Entrepreneurial Thought Leaders (ETL)
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?
00:33:12 · Michael Bernstein, Ravi Belani
[00:33:12] S2: Yes. I think there are a few things to consider. One is I think of the large language models that you would use day to day, whether that's Gemini or Claude or whatever, as having sort of like a gravity well. Right? People go to them automatically, they turn to them by default for everything. Right? So when people try to make, you know, here's my app, here's my design, here's my tool, We have to assume that people have a, like, a default toward the existing thing. They're gonna try to ask Claude or ChatGPT at first. So you have to be so much better that you can sort of hit escape velocity and not get pulled into the gravity well of the language model. [00:33:54] S1: Can you quantify that, or is there a litmus test to know if you're so much better? [00:33:57] S2: Yeah. Do people say wow? Honestly, they're not saying wow, it's not gonna last. [00:34:03] S1: Okay. [00:34:03] S2: I've seen so many things where it's like, it's better, but like we're creatures of habit. And so unless it's like truly deeply like fits into a different category in my head, meh. [00:34:20] S1: And the old heuristic was always if people actually tell other people unprompted about the product, then that's also the heuristic. It's something qualitative, it's not a quantitative I would [00:34:29] S2: say that there's one other bit here which is interesting, which is that I think we're entering an era where because people will turn to their agents by default, suddenly value means not only did you open my app, my web thing, but does agent use my MCP or other?
00:34:29 · Michael Bernstein, Ravi Belani
[00:34:29] S2: say that there's one other bit here which is interesting, which is that I think we're entering an era where because people will turn to their agents by default, suddenly value means not only did you open my app, my web thing, but does agent use my MCP or other? [00:34:48] S1: That's a good heuristic. Yes. [00:34:50] S2: Like is it providing something that is useful? Because that winds up for some folks being like an interesting metric of like usage. Yeah. Right? Are the questions that they're asking their agent, is the agent seeing fit to actually go query us because we are the ones who have the correct answers about that. [00:35:06] S1: That's great. That's a great way to think about it. I want ask one more question, I'm going do the signature questions for the class, which is you faced this classic dilemma when you were starting out about whether or not to build a horizontal platform that can simulate almost anything or focus narrowly on a single use case. Yep. And like you... Like test marketing. I was wondering if you could walk us through that decision because I think that's something that abstracts to a lot of founders on where to start.