00:31:00 · Joon Sung Park
[00:31:00] S1: What our users have today is they have access to a online, let's say we basically serve a SaaS platform to our users. So they get access to this web interface where they can basically come in and define two things. They can say who they want to talk to and what questions they want to ask. And really, they can ask anything. And right now, they can ask any number of questions. There is no limit to the number of questions they can ask and will generate responses. That's what they get today. Now, one of the things that is the most important though is this is a new way of making decisions. We haven't quite seen simulation that is so generalizable in social context that can inform all human decisions. So one of the things that we do and we work very closely with our users is the change management, helping users take these results and then helping them calibrate when to use it, how to use it, where do we need more data, how do we validate it. One of the core functions attached to these reports in relation to what I'm just saying is this idea of motor of confidence or predicted accuracy. So this actually motor is actually quite important to us. Similarly, we'll be generating predictions, simulations of future. Alongside the core foundation model that we are training to make the simulation though, we are jointly training a predictive model of accuracy where its job is one thing. Its job is to output zero to 100 range probability on how confident we are in this particular response on the basis of all the data that we use to train our model and the question that we got. And this is sort of like in inferential statistics. If you're a social scientist, it's a little bit like having p value. And in science, when p value is less than 0.05, that's sort of your canonical way of saying, we're ready to make a decision. We provide that. So if confidence is higher than 90% or usually 85%, our customers have found it to be robust evidence that can actually be used to make very high stake decisions. So it is actually the whole package. So it is about delivering the insight and then also delivering evidence that is undeniable that, well, if this is the evidence, then this is a totally reasonable outcome that we have to prepare for, and then providing this model that basically is giving us a degree of confidence calibration.
00:33:34 · Molly O'Shea, Joon Sung Park
[00:33:34] S0: It's really interesting. One of the questions I had was what is the most expensive mistake similarly helped a customer avoid? [00:33:43] S1: Yeah. [00:33:45] S0: What is it?
00:33:46 · Joon Sung Park
[00:33:46] S1: So, there's actually an interesting study that we actually ran. This technically was before simile, but I still mentioned it because I think as an example, I think this is quite interesting. We worked with a few automobile companies, and one of them, and in fact, of them were interested in electric vehicles. Should we do it? When should when should we do it? How should we do it? That was the top of their mind for every single electric vehicle company that we've signed... We've met. And these were some of the largest players in the field. And one of the simulations that we ran actually had to do with can we predict or can we simulate how people would react to different messaging for new... The entry of new electric vehicle in this particular market, and how can we optimize that messaging so we convey our values to our customers. So there was a strategy that said, okay, this is a clear winner. Fantastic. We're ready to go, and this is good. What was interesting, though, is this actually had downstream implication. This particular messaging, this particular approach towards the market actually shifted people's overall perception of nonelectric vehicle and globally actually lower the sales within that entire market. Geez. And obviously, was run-in studies, and this ended up becoming an input, but this was, again, this wasn't a part of a simile, back then. But it's these kind of findings that I actually find to be quite interesting. These are the findings that are not just narrowly focused on one particular question, but it's more generalizable, where you're looking at the entire field holistically. Simulation allows you to explore that, and when it's at its best, it actually should surface insights that's counterintuitive, that's surprising. So that's really the goal and that's really the value.
00:35:52 · Molly O'Shea
[00:35:52] S0: How good is AI at real professional work? Merkor's AI Productivity Index, or APEX, tracks it with every major Frontier model release. Bill with partners like Harvey and Cognition. See where the Frontier actually stands at merkor.com/apex. Today's episode is sponsored by VCX by Fundrise, the public ticker for private tech, allowing investors of all sizes to invest in venture capital. Learn more at getvcx.com. Public is a multi asset investing platform where you can automate your investing with AI agents. Create your own custom agent with a simple prompt. Agents can monitor markets, execute trading strategies, and manage your cash across stocks, bonds, options, crypto, and more. Want to buy at market close and sell the next days open? Just tell your agent. Automate your investing at public.com. Founders scale faster on deal. Set up payroll for any country in minutes. Hire anyone anywhere, get visas handled fast, and get back to building. Visit deal.com/sorcery. That's deel.com/sorcery. Do you have any dream scenarios or dream simulations that you want to answer or uncover? What's the wildest? You probably have some wild things in your head. What is the wildest kind of thing that you wish someone would want you to solve or you could solve on your own?
00:37:20 · Joon Sung Park
[00:37:20] S1: We're running this company, and we're delivering so much value, but there's a small part of me that still is the researcher part. And as a researcher, I am inspired by questions that are truly societal and generational. Every generation have a few of those questions. One of my research heroes is Thomas Schelling, who was behavior economist back in the 70s and 80s who introduced what we called the model of segregation. And racial segregation was a big topic. That was the generational topic. So he decided to apply simulation to solve that, to at least have an understanding of its causal mechanisms. So I do think about, well, what are those questions that we have today? Well, one of them is climate change. Climate change is hard not just because of the physics of it, but because it's about coordination. There are so many actors with competing incentives who have their own things they're trying to optimize for. When they come together and clash, the equilibrium state they generate is nearly impossible to navigate. So if we can simulate that over and over again and see what kind of outcome we might get depending on what policy, that could be potentially quite powerful. Another one is, well, if we believe AGI is here or if it is imminent, then obviously we as a community, at least in some pockets of this community, have presented a few different ideas. One of them might be UBI. There might be some other ideas. Can we simulate these kind of findings? Instead of running five year study on UBI, could we actually have simulated that and shown that this is where it worked, but this is where it didn't? Those are good simulations to run.
00:39:06 · Molly O'Shea, Joon Sung Park
[00:39:06] S0: Have you simulated who you should be taking money from? I know you just raised a huge round. Well... You must have, right? [00:39:15] S1: In fact, what I often do is I do simulate these kinds of conversations. So like obviously, know, Molly, you have a very big online presence, so of course you bet I simulated the conversations before walking in. [00:39:30] S0: Is this one of them? [00:39:31] S1: Okay, here's It the funny did warn me that you might ask whether we live in a simulation. [00:39:39] S0: Of course. [00:39:41] S1: So I should have taken that advice more seriously. [00:39:44] S0: Pull up your results. Let's go through this right now. What else did it simulate? [00:39:48] S1: That was a big one. And thought it was... It's not something... And this is interesting, right? Because that's not something that every interviewer's asked. But for some reason, this is actually a good question that the simulator actually thought, given everything that we know about you that you might ask. [00:40:06] S0: Well, I do tweet Elon a lot, so that is part of it. I am curious, though, you have raised 300,000,000 in total at a valuation of 2,000,000,000 reported, and you have really great names like I mentioned Green Oaks, Index, you also have some really cool individuals like Fei Fei Li and Andre Karpathy. How did you collect such an interesting base of investors and angels? Right.