do we set the next person up for success? Because we should not be debating these programs once they exist. We should be making them well-run, good experience so that people can participate in society. One of the things that we talk a lot about on this show is kind of the commitment to security and privacy, which I imagine is super important for promise anyway. Yeah. So how have you implemented all of that into the platform? Well, I would say I'm probably the person least worried about this because I work with a bunch of nervous Nellies who came out of national security. So I was like, why can't we store that information? And I think the thing that's been really important is you want people to trust you. And so for example, we don't sell to consumers, right? You couldn't call me and say I want to hire promise. And the reason is because we don't have a value for data if we don't sell it. So there's no reason to misuse it. And so I think the first principle should be you shouldn't have an incentive to misuse information. When we get more information, it's only towards one cause. It's not towards something else. And so the only people we work with are highly regulated utilities and governments. And so the only thing we use information for is to get someone a social benefit program. We're not selling information. And we've had people even try to tell us you could sell your analysis to private equity. And like, oh, because you know these things. And I I just think any kind of graying of those lines, just-- makes the company less disciplined and less safe, and the market is so big and the opportunity is so big that we shouldn't be diluting our outcomes to try to figure out how to sell information, and so I think there's no internal mission or reason to do it, right? That's awesome. Okay, so AI is obviously creating massive economic opportunity, but it's not equal opportunity. Would love to hear your thoughts on that. Yeah, it's a great question. I'm worried that we're trying to make people's consumers instead of builders. And so in general, I think we are trying to get people to use some of these services as like a Google alternative instead of building, and so we want to figure that. And then the second thing I worry about with AI is we think about the very real consequences of things like data centers or other pieces. It is that I worry that the people who are most likely to be impacted have a response to just stop, and they're not going to win on the stop. I think that AI can be transformative, and I'm seeing programs work better, programs work quicker, programs work better for people that rely on them the most, but there's not enough people deeply understanding how it works, how to make it, how to do it, and I see a lot of bad prompt engineering called AI. And so the piece that I would just say is AI is either going to happen to you, or it is going to happen with you, or it's going to happen for you. And our goal is to have it happen for you. And if you believe that artificial intelligence and super intelligence are pretty close, and the robots will eventually control our society, which feels very scary, then you should want to think about what are the conditions in which it exists. And the people who are closest to AI are training it to make sure they exist, right? They're training it to make sure they're saying that they flourish. The idea that we will just protest and let the dudes who want it to work for them control it is just not a good strategy in life. And so I guess I would say is it is happening with or without you, and the real question is does it happen to make your family's life better? Or does it happen in a way that negatively contributes? But no one has effectively stopped the progress of AI. I'm worried about the distribution of knowledge and income, right? I think I came out as a labor moment many years ago. So I think people are right to be concerned and we should acknowledge it, but I don't think the answer is to ignore it. Yeah, I love your line of AI is going to work without you, maybe with you, but wouldn't it be awesome if you made it work for you? Right. Because I was thinking about we're talking about health care as an example. So it's like everyone's right. It's not great, but the thing I worry about is people are imagining the impact for them as someone who has health care, who has resources. They're forgetting at the Puppout person who's living in a rural area that doesn't have access to many of those things. And the idea that we would not use AI to be supplementative to their lives, instead of like measuring it by what does it do for someone in San Francisco or New York, who has resources, doesn't make any sense to me. Yeah, absolutely. I find your story super inspiring because you are building at the intersection of creating wealth and hopefully also a ton of impact. I love that. That is part of my investment thesis, but also my daughter's 23, my son is 17 and a half, and we try to like talk about these core values. What is your advice to young people? I don't know how old your kids are, but what's your advice to young people who are trying to both be impactful, but also do well? I have a 14 year old and we have some older kids. For my 14 year old, I told her, she can be on a screen as long as she's building not consuming. I love that. She can, whatever, she can be on 20 hours if she's building. I was like, I don't want to see you on someone else's app. I don't want to see you like, you want to be on the computer for 50 hours, go hard and long, but if I see you on someone else's algorithm, getting your brain impacted or your own thoughts changing, because you're letting someone else determine how and what you see, that's very, very time limited. They're not, she's 14, she's not allowed to be on social media. I want her to know how to read and write and well, because I think writing is going to be a skill that not many people will have, and you will critically need. I also think the ability to analyze information, because now we're going to be getting so much information in that has been gone through someone else's filter, and it is based on their own truth. One of the things we're talking about is Google before said it was like showing you a window. It's not showing you the truth. It's showing you a window. So we need children to understand, even adults to understand, that all that we're seeing is someone else's perception. And the way I think about it is if I asked someone in the United States, what is God or who is God, they would tell me something very different than someone in India would. Which of those is the truth? Which of those is truth? And so that's how we have to think about AI. So, right, is someone's truth is so different that you have to be able to make the dissension of what's true or not. I want everyone to do is be builders and be super smart and good, because I think we have enough critiquers, analyzers. I think we're building a new society right now. And I would want our kids to know that and to realize the rules are being rewritten, because the people that are making laws don't understand what's happening. So I'd be like, build, create a compelled belt. What do you care about? Go build it. Go build a company that does something with frogs. And maybe the last thing I think I'll say is for our kids is I think the benefit is I grew up on food stamps. I didn't have a lot of opportunity. I was scared to fail. Like I just was scared. And now I think we should be telling, I want my kids to fail over and over and quickly. And that is such an incredible luxury to give our children, which is you get to fail and I'm going to protect you. But if you are running towards achievement and making the world better and you fail, I got you. Not going to help you if you fail with your boyfriend or girlfriend or something like that's your responsibility. That's on you. That's on you. You got to be able to function as an adult. But if you fail because you were trying to bend the arc of justice or be a builder, I too invest. I'm an investing in my kids before I'm investing in anyone else. I love that you literally gave me goose bumps with that mine. And I'm going to share it with my kids. And that is an amazing way to end our conversation. I don't thank you so much. Thank you. It's so nice to have this time together. Thank you so much for listening. We'll be back next week with a new episode. High and years of AI is a weight-wet original. Our executive producer is Eve Trobe. This episode was produced by Megan Tan, video editing by Eric Purcell. Our senior talent executive Stephanie Stern, mixing and mastering by Brian Pugh, original music by Ryan Holiday. You can join the conversation on LinkedIn, Instagram, TikTok, YouTube and X. Just search for at High and years of AI.