Could AI Transform How Science Gets Done? –with Jungwon Byun

Elicit co-founder Jungwon Byun discussed on the Dream Machines podcast how her company builds AI tools for scientific research, emphasizing reliable citations and evidence to counter hallucinations. Byun highlighted Elicit's chatbot, which links responses to published papers and clinical trials, and explored AI's potential to help scientists decide what to investigate next, possibly leading to discoveries in areas like disease cures and global energy.

Could AI Transform How Science Gets Done? –with Jungwon Byun What happens when AI moves beyond answering questions and starts helping scientists decide what to investigate next? Dream Machines hosts Alexis Madrigal and Robin Sloan talk with Elicit https://elicit.com/ co-founder Jungwon Byun about building AI tools for scientific research, why reliable citations and evidence matter as hallucinations become harder to spot, and whether connecting vast amounts of research could eventually allow AI to make discoveries that humans might miss, like curing disease or solving global energy issues. They also discuss what it feels like to build an AI company in the Bay Area right now, and we’ll hear about Jungwon’s “oh sh ” AI moment. Guest : Jungwon Byun, co-founder of Elicit Episode transcript This is a computer-generated transcript. While our team has reviewed it, there may be errors. Alexis: Hey, I’m Alexis Madrigal. Robin: And I’m Robin Sloan. Alexis: And this is Dream Machines. It is a podcast about how AI works, also how it makes us feel, and it is rooted here in San Francisco, of course. Robin: Uh, today’s episode, we are gonna talk about the people who are doing this work, uh, in the streets of San Francisco, which can be a sort of surprisingly elusive subject because so many of them are essentially locked up inside these two or three big AI titans, you know, OpenAI, Anthropic, and, you know- And even if Alexis: you know them- Yeah they’re, like, not emailing you back or anything. Robin: Yeah, yeah. You, you haven’t heard from them in three years, and they can’t possibly talk. They’re on Alexis: the rocket ship to a trillion dollars. Robin: That’s right. Yeah, yeah. So more power to them, but, um, for those of us who are interested in, you know, the industry and how it’s changing and sort of enlivening the 00:01:00 city and the whole area around us, uh, we gotta find somewhere else to look. And the good news is, of course, it is more than just two or three giant companies. There’s actually hundreds, uh, maybe thousands of these little, uh, AI startups. So Alexis: who are we gonna talk to as our sort of avatar of the new generation of AI folks? Robin: The company is called Elicit, and I have to say, you know, the whole team there was, um, just a sort of delightful group to talk to because they are so energized by what they’re doing and excited to be part of kind of this AI movement, and- And Alexis: what do they do? Robin: Yeah. Their basic approach is, uh, AI for scientists. Um, and the re- you can tell because when you log into the application and start using it, it assumes you have, like, a research program. It’s quite serious in that way. Yeah. Um, and the offering is com- some sort of, um, you know- Alexis: But you chat with it still, Robin: right? Yeah, it’s still… Yeah. Yeah. It’s a chatbot, and you kinda can keep notes and feed it documents and have it analyze things, but it’s all in this framework of, like, you have a research project. Maybe you’re trying to figure out a new hypothesis for your lab. Maybe you’re trying to 00:02:00 map out a field that you’re not totally familiar with. Um, and they back it up, um, with some really, really rigorous, uh, essentially footnoting. You know, everything you hear back from this particular chatbot is linked to, like, a published research paper, a clinical trial- Mm … like real data somewhere. The, um, co-founder of Elicit is named Jungwon Byun, and Jungwon in particular, uh, I found quite incandescent. Um, she articulates their mission and its value really well, and, uh, she’s got a cool story about her own kind of, you know, entree into the AI world and the San Francisco- Yeah … Bay Area startup scene. So I thought it’d be fun to invite her to cross the bay, um, from their office in uptown Oakland and join us here at the studio in KQED. Let’s do it. Alexis: Jungwon, welcome to Dream Machines. Thank you. Robin: So to kind of, I don’t know, set the stage here and, you know, figure out how the players came to the stage, uh, we thought we’d start by asking you your San Francisco 00:03:00 Bay Area origin story. Yeah. You know, how did you, how did you, uh, end up in the dreary backwater of, uh-AI and tech in San Francisco? Jungwon: Yeah, reluctantly. So I moved here in 2015. I was living in New York at the time, and I really didn’t want to move. Uh, but there was a job opportunity here. I, I worked at a company called Upstart. Um, and back then you had to move for your job. So I did that- Right … even though I didn’t wanna come. Um, and um, it was actually ca- a pretty big adjustment for me ’cause I, I felt like in New York I had just g- found my community and I had just found my l- you know, my life and, um, to just move for a job, um, and, and San Francisco’s really different from New York. A lot of people really struggle with that transition. So f- it was difficult for me too. Um, but I came here, and in many ways I think, um My experience of those two cities continues to kind of reflect that decision, and maybe what a lot of people experience, which is San Francisco is v- very work-focused. So here it’s, like, the most incredible 00:04:00 place I could be to do the work that I want to do, but any time I leave, I feel like I’m a different person. And when I go to New York, I, like, immediately go back into that young 20-year-old person who went to- … like poetry slams and, you know, ran through Times Square in the middle of the night. And so that’s something that I, I think I still wrestle with here. Yeah. Robin: Do you feel like it was a definite thing that you were gonna find your way into AI and/or science? Was that kinda just by chance? What was that, what was that connection? Jungwon: AI, definitely. So even when I was in New York, actually, I was… I s- I had some personal experiences that made me start thinking about, oh, how could AI really help people navigate some of the hardest questions they wrestle with? So I had, I had friends in my life that were struggling with mental health, and I was really surprised that there were basically, like, no resources available to them. Um, I, yeah, one, one person, one friend was having a really hard time, and so I was trying to call up, like, different support lines and, uh, you know, re- mel- uh, mental health kind of call centers, and, like, literally one of them, it felt like a guy picked up, like, having just woken up from his nap. And I was like, wow, 00:05:00 this, I live in, like, the, one of the greatest cities in the new, in, in the world, and, um, there was just, like, no s- no support for them. And so I started thinking, like, how… Is there a world where AI could help people navigate, like, incredibly overwhelming thoughts and stress? And so I just played with that idea for a while. And at the time, we had started this research lab called Ought, and our mission was to figure out how to help, use AI to help people figure out what they ought to do. Um, it was a very… Everyone working in AI at the time was very weird. Um, it was like, it was like the East Bay, East Bay weird, right? Uh, but we were in North Beach, and we were working out of this kind of, I think, like, historic building that was definitely not zoned to be an office space run by s- a very, very, you know, elderly family. Um, and it was, it was late, and, you know, the sun had set, and my co-founder and I had just gotten research access to this model called TNLG from Microsoft. Uh, GPT-2 had already come out, and my co-founder was, like, obsessively playing with it all the time, and I was like, “Why are you always playing with that thing?” TNLG actually had a style that sounded more human. We could have more 00:06:00 conversations with it. And I think that was the first time I re- y- I realized, oh, this is something that’s going to happen in my lifetime And before all the crazy AI p- pilled people thought, you know, “2050, let’s prepare for our future generation.” But that was the moment that I was like, “Something has qualitatively changed.” And so I remember walking outside of our office, walking past Washington Square Park, and I’m, and I’m on my way to the BART to commute home, um, and everyone is out in North Beach, like, eating dinner. It’s like- Mm … all the lights are on, it’s glowing. People are so ha- have, you know, having a wonderful time, and I’m like, “You people have no idea.” You don’t know what’s coming. Yeah. Such a good- Yeah … Robin: I- I… What’s amazing is I feel like that, that’s a fabulous scene, fabulous feeling, and I feel like it has now been repeated. You are, you are pretty early to that feeling. Yeah. And now, like, what? Tens of thousands, maybe low hundreds of thousands of people have had that experience- Yes … walking out into the San Francisco twilight- Yeah … saying, “Oh, nobody, nobody knows.” Wait, what was Alexis: it in that early model, you think, that, that gave you that feeling? Jungwon: I think it was the fir- so GPT-2 was barely coherent. Like, it could put 00:07:00 words together, but it just, like, didn’t make any sense. I think that model could kind of interact in a little bit more. Like, we could do a couple more turns together. Alexis: Like, it’s passing the Turing test. Jungwon: A little bit. Yeah, yeah. Robin: I love, I love the, um, the way model culture… ‘Cause of course, again, now it’s, like, big business and it’s, like, you know, consulting companies are talking about them. I love the ways in which it can also just be, like, straight up culture. You know, you’re basically talking about, like, a deep cut model. Yeah, yeah. You’re like, “Well, most people are into the Ramones, but actually- Yeah, yeah … um, there’s another band.” Uh, Jungwon: yeah, exactly. Robin: Yeah. Jungwon: You would’ve never heard of them. You would… Robin: Yeah, that’s what it is. Don’t worry about it. Yeah. Don’t worry about it. Yeah. Wow. So you had this magical, maybe slightly scary moment- Yeah … walking the streets of North Beach. Um, and then fast-forward to Elicit. So, uh, as I see it, um, what you and your team at Elicit have built is a platform, m- mostly a, a web application Um, it’s quite serious actually, its application. You log in and, and it kinda assumes that you’re a scientist, a researcher, you know, with some serious goals. 00:08:00 It is for doing, uh, reviews of the literature, maybe for finding holes a- and, you know, interesting open questions in existing research. And one of the primary offerings there is that it will, you know, answer your questions or go off and do a big research job, but then everything it tells you is pinned back to a real piece of research somewhere. And this is not just material from the open web. This is not- Mm … you know, citation, uh, Alexis: this- This guy on Reddit … Robin: ZergNet forum, you know- … page 14. It’s, uh, you know, uh, clinical studies. Mm-hmm. It’s research papers. Mm-hmm. Et cetera. Jungwon: Yeah, that’s right. Robin: How does that sound to you? Jungwon: Yeah, that’s a very accurate description of where the product is at today and, like, a big part of how we got started, it was, um, we always built for researchers. And for us, it just seemed very obvious that everything would have to be cited because we were like, “Well, how will we know if what we’re p- putting out there is correct or not, and how will we check if any of this is li- is hallucinated or accurate?” And so we needed to check for ourselves, so we built those citations to make it easy for us to check, and obviously it’s the same thing researchers needed to check. And it’s kind of crazy for 00:09:00 all, every, all of the progress that we’ve made that this is still a problem. Yeah. Like, hallucination is still a problem. And just, like, the number of times I work with Claude on something, and then I’m like, “Okay, where did you…” Y- it’s giving me really detailed information and numbers, and I’m like, “Where did you get that information?” He’s like, “You’re right. I didn’t get it from anywhere.” And I was like, “Oh, yeah,” that, you know, it’s kind of trust breaking and it’s, it’s surprising that it still doesn’t do that. But I think the longer term vision is, like, you know, how do we… For us, um, the, the evidence base and the research was always a fundamental primitive to informing really important decisions. We’ve always been motivated by very high stakes decisions, and kind of being on this journey through the pandemic I think really made that even clearer. Um, and so how do we help- Really important policy decisions, strategic deci- decisions to be more evidence-based. That’s kind of the first, yeah, step. Alexis: Maybe you could just walk us through an example of, like, a specific kind of contested terrain or d- or something in science that people are trying to use these systems to make decisions about. Mm-hmm. Like, where to put research dollars and 00:10:00 X or Y. Jungwon: Yeah, yeah. So one of our customers is at a, um, a large pharmaceutical company, one of the largest pharmaceutical companies, and they are an R&D director. And so they manage a team of 40 different scientists. So they have to think about what science is worth doing. Like, how do we s- what- how should we spend our time? How should we spend our resources? And then they have individual scientists to actually figure out the execution of that. Um, so they worked with Elicit to map, uh, about 16,000 different drugs in oncology to understand where’s there a lot of concentration, where is there… where have things been really well-validated, what are some opportunities for me, how do I make trade-offs between, uh, biology that’s well-understood, but it, you know, it’s a space where there are a lot of people, you know, are then, then have drugs or, or things like that- Mm … versus something that’s more novel but is a bit more risky. So I think it’s, like, those kinds of questions of, like- Mm-hmm … what should we do? Mm. At higher level, how do we trade these things off? There’s not exactly a right answer, right, that Elicit is, is really aim- at. Alexis: What if I just wanna know which peptide to inject into myself? Jungwon: You can do that, too. Yeah. You can map all of the peptides, actually. Yeah. Yeah. 00:11:00 Yeah. Robin: And Alexis: do, do you, do you feel like you have some responsibility as Elicit to be like, “You might not wanna try that one”? Like, do you know what I mean? Yeah. Like, how do you… when, when you know people might use it for this sort of doing your own research kind of, uh, kind of a mode of medical thinking now, which I myself sort of do have, I suppose, at this point, um- How, how do you, like, keep people safe, or at least not encourage them to do things that are stupid? Jungwon: Yeah. One of the big problems I think we see with language models is this idea of sycophancy, which is they basically just tell you that whatever you think, it’s great. And so as much as possible, we try to avoid that, and we have specific evaluations for trying to see, like, how easy it’s a model to push around. Alexis: It’s really interesting because, you know, when you talk to scientists or, you know, in my case, tons of science journalists over time, you know, they have all these different, like, heuristics for evaluating, like, the quality of data- Mm-hmm … that’s in these research papers. Yeah. ‘Cause even in the research literature, there’s this huge- Yes variability within. So how do you, how do you make those things something that the AI will 00:12:00 pick up? Like, how do you figure out what is really good data, what’s less valuable data, what’s comparable, what’s not comparable? Jungwon: Yeah. A lot of it, a lot of what we think has to happen here is the AI assists with the human evaluation of that because it is re- it really varies by domain. You know, some domains you’re gonna have huge randomized control trials, and it would be very weird if there was a study that only looked at two people. In another domain with rare diseases, like, that’s all you can do. Right. Right? Mm-hmm. Yeah. So, um, so a lot of what we try to do is we have the AI systems do, like, a best guess, and we specifically try to look at the content of the studies and actually look at what was the methodology, what did they control for, what were the statistical techniques, and then we take a guess, and then the researcher c- can override that, right? And they can still say, “Based on my experience, I, I weight these criteria more or less.” Alexis: I, I know some scientists who are quite skeptical of AI for various reasons. Mm-hmm. Um, does this… You think this is sort of the kind of harness that feels comfortable for them? Like, “Oh, now I can, like, let myself- Dive into this? Jungwon: Yeah, I think so because the, 00:13:00 like the, the citation verification is really important for them really seeing the citations and just having that be there by default. Um, because otherwise if they, if the scientists feel like they have to check everything, then it doesn’t save them much time. Um, and now what we see is the, um, the hallucinations get more subtle, right? And that’s kind of the risk we’ve always seen with these models. Before it was like, “Oh, you were wrong. You were clearly wrong. This paper never existed. You could just Google it, and you would know that the paper would not exist.” Now I- now the base models kind of tell y- you know, they might link you to a particular paper, but you’d have to read the whole thing to realize the information was never there, and it gets more expensive to check. Um, so we try to make that really easy. So I think that just having that confidence that it, there, it’s always gonna be, the claim is always gonna be grounded by the ground truth. Alexis: Do you guys kinda miss some of the old hallucinations? You know, when these models used- … to just make stuff up- Yeah … that was like- Robin: Well, it was, it was clearly psychedelic. Yeah. Alexis: Yeah, yeah, yeah. Or even like when they would sort of like imagine a book, like in between books, and you’re like, “Actually, that book should exist.” Yeah, yeah, yeah. Right, right, right. And so you went there, but now it’s not there anymore. That actually does, 00:14:00 it kinda breaks my heart- Yeah … that now they’re like so subtle that you wouldn’t, generally speaking, pick up on them, and they’re no fun anymore. Yeah, Robin: yeah. It’s, it is, and isn’t that so funny? It’s, it’s quite profound to, to sort of reckon with the fact that the most dangerous hallucination of all is one that like correctly identifies the paper, the author, the subject, but then changes like one digit- Mm-hmm Alexis: Yeah Robin: Mm … in a very, very- Yeah … important number. Exactly. I mean, that’s, it’s, that’s wild. Yeah. It’s really wild to think about. One of the things I appreciate about the platform is that it is so specific, um, beginning with the fact that it’s, you open it up and you kinda go, “I think I might not be the kind of person who’s supposed to be using this app.” Yeah. Which is really cool. Yeah. That’s so different from the sort of, as you say, sycophantic, always inviting- Yeah alluring, “Morning Robin,” you know, “What’s up?” Yeah. Yeah, yeah. Of the other- “What Alexis: can I help you with Robin: today?” Yeah. Yeah. Of the other- “What do you Alexis: wanna build?” Robin: Of the other chatbots. Yeah. Yeah, exactly. I, um, am a little jealous honestly of the position of kind of interfacing with so many scientists and so many labs all at once. Mm-hmm. 00:15:00 Um, just for the viewpoint, that kinda like vantage point of- Yeah … you know, science in, in the 21st century. Mm-hmm. Um, going beyond just the, the offering, the specific offering and kind of the research tool on the front end that, that Elicit provides, um, what are you seeing? What do you think, what do you think scientists need in 2026? Jungwon: Mm. What do they need? I think in general with science, it’s just, like, so easy to rabbit hole, that having a really good overview of, like, the whole landscape and how my work fits into all of the other work is something that more scientists would benefit from, without having to then specialize in mapping out a domain. So that’s how I use Elicit a lot. I’m like, “ALS, what, what is going on here? What are all the different treatments? Why do they exist? What are the things we’ve figured out, w- we haven’t figured out? Why haven’t we figured it out yet? Can we make a leap from, you know, over here all the way to over there?” Multi- multiple hops of inference. And so I think, you know, a lot of people… One of the questions people have about AI is like, “Oh, can you really automate ingenuity or creativity or insight?” And I guess one of my controversial beliefs is that, uh, 00:16:00 that’s actually just really powerful search, and humans are able to kind of make multiple leaps of inference, maybe without even realizing how they do it, in a more intuitive way. Um, and so one way we might be able to replicate that is if we actually just built out all of those relationships. Robin: So the, basically it’ll be, um, the 21st century, uh, equivalent of Google’s iconic I’m Feeling Lucky button. Jungwon: Yeah. Robin: It’ll just be, it’ll be like the genre buster button. Yeah, yeah. Yeah. Allow you to listen, you’re like, “Let’s do it.” Alexis: Yeah. Jungwon: Yeah. Yes, exactly. Robin: Actually, that would be great. I mean, I mean- Actually, I would say, yeah. Alexis: Yeah. Yeah. I mean, I, I think when we think about AI in science, too, there is this promise that is being made by the AI industry- Robin: Right, the, the promise that kind of, is kind of what underpins, you know, any number of, um, or, or justifies or allows any number of- Alexis: It’s gonna cure cancer. Robin: Mm. Yeah, data center. Yes, like, don’t worry about the data centers- Mm … or the electricity. Yeah. You know what’s weird? Or the, or the job stress and, you know, your, your email suddenly is all weird and full of little glittery AI sparks. Mm. Don’t worry about it, because, dot, dot, dot, dot, 00:17:00 dot, super AI science, um, will give us all these great things. Mm-hmm. Um, I guess that maybe the start- Well, Alexis: first one, do you think that’s gonna happen? Robin: Yeah, yeah, what do you think? Jungwon: Yeah, I think so. Robin: You Jungwon: do? There are still major bottlenecks in the process that are much harder to reduce. Like, if you’re going to measure overall survival in a cancer patient, you just have to wait 10 years, right? Mm. So that’s not a thing that you can accelerate with AI. But I think there’s a lot, like it’s, there’s a lot around that process, even getting to the clinical trials, everything that happens after clinical trials, where there’s just so much work that, uh, can be automated and accelerated, that people want, don’t, don’t want to be doing manually, that I think we can shave a lot of time off. Alexis: But what about, like, the thing that’s being sold, which is essentially self-improving science- Yeah … via more or less autonomous- Right, I guess- … AI agents. Right. Yeah. What I’m hearing, you’re, you saying is, like, we can deal with this balance of system cost piece. Jungwon: Mm-hmm. Alexis: But I think what’s being sold is like, “No, we’re gonna make a solar cell that has 60% efficiency, and we’re gonna, like, solve energy forever.” Jungwon: 00:18:00 Yeah. Yeah. Yeah. I think that one, um, I guess- It’s, I think I’m, I’m AGI pilled enough to believe that, yeah Robin: Yes, Jungwon: yeah. Robin: So it’s g- Yeah, Jungwon: yeah, yeah … it’s just a matter of time. Robin: Yeah, yeah, yeah. And so, and so- Yeah … sort of a vision where it’s, yeah, right, it’s not a mere human, uh, you know, oh, a sad, pathetic little Nobel Prize winner reading, uh, the report from Elicit. It’s another agent saying, “Yeah, you know, I, uh, me and my, uh, million buddies in the data center, uh, looked across the discipline, identified some holes- Yeah … and then spun up, uh, experiments and some-” scary, dark, wet lab connected to the internet somewhere, and, uh, interesting, interesting work came out. Yeah. Is, I mean, something like that, right? Jungwon: Yeah, and I, I think, I think it’s still, it will still take a lot of time to build all the pieces together- Mm-hmm … just because success- making a successful drug and validating it is so complicated. Yeah. So it’s not, it’s not like, oh, I f- I write a program and then it runs 10,000 times and now I have a successful drug. So I think it could still take us, you know, quite a while to put it all together, but I think that is something that we can do. It’s tractable. Robin: 00:19:00 I’m, I’m still very conscious of the sort of friction of the physical world. Yeah. I mean, it’s telling that the, the huge gains, I mean, the really just incredible, um, sort of leaps forward have been in realms like math- Jungwon: Mm-hmm Robin: code, obviously. Now, having said that, there are some new, like, robot hands they’re making down- … on the peninsula that are, like, daintily cracking eggs. Mm-hmm. And, and so e- even that, even that I feel a twinge of maybe not. But, um, but, but truly, I mean, as someone who’s been thinking about this for a long time, and, um, and cognizant of the, I mean, just the, the surp- surprise after surprise, um, I still think that the, the grit and kinda friction and, and everything, slipperiness and unpredictability of the physical world is a, still a bit of a firewall- Yeah for this kind of stuff. Jungwon: Yeah. But I guess I just feel like we’re not gonna stop until we try and… Like, that’s, you know, we’re never gonna stop trying science. We’re never gonna try to make it better. We’re not gonna, we’re never gonna stop curing these diseases. Yeah. So at some point we will get there. Robin: We, we skipped over that, and I guess- Yeah in the truth, I forgot about it. Elicit is a term of art, actually- Mm-hmm … in the AI engineering and kind of product world. Can you explain, what does it mean to 00:20:00 elicit a model’s capabilities? Jungwon: Um, basically, it’s like, you know, the mo- model has kind of this raw power, but you have to kind of know how to ask it to do certain things, or how to get it to actually do that, or get it to do that in a, in a reliable way or a helpful way. Um, so that’s kind of one meaning of elicitation. But the other we think about a lot is the elicitation from the person. One of the hardest things, I think now, and increasingly as we have this capability that can do anything, is kind of getting it to do, making sure it knows what to do or what it’s supposed to do. Like, whatever, whatever it, it, it understands its job to do, it will, it will get it done, but it’s hard to know how to tell you as a person to tell you- Uh-huh … tell it, like, what good looks like or what you’re trying to achieve, right? Um, so that’s another frame in which we think of Alexis: elicitation. Okay. Yeah. El- uh, elicitation for you is on both sides. Jungwon: Yeah, exactly. Alexis: That’s it. Jungwon: Yeah. Yeah. Eliciting knowledge and capability from the model as well as goals from the, from the person. Alexis: I mean, part of what I understand elicit to be trying to do, too, is to, to make the thinking that these machines are doing consistent across different experiences- That’s Jungwon: right. Alexis: Mm-hmm … Jungwon: 00:21:00 too, Alexis: right? Which it strikes me as, like, a, a really, uh… Ev- every time I’m playing with these models, I feel like they’re unstable in their approach- Jungwon: Mm-hmm … Alexis: to problem solving. Mm-hmm. And sometimes that’s just ’cause I’ve given a slightly different prompt. Like, like, I prompt this way and it makes it like this. Yeah. Prompt that way, it makes it like that. And there’s probably good reasons for that to happen in, in like my whatever, like I’d like to know all the Bay Area books that are coming out in the next quarter kind of task. But if you’re testing drugs- Yeah … if you’re doing these serious decisions, you kinda want it to Be structured in how you think That’s right. Jungwon: Yeah, ’cause that, that’s the only way you can then go back and say, “Okay, well what about our approach was right or wrong? Do we now wanna c- like correct?” And if, if you wanna kinda do that meta-reasoning, you wanna have pretty well documented what you did and why. Um, you also, a- you know, certainly within f- the pharmaceutical industry you’ll have auditors or regulators come back and, like in a really detailed way, be like, “How did you arrive at this?” Right. And that could be months or years from when it happened, and you need to be able to defend that. Um, so the 00:22:00 reproducibility matters a lot. Yeah. Robin: Well, well sir, uh, it does appear that I added several playful emojis- Yeah. … to my, to my initial query- Yeah … which led to, um, unintentionally, uh- … playful results. Yeah. Yeah. Um, Zheng Wen, I wanna move to just a little bit of speculation about… Or, or just ask you, what are some of your, what are, what are your fears right now? Jungwon: Yeah. Robin: What do you think about? Jungwon: I generally feel like we are telling people to be anxious, and they need to be worried, but we are not telling people what they can do about it. And I feel similarly. I wish I… I also feel like this is big. L- we need to take it seriously, but then I can’t give people a way of like, “And this is what you should do about it.” Be Alexis: like slapping their pasta out of their hand- Jungwon: Yeah … and Alexis: be like, “This is what you need to do.” Jungwon: Do something. Yeah. Anyone, anything. Yeah. Yeah. Um, and I, I, I definitely worry a lot. I think I worry a lot about like large scale social change, and I worry a lot about job loss or displacement. I’m hopeful about ways I can look good, but I think, I just feel like it’s, 00:23:00 change is just going to be big and scary. Um, and I still feel like we don’t have a good answer to what happens if things get very consolidated and automated. Robin: I feel like the, uh, best guides here are of course, uh, speaking on behalf of the science fiction writers. Um, unfortunately, the tendency, uh, which is driven by narrative and aesthetic, uh, purposes is, uh, to result in, uh, dystopia rather than, rather than e- utopia or even boring-topia. Mm-hmm. Yeah. You know, of like, “Oh yeah, and they muddled, they muddled through- Yeah um, by figuring out- Yeah. That’s right … some, some practical new policies.” Yeah. Yes. Great, yeah. Uh, those, that, those apparently don’t get written very often. Yeah. But, but, uh, I mean sincerely, it is, it’s a time for, for imagination and, uh- I think so … there, there, there needs to be more of it. Jungwon: Yeah, I agree. Robin: I’m sure you know people in your life, your family, you know, friends who are anxious, um, over just thinking about the next 5, 10 years. What do you tell them? What should they, what should they be looking forward to or thinking about? Jungwon: Um, yeah, so I think one cause for optimism is there are truly so many problems in this world still, and it 00:24:00 would be really great to solve them. Like it really, um, you know, it just, people who are, who have rare diseases and, uh, limited prognoses, like it, you know, we obviously we wanna do everything we can to cure them and use whatever technology we have at our disposal. Um, and so I think that is, that is cause for optimism. And I, I wonder how often dystopia versus utopia is just a matter of tone. And like to what extent could you not describe our current, I mean you could describe our current reality as a dystopia. Robin: Absolutely. Jungwon: There are plenty of people who are happy in our current reality. So one optimistic case is from where we’re standing today looking at the future as outsiders it seems dystopian. But, but for whatever reason the people living in it are still happy and they’re able to get by. Robin: Yeah, yeah, yeah. Jungwon: Even if it looks so foreign to us. Um, and then, and then I think I do believe that like I, I think humans just have this incredible ability to To overcome and to be ingenious, and maybe the problems that we’re currently wrestling with get 00:25:00 solved, but we continue to exist on higher levels of abstraction. I guess that’s the dream, right? Mm-hmm. So solving even more ambitious problems. Can we, can we with, you know, technology that helps us think rigorously about science and experimentation and, um, facts, spend more of our time thinking about what institutions ought to look like, right? What kind of society do we want to create? How should we deploy these powerful technology? If we can do anything we want to, what should we be doing? I think a lot of those questions are still unanswered. Robin: Mm-hmm. Mm-hmm. Yeah. You talk about, Zheng Wen, you talk about people, you know, finding ways to live in our present dystopia, utopia- … whatever it is. As we always do. Hey, it’s Alexis: Oakland. Robin: Yeah. Yeah. And, and it is, I, well, again, you know, almost if you just ignored all the specifics of what Alyssa does and just, you know, described you and it as a… You’re a co-founder of an AI startup in the San Francisco Bay Area at this moment. That is a, that’s a wild thing. Mm-hmm. Um, I mean, even more so than, than it, it was a few years ago. Uh, so first and foremost, how does it feel? Mm-hmm. Like, what is your, what is your nor- what is your baseline emotional 00:26:00 state, uh, as a company leader? Mm-hmm. Is it like, uh, excitement to wake up every morning? Is it dread at all times? Hmm. Um, is it a sense of competition? Yeah. Jungwon: Something else? I think in many ways that psychology probably was similar to just, you know, the founder’s psychology has always been the founder’s psychology, which is, like, incredible highs, incredible lows, like, every two seconds, you know? Like, macro optimists and micro pessimists, all that. It’s j- it is really about holding a lot of tension. Speaker 5: Yeah. Jungwon: Um, and, and maybe AI has accelerated that because the pace at which things are moving has, has accelerated, so it often feels like I both need to really understand, uh, what are my core convictions and where am I, where, what are the fou- what’s the foundation that’s stable, and also be willing to let go of everything at all times instantly, like anything I ever believed about the world, and just be really be willing to, like- dynamically change that. Um, so that’s, that’s hard. But yeah, holding that tension is, it’s probably a big part of being a founder. Robin: Oh, oh, being, uh, uh, willing to shed your skin- Yeah … your, 00:27:00 your psychological skin like a snake. Yeah. Uh, “Oh, is that all?” Jungwon: Yes. Robin: Multiple times a week- Exactly … and/or a day. Jungwon: While still- Yeah having your identity and some skin, you know? Yeah, yeah, yeah. Yeah.