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- [00:00]
Josh Tieringal
This is an iHeart podcast.
- [00:02]
Jacob Goldstein
Guaranteed Human Small businesses are the pulse of every community. They bring people together, create opportunities and drive growth. Chase for Business helps business owners like you with personalized guidance and convenient digital tools all in one place. With that guidance and your determination, you can take your business farther and help build a brighter future for your community. Learn more@chase.com business chase for business Make More of what's yours the Chase Mobile app is available for select mobile devices. Message and data rates may apply. JPMorgan Chase Bank NA Member FDIC Copyright 2026 JPMorgan Chase Co. We spend hours deciding what to buy, but there's a split second decision that can make or break a sale. Do you have the trust to hit buy now? Agentic Commerce is testing that moment more than ever. And that's where PayPal comes in. With 25 years of checkouts, 400 million consumer accounts globally, and the benefit of purchase and seller protection, all of which make sure wherever a purchase starts, it ends with trust. Built for payments, growth and agentic PayPal open built for all business, visit PayPal open
- [01:22]
Josh Tieringal
purchase and seller protections on eligible transactions. Only terms apply. See paypal.com risk-management for details.
- [01:32]
Jacob Goldstein
I'm not like a coffee connoisseur, but recently I tried coffee from a company called Perk and I loved it. And I'm not just saying that because this is an ad for Perk, though. This is an ad for Perc. I really did think the coffee was delicious. Perkins Perc has lots of different coffees to choose from and they color code their bags. The blue bags are mild coffee, the pink bags are wild coffee. You can find the coffee that matches your vibe and get 15% off your next order with promo code problem@percccoffee.com that's P E R C coffee.com promo CodeProblem. Pushkin. I'm Jacob Goldstein. This is what's yous Problem? And my guest today is Josh Tierengel. He's a staff writer at the Atlantic. And and he just wrote a new book called AI For Good. And the subtitle of the book explains the problem that Josh is interested in. The subtitle is how real people are using artificial intelligence to fix things that matter. As you'll hear, I talked with Josh about several of these real people. A professor trying to understand her nonverbal son, a general who ran the logistics side of Operation Warp Speed, a hospital CEO trying to reduce the rate of deadly infections. And one thing just to note here is, you know, and I guess this is not surprising, given the state of AI. Some of these stories are not yet finished. They're kind of stories that are still in the middle. But before we got into the specific stories, I asked Josh what common threads he saw across all these people he wrote about who are, you know, trying to use AI to do good things.
- [03:28]
Josh Tieringal
They're largely stubborn.
- [03:30]
Jacob Goldstein
Uh huh.
- [03:30]
Josh Tieringal
They're really stubborn. And you know, one of the things that I admire, I'm very impatient. And when I see a problem, oftentimes I have what I think is a fairly normal human impulse, which is like wipe the mismatched puzzle pieces off the table and get a different puzzle right. It's frustrating to dig into systems to learn technology. And these people, they don't get frustrated, not to the point of destroying the system. In fact, most of them have no experience with AI or technology. They care about medicine, they care about teaching, they care about solving a really hard problem. And so they just keep looking for solutions. And when something seems promising, their ability to concentrate and learn the technology of that promise and apply it to their problem is through the roof. And so I just kept being awed by the fact that they were curious, they were patient, they were impatient at exactly the right moments when they sensed that things weren't moving. They knew how to deploy either their political capital or their temperature in ways to get things moving. And, you know, I found time and time again that when I would talk to other people who were witnessing these phenomenon, whether it was in hospitals or in government, we would all just kind of be like, yeah, these are the ones making it happen. They're kind of samurai, right? They care about their problem more than other people care about standing in the way of the solution. And they get it done.
- [04:55]
Jacob Goldstein
Tell me about Christy Johnson.
- [04:57]
Josh Tieringal
Yeah, so Christy Johnson is certainly one of the most inspiring, interesting people I've met in my journalism career. So I, like a lot of people, was thinking about AI and human connection and reading all the headlines about the sort of parasocial crisis in which people were dating ChatGPT, falling in love with Claude, realizing that this was mimicking a lot of the worst elements of social media, seeing the studies and getting very, very depressed about what AI would mean for human connection. So I was having that conversation with Rosalind Picard, who runs the MIT Media Lab, and she basically said, look, you got to meet Christy. And so she introduced me. Christy Johnson was a physicist and a brilliant student. She met another physicist in a program. He is an astrophysicist and actually was on the team that took the first picture of a black hole. The two of them fell in the deepest form of nerd love you could possibly imagine. I mean, even the two of them laugh at just how dorky their love letters are to each other. All about physics and math and the world. They get married, and they have a kid, and about a couple weeks in, she's like, there's something wrong with my son. And she's kind of gaslit by the medical profession for a year, just hearing, no, he's fine. Stop worrying. Enjoy your kid. Finally, they get some testing done, and he has a genetic deficiency. And the other six children like him in the world are all severely autistic, epileptic, and completely nonverbal. And so this is the kind of diagnosis that changes everybody's lives within the family. They begin to grieve a little bit for their expectations for their son. But they're also scientists. And Christy and Michael both are like, we should understand this. We should do something about it. A couple years go past, and Christy is, like, no longer all that interested in physics. She's very interested in her son. And so what she set out to do was solve the problem of the more than 1 million nonverbal Americans who are stuck to their parents and their caregivers. That's it for everybody in that universe. The. The universe shrinks.
- [07:18]
Jacob Goldstein
Just. Just to be clear, what is her dream? What is the outcome she hopes to achieve?
- [07:22]
Josh Tieringal
Her dream is that her son and other people like him can interact with people they don't know all that well and share what it is they want and need.
- [07:32]
Jacob Goldstein
So a machine will turn their nonverbal vocalizations into words that verbal people can
- [07:38]
Josh Tieringal
understand, that other people can respond to and interact with. And so she made a bet. She basically thought, look, I see where AI and machine learning are right now. My hunch is that they will get better and better, but what I need is data. And so she created a scientific protocol to get vocalizations from these kids, and she used every tool available. So she went on YouTube, she went on social media, she said, this is me and my son. This is what happens when he's hungry. Listen to that vocalization. And now I'm recording it. And she began to generate a little bit of momentum. She went to the MIT Media Lab and spoke to Roz Picard one night, kind of cold called her, and Roz realized, like, this woman has no qualifications to be at the media lab. But, my God, what an incredible human. She got there, she did her PhD, and she proved that if you can gather enough of these Vocalizations as audio files, you can begin to attack the problem. And so what she learned largely through using AI is at first you have to multiply the files. So a system like ChatGPT or Claude is trained on potentially trillions of data points. In order to become as fluent as it is, she had a couple thousand audio files. So the first thing she did is get her graduate students to figure out how to synthesize more data, how to take those data files, those original sounds, and grow them and multiply them.
- [09:08]
Jacob Goldstein
It's like what they're trying to do with self driving cars, right? Exactly. How cars, like drive on pretend roads, essentially.
- [09:14]
Josh Tieringal
Exactly. It's called synthetic data. And AI is really good at making synthetic data.
- [09:18]
Jacob Goldstein
And they're even. They're working on that for text. Right. Because now the current models basically are trained on the whole Internet. So, like, there's no more real text for them to read. So they need synthetic texts. Right.
- [09:28]
Josh Tieringal
They're out of stuff. Right. And so if you want the training to advance, you have to synthesize a lot of this data. And she's continuing to grow the organic files as well.
- [09:36]
Jacob Goldstein
Tell me about Roscoe.
- [09:38]
Josh Tieringal
Yeah. So Roscoe is a protocol. And basically you can't do what Christy is trying to do without science because you need some sort of rigorous idea of what you're testing for, or else the data that you're recording is meaningless.
- [09:55]
Jacob Goldstein
It's not really data. Right?
- [09:57]
Josh Tieringal
It's not really data. It's just sounds. And so working with a grad student, what she figured out is, okay, we don't need all the data. All the data is unprocessable. We need the right kinds of data for this task. And so what they did is create this protocol where they would run families through a certain kind of test. And it was split timed so that it was standardized. And they would say, for two minutes, give your child a YouTube video. Because we want to hear. And you know, in the autism community, YouTube is great. It cues up the same video as many times as you want. People always have their favorite video. She said, whatever. Their favorite video is queued up at the 1:45 mark. They would introduce a beach ball and
- [10:40]
Jacob Goldstein
buffering beach ball, meaning the spinning wheel that says, like pause. It's loading.
- [10:44]
Josh Tieringal
Yeah. They would pause the video because what they wanted to do was get a vocalization of dissatisfaction and frustration. And so that was a way to do it. And they would say, okay, this is a five minute snack period. We want you to make sure that you are giving them their favorite snack. And we want to hear what that is like. And so they would do these things that are really relevant to her ultimate goal, which is making sure that these kids could be out there in the world talking to other people and having needs met. And so it was a very scientific protocol. And then she would send it to families along with a phone and a camera, and basically say, record it from these various angles, send it back to us so that the data is standardized. It's not just random vocalizations that they're figuring out. And because the population is so specific and because the keepers of the knowledge are so specific to parents and caregivers, it took her a while to figure out that's how you actually want to get the data.
- [11:44]
Jacob Goldstein
Yeah, it sounds like a lot of work. It sounds hard to scale.
- [11:49]
Josh Tieringal
Yeah. And it would be hard to scale if not for synthetic data and if not for all of the advances on the other side with translation.
- [11:56]
Jacob Goldstein
Yeah. So you don't have to scale that much is what those things.
- [12:00]
Josh Tieringal
Well, you don't have to scale it nearly as much. Right. And what would have been a problem project that would probably have happened over three scientists lifetimes is now maybe only over half of her career. And that's a real form of progress. And so even though Christie doesn't have the kinds of funding that Google or OpenAI have, she's able to kind of surf in their wake. And they've made these progressions on things like zero shot translation, which some people may have heard of. You know, historically, we would translate things by taking one set of language, decoding each word, and rewriting it in a different language. And up until about 10 years ago, that's what machine translation was. Zero shot translation is actually translation at such a deep level that you're not translating language to language. You're basically translating every language to every language simultaneously. And then we got to a certain point recently where we can translate based on audio waves. And so if you can translate audio waves, it becomes much easier to translate vocalizations from people who don't use words at all. And so she, you know, you mentioned a lot of these stories aren't done. This is the least done story. Right. And you know, what's interesting is that technology has already changed the lives of lots of autistic people because, you know, she, she showed me her phone, right. So we were having dinner on her porch, and her son Felix, who's lovely, sweet in many ways a normal American teenager, just picked up the phone and went off to go do something. And then he came back and he Dropped off the phone, and she and her husband showed me photos that he had taken of what he's fascinated by. And so some of them were hairbrush bristles that were actually these. These kind of beautiful plastic forests. He'd really used a filter, and he'd taken them and hundreds of these photos. And before that, he'd taken photos of windows and was really interested in the way windows around their neighborhood worked, or castles or sketches of castles. And she said, like, before the iPhone, when he's staring at something, I would have no idea what he would have been staring at. But here's this really rudimentary piece of technology, at least now. And through that, I'm able to understand what he's looking at. I don't necessarily understand why he's fascinated by it, but I know that he's interested. And I can make his life richer by exposing him to more of it. And I can frustrate him less by making sure there's always a hairbrush around. And so those kinds of things I have to say, they make me more optimistic that if we can wrestle this technology, tech doesn't always have to be bad. Like, we're not fated to the world sucking because technology's coming out, but we do have to figure out how we want it to work. And so I left Kristi feeling like she's going to get there. And I know that that's not guaranteed, but she is so stubborn. She's so motivated by love and by problem solving. And the tech is moving so fast that she's been at this 10 years, right? And 10 years she's only gotten so far, but the next 10 years are gonna move so much faster, and more people are gonna come and attack the problem. And it wouldn't surprise me if 10 years from now she's successful.
- [15:20]
Jacob Goldstein
So another story you tell in your book is about Operation Warp Speed, and in particular, it's about the logistics of getting vaccines out to the public. And, you know, I knew a moderate amount about the vaccine development side, but. But I really didn't know anything about the getting the vaccine out to the world side. And I certainly didn't know that it was an AI story. So tell me about Operation Warp Speed and in particular about this guy you write about, this general named Gus Perna.
- [15:51]
Josh Tieringal
Look, I was writing, and I was hungry for understanding and trying to figure out how exactly machine learning and artificial intelligence integrate into society in ways that are helpful. And so I kind of tripped across this video of Gus Perna, who oversaw Operation Warp Speed, who is not A technologist by any means. He's a logistician. He oversees generally, or did before he retired, the delivery of munitions and uniforms and food so that we can fight wars. And he was placed in charge of operation warp speed and the delivery of it so that hopefully Big Pharma can execute on all these vaccines. But then what? How do they get into plastic vials? And how do those vials get delivered to everything from a CVS to a doctor's office to in rural areas, sometimes a general store that has no digital footprint. And so he shows up in Washington and discovers that there's no plan. Like he is the plan. And so in Washington, like it or not, everything starts with consultants. Everything. And so people come in and they pitch him on things. And again, not a technical guy, but he keeps hearing these crazy pitches over the course of an hour. Somebody's like, we're gonna put a medical blockchain together. And in a couple years. And he's like, no, somebody else pitched like, hardware. We'll just put a hardware device in every doctor's. And he knows enough to say no. And finally he meets with these two reasonably seeming people who are like, look, you have a data problem. This can all be solved if we get the right number of data streams flowing into the right interface so that you can actually see everything from plastic vials, metal needles, refrigerated trucks. Because remember, half of the vaccines needed to be really cold refrigerated for delivery to track which states might want Johnson and Johnson versus, like, you have to build a whole civilization to do this stuff. And they said, look, you can build a civilization in data, probably won't cost that much, but we will need you to solve the human problem, which is getting these various agencies to play ball so we can actually get to the data, clean it up and feed it to you. And so that's a machine learning story. And when I went down the rabbit hole of how it actually happened, it was really instructive because obviously the end state was a huge success.
- [18:12]
Jacob Goldstein
Underrated. Every chance I get, I want to scream about how underrated war speed is weirdly politically orphaned, right? Like the Republicans dec, they don't like vaccines, even though they did it. It was like, as far as I'm concerned, the great success of Trump's first administration. And then because it was Trump, the Democrats can't say it was great, right?
- [18:31]
Josh Tieringal
Exactly. And so it's wandering out there, this great American success story that just got buried by the stupidity of our politics. But we did it, and we wouldn't have been able to do it without really successful machine learning and AI.
- [18:45]
Jacob Goldstein
So how did it work? Why was that an AI problem? And what did they actually do?
- [18:49]
Josh Tieringal
So it was a data problem, but also an actionable data problem. Right. And so there's a company called Palantir. Very, very provocative company. A lot of people respond poorly to its name and it has this kind of mystical aura to it. In part because it was founded by Peter Thiel and co funded by the CIA, and in part because it's named for the Stones in the Tolkien trilogy. But really all they do is plumbing.
- [19:17]
Jacob Goldstein
I am not reflexively anti tech or anti company. I have some amount of wariness toward Palantir, I guess because I associate them with domestic surveillance at some level, like that piece of it, I mean, but I don't know it that well. I've never interviewed somebody from Palantir. I haven't read a book about them. So, like, tell me about Palantir as a company. I guess first of all, like the side that makes people wary. And then I'm curious more generally how they operate, what differentiates them, what they actually do as a tech company.
- [19:46]
Josh Tieringal
Yeah. So as a company, they're created in 2003 by two friends who have no business being friends. One is Peter Thiel, who I think most of your listeners really know about. He's sort of largely libertarian, Republican, big Trump donor, has very controversial stances on pretty much every possible issue.
- [20:06]
Jacob Goldstein
Including the Antichrist, Weirdly, including the Antichrist.
- [20:09]
Josh Tieringal
He really finds a way. His friend is a guy named Alex Karp, who. Who is kind of his polar opposite. He's New York born, he's half black, half Jewish, largely identifies as a socialist, a big donor to Hillary Clinton and Kamala Harris. They met in law school, they argued all the time. They agreed about the need for this kind of technology. And the only other thing they agree on is that in general, the world is better when America is a strong actor and American values like freedom are propagated throughout the world. So that's what they agree on.
- [20:43]
Jacob Goldstein
Do you think that the liberal antagonism toward Palantir is misplaced or do you think it's valid?
- [20:49]
Josh Tieringal
In some ways it's incredibly complicated because it has now grown to include not just surveillance, but Alex Karp's position on Israel, which is that Israel should be supported by Palantir products, just as Ukraine should be. And so there's lots of different valences where people can find something they don't like about Palantir. And by the way, I Respect all of them.
- [21:10]
Jacob Goldstein
Yeah. And so just as a tech company, what is Palantir's thing? What do they do? And in particular, what did they do in Operation Warp Speed to get vaccines out to the public?
- [21:22]
Josh Tieringal
I spoke to their chief architect and he's like, look, we're the mole people of Silicon Valley. We take these data streams, we lay them out, we clean them, we integrate the data from various different places. So like for instance, a CVS and a Rite Aid and a mom and pop place and a trucking company. And, and we put them together so that on an iPad or a laptop, someone could play the pandemic like a video game. Right?
- [21:47]
Jacob Goldstein
Play it like a video game. Meaning there's like a crazy dashboard. So you can see here's where the people are and here's where the vials are, and here's where the vaccine is and here's where the trucks are. And we need to get these trucks to this vaccine, to these people. That's the video game you're playing.
- [22:01]
Josh Tieringal
Yeah, and constantly up to date and constantly accurate. And literally an administrator can sit there and just move things around and know that they're actually going to the right place. Yes.
- [22:12]
Jacob Goldstein
A wildly hard problem. Like, it's not sexy like developing a vaccine. And we've heard a lot about MRNA vaccines, but like, figuring out where the actual vaccine is is totally non trivial.
- [22:24]
Josh Tieringal
Yeah, it's a huge deal. And so there's two problems you're solving, right? One is the technical problem, and that involves a lot of really dull, really unglamorous work, which is like trying to figure out why this pipe, you know, it's because it is pipe coming from a distribution center is broken. And so AI can attack that.
- [22:42]
Jacob Goldstein
When you say pipe, do you mean the pipe through which data is flowing? What do you mean pipe in this instance?
- [22:47]
Josh Tieringal
So to build a data pipeline is basically a work of code, but everybody's got lots of different code. And so when your data pipelines are built in many, many different codes to many different standards, they have to be standardized. You have to be able to see, oh, okay, this data coming from CVS matches up to the data coming from the trucking company. Um, it's clean, it's, it's modern. Most of the data that they were dealing with, in fact, was not modern in any way. It was, it was created a decade ago. Right. Using old code and, and often had bad information. And so you need to standardize it, you need to clean it. And then where it really gets fun is you can use those pieces of information to recommend actions to note that like, oh, you know, you're overly distributing in one area, you could be moving here.
- [23:37]
Jacob Goldstein
Yeah.
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Josh Tieringal
In fact, this rural area, for instance, might really want the one shot doses, whereas an urban area might want to.
- [23:46]
Jacob Goldstein
And those suggestions might be coming from the. From the machine learning model, from the AI, basically.
- [23:51]
Josh Tieringal
Correct. Yeah. So that while the, you know, the very overtaxed humans dealing with political problems and interagency problems are on the phone, the AI is just kind of nudging them toward, like, easier ways to make this whole project happen. So, like, the technical is one problem, and then something that's kind of a light motif of your show is the humans are another problem because we create our own systems and there is nothing more complicated than the federal bureaucracy. And I mean that oftentimes with admiration and sometimes not with admiration. And so inside of the CDC are many different agencies, and a lot of them were even in COVID 19 had very sharp elbows. Yeah. They didn't want the Department of Defense, which was nominally overseeing this effort, getting involved. They thought, no, this is our territory. What do you possibly know about this? And General Perna was so important because he really didn't know that much about technology. He really didn't know that much about vaccines. What he knew about was getting stuff done.
- [24:53]
Jacob Goldstein
Yeah. You tell the story of a moment when he's arguing with a governor about whether some amount of vaccine has been delivered to the governor's state. Tell me about that.
- [25:04]
Josh Tieringal
Yeah. So once the vaccines started to get delivered, this is where the video game element, the dashboard element, really became important because they could see trucks pulling up to a registered refrigeration point or warehouse point in every state. And there were states that were really eager to get the vaccines. And the governors felt that their entire political futures depended on getting it and distributing it and being able to tell their citizens, like, hey, Massachusetts, we got it, we got it. You can go to these places. Remember, there are people frantically refreshing their browsers to try and find weights up to three, four weeks. And so somebody would say, hey, you promised me this dosage, I don't have it. And he said, oh, but you do. Here's who signed for it. Go get them. I'm gonna stay right here. You go get them. And so what he also did, and he had plenty of people working for him who helped with this. These systems need accountability. And what I think, what I saw time and again in reporting on the book is that the accountability is human based. We respond to human beings saying, actually, I'm calling your bluff because I can see the answer here. Actually, I'm gonna follow up with you because you said you would do this pilot with us and you didn't. And if you don't have the right human in that chair with the right temperament, the right force, the right seductive charm, when that's what's called for, it's going to fail. And so I can't say enough like my conversations with Perna. I just came away thinking we should put him in charge of a lot of stuff.
- [26:35]
Jacob Goldstein
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- [30:03]
Josh Tieringal
Mostly because they asked me to come in and so I was really, as
- [30:08]
Jacob Goldstein
you know, looking for his keys. Under the streetlight.
- [30:10]
Josh Tieringal
Yeah. And also, listen, healthcare is a universe, right? So what I really wanted to explore is the as much of that universe as possible. Cleveland Clinic's one of the top systems in the world and the United States, and they're run by a really interesting guy named Dr. Tomas Mihaljevic. And so what he told me at the beginning is I want you to wander around because what we do with AI is going to be different than what a lot of other people do. First of all, we actually have one major priority, which is the treatment of our patients. And so that means that when technology comes to the Cleveland Clinic, it's not run by technologists, it's run by doctors. And that is going to eliminate certain partners right off the bat because most software folks believe that software is the most powerful force in the world and, and they don't want to explain themselves or work within some sort of old fashioned medical system. I spent a fair amount of time with the chief technical officer who had come from outside of healthcare and was recruited specifically from outside of it. And he obviously knew tons about technology, but knew very little about healthcare.
- [31:12]
Jacob Goldstein
He seemed somewhat frustrated in your book.
- [31:15]
Josh Tieringal
Yeah, he is frustrated because he's used to being in charge and what he learned in his first couple years, it's like, I'm not in charge.
- [31:24]
Jacob Goldstein
Yeah, and like, every doctor is in charge in a weird way. Right. One of the maybe distinctive things about healthcare, and I know doctors don't see it that way, but doctors have a lot of autonomy, and a lot of them don't want AI.
- [31:38]
Josh Tieringal
They have a ton of autonomy, but they also have a ton of authority. Yeah, Doc. You know, medicine is a hierarchical business, and you want it to be that way. When you're the patient, you want someone who actually says, I see all the data. I have all the inputs. Here's what we're going to go do, and then distributes the tasks back. Technology doesn't work all that well that way. And so Rohit Chandra came in, and
- [32:00]
Jacob Goldstein
he's basically like, this is the chief technology officer.
- [32:03]
Josh Tieringal
Yeah, the cto. And he's like, I'm supposed to help turn this place around with technology. The business is terrible. Our margins. You know, Cleveland Clinic is actually one of the more successful nonprofit hospital systems, and it's got a 2% margin, which most people would find horrifying. And so he goes at it like a technologist, and time and again, he runs into this wall, which is doctors saying, well, I don't want to do it that way. And sometimes with good reason and sometimes not. But again, you can't just solve the technical problem. If you want AI to actually work for good, you have to solve both the technical and the structural problem of what you're trying to fix.
- [32:42]
Jacob Goldstein
So actually, at the Cleveland Clinic, you encountered this company called Bajan Health. And as it happens, I interviewed the founder of that company, Suchi Saria, a few years ago on this show, and they built this system where they use AI to flag when patients are likely developing sepsis. And, you know, that was a few years ago that I talked to her. And so I'm curious, how's it going? How is it working in the world at the Cleveland Clinic?
- [33:07]
Josh Tieringal
Yeah. So she's brilliant. And I talk to her as well. And from a technical perspective, her ability to understand not just what sepsis is, but again, to create a bunch of data streams and integrate them and create weights between the streams to create a flag for doctors to say, hey, this might be sepsis. And just as a reminder, sepsis kills more people every year than breast cancer, prostate cancer, and the opioid crisis. Yeah, 350,000Americans a year. And it happens in hospitals. And so the clinic, which loses in 2021, I think, lost about 3,000 patients to sepsis, said, well, we can't. This is crazy. It may be the industry standard, but it's crazy. So they brought in Bajan. I will tell you that the founder, when first approached by the CTO of the Cleveland Clinic, said, yeah, I'm not sure we're interested. She said, you guys are a really big system. Your doctors are going to be resistant. You're kind of a pain. Prove to me that you actually want to implement this in your hospital. And the cto, who has a similar attitude about medicine, was actually like, oh, I see a kindred spirit. I'm going to talk up the fact that our doctors are committed and we're going to do this. And so they went into Cleveland Clinic and they really piloted for a year, and it was a hard year. They had to figure out how it works, not just in, you know, community hospitals, in the emergency room and other places, but like in the ICU. And the Cleveland Clinic ICU is different even than most other healthcare ICUs because it's a world famous institution. People come in from all over the world, and a lot of sepsis looks exactly like a cardiac infection, looks exactly like 16 other things that they're all flagging for. And so if you're an ICU doctor or nurse, your job is basically responding to beeps coming from machinery near your patient. Yeah, it can be a huge pain in the ass. Yeah. And so when a sepsis flag isn't properly calibrated, when it flags too much or flags too little, it's actually not doing its job. So even though everyone may agree on the premise, which is, hey, we want to reduce mortality from sepsis because it's 20, 26 or whatever, you know, you think about your own job, you think about the own annoyances you have in getting your job done. And even though it's life or death, those annoyances mount. And so the software has to work within the way doctors work, or else it's not gonna get used. Well.
- [35:32]
Jacob Goldstein
And I mean, just to be clear, it was a little bit unclear to me in the book how good the software is in that context. Like, is it good enough? Is part of the problem that there's too many false positives, which is a real problem?
- [35:48]
Josh Tieringal
Not outside of the icu. The ICU is the last frontier, because everything is so complicated in an icu.
- [35:56]
Jacob Goldstein
It's the hardest diagnosis, essentially.
- [35:58]
Josh Tieringal
Exactly.
- [35:59]
Jacob Goldstein
So outside of the icu, is there, like, empirical benefit? Are fewer people dying of sepsis?
- [36:06]
Josh Tieringal
Well, the clinic reported a 41% drop in mortality over the course of a year using this software. Now, some of it was because they had drawn everyone's attention to the reduction of sepsis. Right. It's the Hawthorne effect when you say, we're going to do this and everyone's attention shifts. But people are realizing that sepsis is a very perfect for AI kind of problem because it has all of these inputs that are sometimes conflicting and so much data that it can overwhelm a caregiver. And AI doesn't get overwhelmed by too much data. It's really great at processing data at scale very, very quickly. What it often isn't as great at is figuring out how to tell people
- [36:48]
Jacob Goldstein
about it, especially probabilistically. Right?
- [36:53]
Josh Tieringal
Correct.
- [36:53]
Jacob Goldstein
The AI is very good at pattern matching and making a probabilistic assessment. But that last piece seems like the hard. Hard to figure out for humans piece.
- [37:04]
Josh Tieringal
Yeah. And so what Bayesian did, much to their credit, is they heard the caregivers and they said, look, it's not good enough just to send a flag. I get flags all day. Tell me why. Explain why this flagged on this patient and what. What Cleveland Clinic did in the icu, particularly, because they're very aware that this was the hardest place. They basically just set up a workstation. They called it a command center. Command center was a little glorious for what I saw. It was a PC. There's a lovely woman named Dana operating it. She would look at the patients in the icu. She would see the flag. She would hover over the flag, and the flag would say, I flagged this because I'm noticing X, Y and Z. And so she would say, okay, well, what I'm seeing when I go look at the patient is they're actually okay. And I can see why the AI might be confused about this. I'll keep an eye on it. Other times, it was very clear that, like, yep, you're seeing what we're seeing. This is validating what we're seeing. And occasionally it would flag something, and she would go in and say to the caregivers, we have to do a check. Right now.
- [38:11]
Jacob Goldstein
When you step back, having learned what you've learned from the Cleveland Clinic, when you think about healthcare and AI in the next few years, what do you think about.
- [38:20]
Josh Tieringal
I think that healthcare and AI are gonna really be like peanut butter and chocolate. I think it's the best place for AI to come into our lives and have rational uses that make our healthcare better and that make our costs lower. It is going to take change. And so one of the. One of the most interesting people I met at the clinic is this woman named Dr. Rita Pappas, who's a hospitalist and Hospitalists oversee the administration of the hospital, but from a medical point of view. And she started as a nurse and in her 30s, went back to medical school. And so she's seen everything a hospital has to offer, including every type of human in a hospital. And one of the things she told me is like, look, I know that Rohit the CTO is finding lots of resistance across the hospital, and guess what? I'm not the least bit surprised. Doctors are trained a very particular way. And ultimately, like long term, two decades out, if we want AI to take hold in hospitals, we actually are going to need different kinds of people to go into medicine who are less lowercase C conservative, less grounded in their ways, more collaborative, and more willing to trust that technology can do some of the job. The people that she sees going into the profession right now after hundreds of years of selection, are a very particular type. And so she didn't say this in a value judgment. She's very objective about it. But she said, look, that's what it's gonna take. We've already proven that it can work for our bottom line, that it can work for patients. If we're gonna scale it, education for doctors is gonna have to change too.
- [39:59]
Jacob Goldstein
So at the end of the book, you have this epilogue. That's the one part of the book where you're basically like, here's what you should do. You being person who's reading the book, who cares about the world and is interested in AI. What should I do? What's your advice?
- [40:18]
Josh Tieringal
Well, I think, look, there's two pieces of advice here, right? The first is that if you're the kind of person who cares about living in a better world, you're going to have to use these tools to understand what they're capable of. And that responsibility is really frustrating to people. Right. We've now listened to technologists tell us that we have to change our lives for whatever their product is for the last 20 years. And it's annoying. And they get richer and we spend more time adapting. And you haven't seen a lot of public good come from it. But I'm pretty convinced that in the right situations, these tools can help us, can help the republic, can help education, medicine. But if we leave that to the technologists, we're going to get a bad result. So we need to figure out what good they can play in our lives and what kind of good we want to advocate for in the public space. And if you. It's not hard. There are lots of ways to understand what this stuff does. You can do it in a couple minutes, in a day, repeat it. It's a really important component to being a citizen in the 21st century. And it's a lot of what the Pope wrote in his encyclical, which is, we cannot wish these away, but we do have to make them more human. And the way to make them more human is to be insisting that they be more human, so you have to use them.
- [41:40]
Jacob Goldstein
When you say make them more human, what does that mean?
- [41:43]
Josh Tieringal
Left to their own devices, this is going to be enterprise software. It's going to be sold into the Department of Defense. It's going to be used to eliminate jobs. It's going to be used to make corporations more efficient. And I think what we want to advocate for is. No, we want these tools to make many, many people's lives better. It's not to say that capitalism can't continue churning on, but you have to advocate for what you want. And so in the same way that Christy Johnson's use of AI is sitting way downstream of what Google and OpenAI ultimately want, we want to move those things upstream. And so the only way to do it is to know what the tools can do and then insist that we get that kind of utility, because it's just not on the radar for these companies. So that's one component of it. And the other, frankly, is we probably need greater regulation for the negatives to prevent them from happening.
- [42:40]
Jacob Goldstein
Like, what's good regulation?
- [42:43]
Josh Tieringal
The best model I've heard so far is really, you know, kind of using nuclear power and nuclear weaponry as a model. Right. That was the last thing we created that has this kind of scale and power. The International Atomic Energy Commission was established to make sure that we knew exactly how much nuclear material there was in the world and who had control of it and what it was being used for.
- [43:07]
Jacob Goldstein
I mean, can, like, how does that map to this? I get that it says big deal, red flag, but what does it actually mean?
- [43:14]
Josh Tieringal
It means keeping an inventory of all of these models and their capabilities, knowing how much energy they're using, knowing how many chips they have, knowing how many floating point operations, which is the technical term for what these models are processing, each model is doing. You could set a limit on the floating point operations and say, beyond this model, you can't make it, at least for now.
- [43:37]
Jacob Goldstein
So you're talking about, like a global speed limit on the development of bigger models, say.
- [43:44]
Josh Tieringal
I think it's probably the simplest way to go about regulating how AI works in the world and giving governments and societies time to reckon. With the consequences of each new model.
- [43:55]
Jacob Goldstein
And you think it might happen?
- [43:58]
Josh Tieringal
I think something akin to it is going to happen because I think politically actors are realizing in 2026, AI is likely to be one of the two or three biggest issues in the election, and by 2028 it's likely to be the biggest. Look, it's America, and in the end, America tends to regulate after the crisis. That's historically what we do. If I had to bet, I would say that's probably what will happen this time. Something bad will happen and then we will all be looking at the ashes of it and figuring out what to do. I hope that's not the case.
- [44:38]
Jacob Goldstein
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- [47:53]
Josh Tieringal
Yeah.
- [47:54]
Jacob Goldstein
Best Baltimore musician of all time,
- [47:58]
Josh Tieringal
Tupac Shakur did not know a student of Baltimore School for the Arts and the Baltimore Orioles recently gave out a Tupac Shakur bobblehead and the stadium was full.
- [48:10]
Jacob Goldstein
Were you there?
- [48:11]
Josh Tieringal
I was not, but I contemplated going.
- [48:13]
Jacob Goldstein
Is it true that you were on the ground crew of the Orioles?
- [48:15]
Josh Tieringal
Yeah, in high school.
- [48:17]
Jacob Goldstein
What'd you learn?
- [48:19]
Josh Tieringal
I learned that the most disgusting thing in the world is is a tobacco ball combined with bazooka and that most relief pitchers would form that in their mouths, get the juices out, spit it out into the air and then kick it into the outfield.
- [48:36]
Jacob Goldstein
Incredible. What's your walk on song?
- [48:43]
Josh Tieringal
I really love the Rocky theme.
- [48:45]
Jacob Goldstein
Amazing. A cliche for a reason.
- [48:47]
Josh Tieringal
It absolutely is. Like if you are working out and you need to go that last quarter mile, just put it on.
- [48:55]
Jacob Goldstein
I really respect that you went to that instead of some obscure hipster pick. So right that you oversaw times person of the year when you were at time.
- [49:07]
Josh Tieringal
I did, yeah.
- [49:09]
Jacob Goldstein
What's a tip if I want to be times person of the year?
- [49:12]
Josh Tieringal
You know it's really year dependent so I like a fool thought we would just evaluate it based on the most important person of the year and that is not the case. It turns out it's like, oh, but last year we did a bad person so this year we need to do a good person.
- [49:30]
Jacob Goldstein
Well, that makes some sense. There's a mix.
- [49:32]
Josh Tieringal
It does. You want a mix, but it Is not the pure thing that I was raised to believe it is.
- [49:38]
Jacob Goldstein
Were you there? There was a year. I don't remember exactly when it was. I don't know exactly when you were there. When they put like something reflective on the COVID and the person in the year was you.
- [49:47]
Josh Tieringal
You.
- [49:47]
Jacob Goldstein
Was that you?
- [49:48]
Josh Tieringal
Oh, I was there. It was not me.
- [49:50]
Jacob Goldstein
Okay.
- [49:52]
Josh Tieringal
I thought this is pretty silly. You know, we could just make the YouTube guys the people of the year.
- [49:59]
Jacob Goldstein
Failure as an orphan. Success has a thousand fathers. Failure as an orphan. Is that what's happening right now?
- [50:04]
Josh Tieringal
So I was. No, no, no. I genuinely did not oversee that one.
- [50:07]
Jacob Goldstein
Is there nobody who would say I thought that was a good idea?
- [50:10]
Josh Tieringal
I was asked 48 hours before it went to print to write the back page. And so I wrote a back page that, as I recall, was largely about Andy Warhol and the notion of 15 minutes and how that works in a distributed sort of technological. I don't want to go back and look at it or defend it, but I will tell you, it was not my call to put the reflective view
- [50:32]
Jacob Goldstein
on the COVID You know, everybody hated it at the time and I get that. I mean, for anybody to care at all now would be amazing. But it also, like you could argue it's sort of the 15 minutes of fame is like the foreshadowing, the creator economy or something. If you wanted that.
- [50:48]
Josh Tieringal
I think others, there have been others that have held up worse.
- [50:51]
Jacob Goldstein
If you were running a media company right now, what would your play be?
- [50:58]
Josh Tieringal
I would really retreat to original reporting and high quality presentation because I think what you're really competing against right now is a lot of noise and a lot of slopes and a lot of volume. And when you see that going on, retreat to quality and trust your taste. Because the taste of individuals is still pretty interesting, I think. But I wouldn't try to compete everywhere. I would try to compete on the most interesting stories and make them, you know, keep it simple. Make them as good as you can possibly make them.
- [51:31]
Jacob Goldstein
Thanks, man. Congratulations on the book.
- [51:33]
Josh Tieringal
Thank you so much.
- [51:42]
Jacob Goldstein
Josh Tieringal is the author of the book AI for How Real People Are Using Artificial Intelligence to Fix Things that Matter. Please let us know what you think of the show, what you want to hear more of, what you want to hear less of. Particular guest ideas. You can email us at problemushkin fm. I read all the emails. You can also find me on x or on LinkedIn. Really do appreciate all the messages that we get. Today's show was produced by Gabriel Hunter Chang and Trina Menino it was engineered by Hans Dale. She and edited by Lydia Jean Cott. I'm Jacob Goldstein, and we'll be back next week with another episode of what's yous Problem? I'm not like a coffee connoisseur, but recently I tried coffee from a company called Percy and I loved it. And I'm not just saying that because this is an ad for Perc, though. This is an ad for Perc. I really did think the coffee was delicious. The bag I'm drinking at the moment was grown in Peru. Perc sources coffee from all over the world. They have lots of different kinds of coffee to choose from and they color code their bags. Blue bags are more mild coffee and pink bags are more wild coffee. That Peruvian coffee I'm drinking now is wild, and if you're on the fence, I recommend trying wild. The other morning I had my first sip and I thought of that Will Ferrell line in Old School where he hits the beer bong and then he says, once it hits your lips, it's so good. Find the coffee that matches your vibe and get 15% off your next order with promo code problem@percccoffee.com that's P E R C coffee.com promo code problem.
- [53:29]
Josh Tieringal
Grainger knows when you're a procurement manager for an office park, you're not managing one building, you're managing all of them. And to stay ahead, you need to see through walls and around corners. Lights about to fail, filters ready to clog H Vac on its last leg. If you wait until something breaks, you're already behind. Count on Grainger for quality products, easy reordering and 24.
- [53:51]
Jacob Goldstein
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- [53:52]
Josh Tieringal
Call 1-800-GRAINGER click grainger.com or just stop by Grainger for the ones who get it done.
- [53:59]
Jacob Goldstein
Ryan Reynolds here from Mint Mobile with a message for everyone paying big wireless way too much. Please, for the love of everything good in this world, stop with Mint. You can get premium wireless for just $15 a month. Of course, if you enjoy overpaying. No judgments. But that's weird. Okay, one judgment anyway. Give it a try@mintmobile.com Switch upfront payment
- [54:21]
Josh Tieringal
of $45 for 3 month plan equivalent to $15 per month required intro rate first 3 months only, then full price plan options available, taxes and fees extra. See full terms@mintmobile.com this is an iHeart podcast. Guaranteed human.