Government benefits are broken. Is AI the fix?

Note: Transcripts are automatically generated from episode audio, and are not fully corrected for spelling, grammar, and formatting.

PHAEDRA ELLIS-LAMKINS: I think the new rules of our society are going to be written by technologists. How society operates will be written by code. It will not be written by people who pass laws. I think AI can be transformative, and I’m seeing programs work more quickly. Programs work better for the people who rely on them 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.

RANA EL KALIOUBY: When people stand in line for hours to get their driver’s license or hear about hundreds of thousands of people who are wrongfully denied government food assistance, they rarely think, “I can fix this.” But Phaedra Ellis-Lamkins 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 Phaedra, we get into how she’s making all of that possible with AI.

[THEME MUSIC]

I’m Rana El Kaliouby, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution. Hi, Phaedra. Welcome to Pioneers of AI.

ELLIS-LAMKINS: Thank you so much. I’m happy to be here.

EL KALIOUBY: I’m so excited for our conversation. I saw you present at Masters of Scale Summit last year, which was great. What was that experience like for you?

ELLIS-LAMKINS: It was great. It’s hard not to enjoy being in a place with some of your favorite people, talking about some of their biggest ideas. It was an amazing experience to be there.

Copy LinkWhy public benefits systems keep failing people

EL KALIOUBY: I would love for you to ground us in the work Promise is solving. It does seem like navigating the government system is a real hot mess.

ELLIS-LAMKINS: Yes.

EL KALIOUBY: Can you describe America’s system of public benefits? How does that work, and what is it like as a user or a citizen navigating that system?

ELLIS-LAMKINS: Promise focuses on 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, and 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. In most systems we’ve seen, it’s clear they are not operating in the most effective way possible. At the same time, it’s not just the public sector. We see whole cottage industries of consulting firms getting paid $300 million to $500 million to do a modernization that barely gets off COBOL, which is an aging programming system.

EL KALIOUBY: My parents programmed in COBOL.

ELLIS-LAMKINS: Yeah.

EL KALIOUBY: Did they?

ELLIS-LAMKINS: Yes.

EL KALIOUBY: Does it still exist?

ELLIS-LAMKINS: Yes. It still exists in many places, and the fact that we still need folks who understand COBOL, or that information is still stored on a server under someone’s desk, is not the best we should be able to offer our society in 2026.

EL KALIOUBY: My parents are over 70, and they got their Green Card a couple of years ago. We’ve been trying to get them health insurance, and it is an absolute nightmare. It’s so frustrating, and it’s creating so much tension within the family because I’m responsible for getting this figured out, but I’m really struggling.

ELLIS-LAMKINS: Yeah.

EL KALIOUBY: I don’t know where to go on the website or who to talk to. I don’t know if you have any thoughts for us.

ELLIS-LAMKINS: I have a lot of thoughts. One reason we exist is because of what’s happening with your parents. The law changes a lot. Right now, for example, your parents would probably get Medicare or Medicaid, depending on their income, and when you look at what is required, there are things like work requirements.

EL KALIOUBY: But they haven’t worked in the U.S. ever.

ELLIS-LAMKINS: Right. 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. Part of what’s hard about these programs is that you have state laws, federal laws, and changing laws, policies, and procedures. If your parents were in the state of Florida, I could help solve this for you in two seconds —

EL KALIOUBY: Oh, my God.

ELLIS-LAMKINS: Because they are our clients.

EL KALIOUBY: Okay.

ELLIS-LAMKINS: The reason is that we have a system they use through AI that can actually manage policy change, audit and say, “Hey, this doesn’t work. This is right,” and then change the system in real time. Part of what we’re talking about is that you might go to a website, and the law changed in January, but the website didn’t update with the new policy change. So you’re still going to be allowed to apply even though your parents aren’t actually eligible unless three things are met, and that is the fundamental problem. We have not updated systems to recognize that laws change and policy changes, and that the system should move as quickly as we pass those policies. It should get easier to use, not harder to use.

Copy LinkThe difference between software and consulting in government services

EL KALIOUBY: Can you give us a sense of how many people are using these systems and how much money is flowing through?

ELLIS-LAMKINS: Yeah. We spend more in this country on health and social services than we do on defense. 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. 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. Part of what I feel so excited about is that we work with the state of Florida, as an example. We’ve integrated with the utilities and with other social programs. So if you, for example, apply in California, you come into an office, you bring a copy of your bill, you might fill out a form online, and you bring your income information. In 2026, we should not run programs like that. In Florida, we’re integrated with the utilities, so we know what the bill is.

EL KALIOUBY: You have the information.

ELLIS-LAMKINS: Right. As we think about it, it’s more effective for us to integrate. It’s also more accurate, and it decreases the risk of fraud. 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 a copy? It doesn’t make sense. I think part of what’s hard about health and social services is that it’s largely dominated not by software companies, but by consulting firms.

EL KALIOUBY: What is the difference between a tech company doing this work versus a consulting company?

ELLIS-LAMKINS: Yeah, it’s a good question. I think the difference is that a tech company is measured based on outcomes, and a consulting firm is measured based on hours and the number of people who 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 done as quickly as possible, as efficiently as possible, because I’m measured based on outcomes. As a society and in 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 the hour? It’s the wrong incentive.

EL KALIOUBY: It doesn’t align the incentives with getting the work done in the most efficient way.

ELLIS-LAMKINS: No. It doesn’t make sense.

Copy LinkHow Phaedra built a mission-driven tech company

EL KALIOUBY: OK. So I want to talk about what exactly Promise does.

ELLIS-LAMKINS: Sure.

EL KALIOUBY: But before that, I want to wind the clock back to how you got started. 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 with a for-profit organization. We felt like that would be more sustainable. So I’m curious, when you were starting Promise, did you have to think about that? Also, what led you to start Promise? Maybe tell us a little bit about your backstory.

ELLIS-LAMKINS: Sure. I had a really different life experience than most people who founded companies, which is that 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 came to understand 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. I 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. For me, starting Promise was really about how you build a company so that, in the same way we’ve done it for the defense sector, you expect innovation to be centered in health and social services, since it’s where we spend the most money as a society. It is also the place that your parents and our children will depend on, and it shapes what we think the future looks like, especially in an AI world.

EL KALIOUBY: Yeah. I have to ask you about your experience with Prince.

ELLIS-LAMKINS: Please.

EL KALIOUBY: I know you get this question a lot, but how did you end up with that kind of relationship and connection, and what did you learn the most?

ELLIS-LAMKINS: It was really interesting. I have a friend, Van Jones, who, if you’ve ever watched CNN, is on CNN.

EL KALIOUBY: OK.

ELLIS-LAMKINS: He introduced me to Prince. I was pregnant, and he asked me to work on a project with him. 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. What do you mean? What would that even mean?” I ended up working with him, and it was really incredible for a couple of reasons. One, I’d spent a lot of my time working on justice issues, and what became very clear to me is that people liked doing fun things more than they liked doing hard things. When you’re having a rally, you’re like, “Come do this.” What I discovered is that when you invite people to a concert, they want to come. If it’s fun, people want to do it. And I was like, “Oh.” So I learned something: we’re asking people to do hard things 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.

EL KALIOUBY: Like ambition?

ELLIS-LAMKINS: It isn’t just ambition. Maybe I’ll give you an example: He was frustrated one day, so he kicked everyone out and sound-checked every instrument himself.

EL KALIOUBY: Wow.

ELLIS-LAMKINS: And so you realize when someone has taken that time and devoted 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. I just thought, there is an incredible commitment to excellence. That’s why when people tell me, “I have a daughter, and she’s like, ‘I don’t want to practice,’” I say, “Prince practiced every day. You can practice.”

EL KALIOUBY: OK.

ELLIS-LAMKINS: For sure.

EL KALIOUBY: That’s good.

ELLIS-LAMKINS: You can do it.

EL KALIOUBY: Don’t go anywhere. We’ll be right back after this short break.

[AD BREAK]

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 govtech companies, right? Companies serving the government sector. I often have two big questions or concerns. One is, how big is this opportunity?

ELLIS-LAMKINS: Yeah.

EL KALIOUBY: And you’re kind of addressing that this is actually pretty big.

ELLIS-LAMKINS: Yeah.

EL KALIOUBY: And two, it just sounds like a nightmare to even get into the system as a supplier, especially since my sense is that a lot of government agencies don’t want to do the wrong thing. So I’m curious about your experience there.

ELLIS-LAMKINS: 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. I think what’s harder than in other markets is that you have forces consistently working against you that have a lot of infrastructure. An example might be: I have 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. 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 that the Defense Department has largely made a decision that it is OK to innovate. No one would expect the Defense Department to build its own plane. It wouldn’t even occur to you. I think it’s largely because, in defense, people have made the recognition that intelligence is valuable, that the way you manage information is really critical. 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.

Copy LinkHow Promise reduces errors before benefits go out

EL KALIOUBY: Can you walk us through a couple of examples?

ELLIS-LAMKINS: Sure.

EL KALIOUBY: Of what life was like before Promise existed, and then you came along, and what does it look like now?

ELLIS-LAMKINS: Maybe I’ll give an example of the State of Mississippi, just based on thinking about your parents. We work on work requirements there, which, for folks who don’t know, means some states have work requirements. There was a new law passed that has very specific work requirements, and in a lot of places, in the State of Arizona, as an example, there’s been almost a half cut in food stamps, the number of people on food stamps, a third of whom are children. So we know fewer children will have access to food stamps. In 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 that the system is working well. So we actually executed a program there that is, think of it as automated work-requirements reporting. What we’ll do is reach out to you first to make sure you’re aware of the work requirements. What are they? What do they require? We’ll do that by text. Then we’re going to allow you to do that reporting through us by text.

EL KALIOUBY: Cool.

ELLIS-LAMKINS: And then what we’re going to 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. You give us the ability to access it, we ask you ahead of time, and then we’re able to pull that information. In Mississippi, they had almost a 2 percent decrease in their fraud rate, or their SNAP error rate. Why that matters a lot is because basically what the administration said is, “We’re not giving you states money to pay for your food stamp administration if you don’t decrease the fraud or error rate.” What we saw is Mississippi did great. When we looked at other states, they brought their error rate down. They’re killing it. Our basic premise — we were talking to someone this week about this — is that 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 them access to information, and stop it before it becomes an after-the-fact fraud problem.

EL KALIOUBY: 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? Why are these things linked?

ELLIS-LAMKINS: It’s a great question. 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. So you’re looking at an income requirement. Health care is the same thing. For food stamps, as an example, there’s a work requirement, which means that in addition to having income below that level, 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 you have to meet to be able to receive that benefit.

EL KALIOUBY: So these are all the qualifying criteria, in a way.

ELLIS-LAMKINS: Exactly. And they continue.

EL KALIOUBY: Okay.

ELLIS-LAMKINS: So it is not just at application.

You have to continue. So the work we’re doing on work requirements is because you have to keep doing it to keep the benefit.

EL KALIOUBY: Yeah. So basically, you apply, and there are all these criteria, including work requirements. What is really magical about what Promise does is that you’re able to almost not auto-populate, but connect to the systems that have the information, so you’re able to continue to pull this information?

ELLIS-LAMKINS: 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 not occur to you, “It’s June, my 18-year-old is graduating.” You don’t think, “Immediately, I have to go announce 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 make a change with you. “Okay. Oh, yep, it changed.” So that’s the first thing. 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? Is there 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 you get the highest-quality data, because that’s ultimately how you reduce the most fraud. The other systems are designed, once you find fraud, let’s audit it, let’s do something about it. Promise is like, how do you make sure people have information? How do you get higher-quality source material? And how do you stop it before the payment goes out? That is our model.

Copy LinkHow AI agents speed up public assistance

EL KALIOUBY: So let’s talk about the role of technology and AI specifically in Promise. When did you decide to incorporate AI into the product, and actually, what kind of AI are you using?

ELLIS-LAMKINS: So I think, for us, it’s important to probably make a distinction: where is there AI, where is there machine learning, just to nerd out for a second.

EL KALIOUBY: Love it. Love nerding out.

ELLIS-LAMKINS: A little nerding out. And I would say we’re more machine learning than anything else.

EL KALIOUBY: Great.

ELLIS-LAMKINS: And the difference might be, for the folks who aren’t nerds, that what you want to do is 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 Waymo, right? First, you started with humans driving. Then it drove with the humans in the car. Then it drove without them. You need those kinds 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 looking at blurry photos. Another agent responding because you didn’t send something. But we wouldn’t expect there to be one kind of generic agent that’s able to do all of those things.

EL KALIOUBY: Because I would imagine every agent or machine-learning algorithm is trained very specifically for a specific task and on its relevant data.

ELLIS-LAMKINS: Exactly. So maybe you’re applying for something that requires payroll records, but it can only be these specific dates. We have an agent that only looks to make sure that wage information is within 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, are much more likely to continue to succeed. We can make a decision in 22 minutes.

EL KALIOUBY: Now, I’m going to play devil’s advocate for a second.

ELLIS-LAMKINS: Please.

EL KALIOUBY: I can imagine somebody listening to this, and they’re thinking, “Oh my God, now AI’s going to make a decision on whether I’m going to get this SNAP benefit or this social service.” What would you respond to that?

ELLIS-LAMKINS: Well, I wish that were true, because I think you would probably get a better decision.

EL KALIOUBY: Let’s talk about bias in AI versus human bias, I guess.

ELLIS-LAMKINS: Totally. Let’s talk about it. We see human bias based on people’s own experiences. We see human bias based on deserving or not deserving. When you look at the impact of high caseloads, I guess what I would say to the person who would be concerned, and which most of America is now apprehensive about AI, is that it is coming. It is like protesting the automation of cars. 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? 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.

EL KALIOUBY: I appreciate you saying this because I think it’s so important to have a high bar for what can get shipped, right?

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 taking that seriously.

ELLIS-LAMKINS: 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, “Ugh.”

EL KALIOUBY: Yeah.

ELLIS-LAMKINS: Especially for things like social services because what I worry about is if someone does fraud because there’s been bad translation and it asks for something different, right? 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 that, and you want the people to succeed. So I think bias is real because part of it is people just have had different life experiences. Even here, I’ll give you an example. We had someone on our team, super smart, and we were having a conversation about paychecks. And I was like, “Oh, you’ve 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 if you make a payment at 10:59 p.m., it makes sense to do it 30 days later at 10:59 p.m. And I was like, “No, no, take it in the morning.” They’re like, “Why?” I was like, “Oh, because it’s payday.” And they’re like, “But the paycheck is going to be gone by the end of the day.” And then they were 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 experience is, as 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. So you’ve got to go figure out those things.

EL KALIOUBY: Yeah. How do you bring that perspective, the diversity of lived experiences, which is going to be so crucial in making the product work?

ELLIS-LAMKINS: It’s a really important point. One, I think the group that often gets left out is 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 government workers who often get vilified, but a lot of them want to do really well, so we should honor that they want to do that. And I think the way we think about it, because we’re nerds, is we try to give metrics to it. So one of the things most folks think is crazy until you do it is we introduce customer service metrics, or CSAT scores, and ask every person who fills out a form or has an experience with us to rate us on the customer service experience, one through five.

EL KALIOUBY: Oh, cool.

ELLIS-LAMKINS: 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 to tell us what your experience was like. And the thing that’s been so remarkable for us is, we’re talking about Mississippi, one of the quotes was from, because we ask both the people who work for the government and the people who are getting the benefit, and they said, “This is the best part of my job.”

EL KALIOUBY: Yeah.

ELLIS-LAMKINS: I was just like, “This is the best part of my job.”

EL KALIOUBY: We’re done.

ELLIS-LAMKINS: Done.

EL KALIOUBY: Done here, yeah.

ELLIS-LAMKINS: And in Mississippi, our average score is a 4.8 out of 5. 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 if we were designing a product for consumers, we would say, “You don’t want so much content,” because we know every time we add content or add a next screen, people drop off. So we should be trying to think about the least amount of drop-off, the most accurate information, and 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, with a good experience, so that people can participate in society.

Copy LinkWhy trust and privacy shape responsible AI

EL KALIOUBY: We’ll be right back after this short break.

[AD BREAK]

One of the things that we talk a lot about on this show is the commitment to security and privacy, which I imagine is super important for Promise anyway.

ELLIS-LAMKINS: Super, yep.

EL KALIOUBY: Yeah. So how have you implemented all of that into the platform?

ELLIS-LAMKINS: 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.

EL KALIOUBY: Okay.

ELLIS-LAMKINS: So I’m always 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 that you shouldn’t have an incentive to misuse information. When we get more information, it’s only toward one cause. It’s not toward 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, “Oh, because you know these things.” And I just think any kind of graying of those lines 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. So I think there’s no internal mission or reason to do it, right?

EL KALIOUBY: 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.

ELLIS-LAMKINS: Yeah. It’s a great question. I’m worried that we’re trying to make people consumers instead of builders.

And so, in general, I think we are trying to get people to use some of these services as a Google alternative instead of building. So we want to figure that out. And then the second thing I worry about with AI is, as we think about the very real consequences of things like data centers or other pieces, 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 who rely on them the most. But there aren’t 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 superintelligence 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?

EL KALIOUBY: Right.

ELLIS-LAMKINS: They’re training it to make sure that they flourish.

EL KALIOUBY: Yeah.

ELLIS-LAMKINS: And so 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. So I guess what 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 came out of the labor movement 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.

EL KALIOUBY: Yeah. I love your line that AI is going to work without you, maybe with you, but wouldn’t it be awesome if you made it work for you?

ELLIS-LAMKINS: Right.

EL KALIOUBY: Yeah.

ELLIS-LAMKINS: Because I was thinking about healthcare as an example. So I was like, everyone’s right. It’s not great. But the thing I worry about is people are imagining the impact for themselves as someone who has healthcare, who has resources. They’re forgetting about a 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 supplement their lives, instead of measuring it by what it does for someone in San Francisco or New York who has resources, doesn’t make any sense to me.

Copy LinkWhy young builders should create, not consume

EL KALIOUBY: 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. Also, my daughter’s 23, my son is 17 1/2, and we try to talk about these core values. I don’t know how old your kids are, but what’s your advice to young people who are trying to both be impactful and do well?

ELLIS-LAMKINS: 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.

EL KALIOUBY: I love that.

ELLIS-LAMKINS: 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. You want to be on the computer for 50 hours? Go hard and go 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.” She’s 14, and she’s not allowed to be on social media. I want her to know how to read and write well, because I think writing is going to be a skill that not many people will have, and you will critically need it.

ELLIS-LAMKINS: I also think the ability to analyze information matters, because now we’re going to be getting so much information that has gone through someone else’s filter, and it is based on their own truth. One of the things we were talking about is that Google once 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, and even adults to understand, that all we’re seeing is someone else’s perception. The way I think about it is, if I ask 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, right? Which of those is truth? That’s how we have to think about AI. Someone’s truth can be so different that you have to be able to make the distinction of what’s true or not.

ELLIS-LAMKINS: What I want everyone to do is be builders and be super smart and good, because I think we have enough critiquers and 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 who are making laws don’t understand what’s happening. So I’d be like, “Build. Create a company.”

EL KALIOUBY: Build, build, build.

ELLIS-LAMKINS: Build.

EL KALIOUBY: Build.

ELLIS-LAMKINS: “Go build. What do you want? What do you care about?”

EL KALIOUBY: Go build it.

ELLIS-LAMKINS: “You care about frogs? Go build a company that does something with frogs.” Maybe the last thing I’ll say for our kids is, I think the benefit is that I grew up on food stamps. I didn’t have a lot of opportunity. I was scared to fail. I just was scared. And now I think we should be telling our kids to fail over and over, and quickly. 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 toward achievement and making the world better and you fail, I got you. I’m not going to help you if you fail with your boyfriend or girlfriend or something like that. That’s your responsibility.”

EL KALIOUBY: That’s on you.

ELLIS-LAMKINS: Right.

EL KALIOUBY: Yeah.

ELLIS-LAMKINS: That’s on you. You’ve got to be able to function as an adult. But if you fail because you are trying to bend the arc of justice or be a builder, I, too, invest. I’m investing in my kids before I’m investing in anyone else.

EL KALIOUBY: I love that. You literally gave me goosebumps with that line, and I’m going to share it with my kids. That is an amazing way to end our conversation, Phaedra.

ELLIS-LAMKINS: Aw.

EL KALIOUBY: Thank you so much.

ELLIS-LAMKINS: Thank you. It’s so nice to have this time together.