Entrepreneurial Thought Leaders (ETL)
00:23:09 · Michael Bernstein
But it does raise the question, like, what's actually a moat these days? I'm curious how many of you have been wondering that recently. Yeah. Now to own my bias, I lead a lot of our... I I lead our data efforts at Simile. So, like, data, I feel pretty good about as a moat. But there are these questions of, like, what will the Frontier Labs sort of capture by means of just their general improvement, racing toward AGI? There's this question of what... Like, what you can do that's durable. Personally, my take is that I think this is a fascinating area that is... That because it requires bespoke data is not something that I think a frontier lab is just gonna automatically get better at. It just doesn't have... The data is not out there nor can it be purchased. But I think if all you do is have an API, then you get into these interesting challenges where there's nothing differentiating. And so I actually think that having the data, the product, these things all mutually reinforcing really becomes critical. Because as I saw, the further our company got and the more successful, the more competitors really start to show up. It feels like every week at this point, like I'm hearing about a new simulation company, and you have to be extremely highly convicted in her approach to be feeling like, you know what? At some point, even if this is so big that the eye of Mordor starts to turn over toward you and pay attention, that you've got what it's going to take to make it not worth it for them to just absorb it along the way. So my answer here, again, my bias, data really matters. Having, you know, identifying modeling techniques that make sense for my task but don't make sense for, you know, math proofs also important, and having novel product does matter. But remember that products can now be built very quickly, so it can't just be the product that you wrapped around an LLM's API. I sometimes refer to us as currently being in what I what I think of as the Myspace era for AI. I've learned that I'm old. I'm 42. At the at the dawn of Web two point o, there was MySpace. Myspace was a social network before social media took off. Myspace was fascinating because it had all of the sort of basic ingredients of what we later like saw with all the social networks that you all use today, but it never really took off.
00:25:46 · Michael Bernstein
It was like it didn't have all those ingredients in the right proportions. At some point, someone figured out the recipe and things went gangbusters. I think the last decade has been a story of massive improvements in the models, and I do think that the improvements in the models will continue. But I also think that if you stare at like the devices and the interactions that you and I use every day, they have really not transformed. We are in the MySpace era of AI. What are we using this to solve? How is it changing our like interactions with each other, with our... Like with information, with society? Like I think we have we have transformed the modeling stack, but we have not transformed the application stack. And my hot take is that I think the next decade, the bet to make is it's gonna change the app stack over the next decade. And I think that that is something that I encourage you to think about. How is it going to change? Why has it not changed yet? Unless you're coding in, you know, Claude code or something like this, Like, most of your experience with technology has not changed, but it's going to. So you better get ahead of it because that's where I think the ideas are gonna be. I also think of simulation as an example of this. It's like, again, a new superpower, a new affordance, a new thing you can do, and starting from that perspective. Okay. My last reflection until you can grill me with whatever you want to hear. Because we're a frontier lab with a product, it has been really interesting and challenging to get everything done. Right? Everything requires us to get modeling, working together with data, working together with product, working together with go to market, engineering, everything else. And it's like you're trying to climb a wall, and if any of those parts are kind of like lagging, you're not gonna you're not gonna climb the wall. In some ways, it's much easier and simpler to just like have a single... Like, there's just one thing that we gotta do. We just gotta, to, you know, hill climb just the modeling or just the product. But if you buy my thesis that you need to actually... For many of the the new transformations, you're gonna need to do all of these things.
00:28:16 · Michael Bernstein, Ravi Belani
Building out, you know, work streams, cross functional teams, like whatever it takes to keep these things in sync and climbing the hill together, that is I think the major interesting challenge in leadership management, whatever you want to call it, for startups these days. Let's say modeling has an idea. It's showing promise. Product doesn't know exactly when it... To expect that it'll work or the cases in which they can assume it will or will not work. How do they design the product around a model that's constantly changing? How does the data team need to work with the modeling to fill in those gaps, and how does product, you know, explain where these things are? Like, how does go to market explain to the customer? Like, they all have to stay in sync, which means you're constantly talking across technical to nontechnical to various different flavors of technical. All sorts of really, really smart people have to stay in all sorts of different kinds of sync. This has been really interesting and challenging, and I think in some ways the fun part of my job because I get to work with people who have so many different types of incredible intelligences, and also it is something that we are constantly working on because if we do not do this right, we will surely fail. And in fact, some of our earliest stumbles involve failures along these lines. So this is always an area for improvement. They told me to keep it brief, and so I've tried. I think we have about twenty minutes for all sorts of open ended conversation. [00:29:59] S1: Yes. That's that's awesome.
00:29:59 · Ravi Belani, Michael Bernstein
[00:29:59] S1: Yes. That's that's awesome. [00:30:01] S2: Everybody give give Michael a [00:30:02] S1: round of applause. So Michael... We're gonna we're gonna do a quick fireside chat with Michael, and then we'll tee up questions for the students. I'll kick it off, and then maybe we can get the student mic set up. Michael, that was awesome. You know, two weeks ago in this class we had Eric Ries, and Eric is the father of the Lean Startup Movement. Yes. And, you know, and he's baked in this practice in the Silicon Valley about closing the laptop, getting out of the building and talking to real human beings when you're doing customer discovery and problem discovery and customer discovery. So much of that, that's become like a mantra in how you build startups. I'm curious for your take. Are we entering into an era where you can do discovery through AI, or do you still believe in the sacredness of talking to live humans? [00:30:55] S2: I think if I were to advocate for, or anyone were to advocate for, like, completely cutting out talking to actual humans, like, I would lose my HCI card. Right? Like, this is a tool, and any tool has strengths and weaknesses. I think the strengths of this is that it gives you sort of instantaneous access to perspectives that might be hard to reach, the ability to iterate, but ultimately, you are trying to build a deep understanding of what the problem really is. And I think at some point, that has to involve talking to to real people.
00:30:55 · Michael Bernstein, Ravi Belani
[00:30:55] S2: I think if I were to advocate for, or anyone were to advocate for, like, completely cutting out talking to actual humans, like, I would lose my HCI card. Right? Like, this is a tool, and any tool has strengths and weaknesses. I think the strengths of this is that it gives you sort of instantaneous access to perspectives that might be hard to reach, the ability to iterate, but ultimately, you are trying to build a deep understanding of what the problem really is. And I think at some point, that has to involve talking to to real people. [00:31:23] S1: I just wanna reinforce that because I think there's a huge temptation just to not do the discovery work and just rely upon the LLMs to give you the insights. But I... But your philosophy is is that that is still sacred. [00:31:35] S2: I... What I see people using Simile to do is that they take what used to be like, oh, we have, like, two ideas, and we have to be conservative about those two ideas, and we have to be certain that they're gonna work so that we can go take them to users and, you know, gather feedback and do and do this. Now I can actually have a 100 ideas and really trial them and iterate on them, and then whittle that down to a much better two that I actually go into the field and see. [00:31:59] S1: Okay. So you can do more of an expansion or a pre pre pre discovery expansion and then...
00:31:59 · Ravi Belani, Michael Bernstein
[00:31:59] S1: Okay. So you can do more of an expansion or a pre pre pre discovery expansion and then... [00:32:05] S2: And as a as a result, it means that people take bigger risks. I think the the the most frustrating thing is if you know you only have like one or two options you can actually field test, you're going to take the sure bet because you don't want to walk back empty handed. But suddenly, now I can take all sorts of risks and see what's going to happen. [00:32:21] S1: Okay, so we're going to lean into taking more risks. And I loved what you said that we're now entering into the era... We're passing model development as the era of focus, and we're moving into applications. And I loved the eye of Mordor comment because I do think there's a real fear that if somebody builds an application, because of the concentration of capital and talent in the big three, in Open AI, Anthropic, and Google, that once the eye of mortar comes to you, your time is limited. And because of how quickly people can build products, how small the competitive differentiation is. So I'm curious if you do think that we're going into the era of applications, and I know you touched upon this a little bit, but can you double click a little bit more on how do you think about a durable moat? How should you in or triage out potential applications to build that will have a moat given the resources of the big three?