Michelle Lee explains first-principles thinking as starting with a core goal, then working backwards to decide what to build next. She emphasizes talking directly to scientists and biotech founders to find common pain points across the market, rather than over-engineering for a single client's needs.
On this idea of first principles, is it the scientific method? Is it basically just articulating some basic premises of inputs that you think are driving an output and then testing that or, or can. Or is there. I don't know if there is an example you can. Is like with. Are you dealing with that right now? With hiring, for example, is that coming up with issues on the team or is there an. A tangible example we can show, we can walk through on how you apply first principles.
Okay, so let's take the example of what should my team focus on right now? In the beginning when we started working on these, we had a customer. So they told us exactly what we should be working on. Once we finish that customer, then it's what's next, right? Should we get another customer? Should we be doing R and D? Should we be building A, B or C? And I think that first principle of thinking is really thinking first about like, what is that goal? What is the goal you're trying to achieve here and really be able to work backwards from there. So our goal is to create a product that scales and a product that someone that customers truly want that really solves their problems. Right? That's really our main goal is like we want. If we, I believe if we can solve customers problems, then we're building something with value. Well, then the question is how do we actually figure out what problems to be solving? Let's say we just signed another customer and we built for that customer. The question is like, maybe that customer just had a very specific need that is not what every other customer wants. Right. So then that, that wouldn't be helpful. So if we really wanted to understand what's going to solve problems for a whole market and a whole field, we have to go talk to people. And so I realized that actually instead of spending my time doing more engineering work and you know, trying to, to, to maybe build for new customers or like building like what we think is. Would be really cool. I just need to go out there and talk with scientists, talk with biologists, talk with founders of biotech companies and really understand like what, what do you actually need and can we help you with that? And what should. What should. Like if we. If. And maybe figuring out like, are there clusters of things where we see there's like a huge need and so we can really focus our time and energy on that. Another example here would be. Oh, or Ravi, I don't, I don't want to.
No, keep going. These are great. This is great. It's like prompting all these other things. These are great. So keep going.
Yeah. So another example is we now have customers that we deployed our systems, and with every new product, you're. It's the first time it's mvp, right? The minimum viable product, the first time people ever used it. And so there's going to be problems with it, right? And so we're now, like, supporting our customers and trying to fix all the problems. And a question comes up of, like, what should we be focusing on? Should we again try to get another customer? Should we try to make it easier to have new customers, or should we try to really support our current customers? And one tool that you can use when you're doing first personal thinking is to really think about things taken at extreme, right? Like let's say we focused on. On making it really fast to scale our product right now. Like, so every new customer, maybe it takes like, you know, two weeks to be able to set up a new robot for them. Well, if you take it to the extreme, right, and say, let's now deploy hundreds of robots, like every two weeks, we're going to do 100 robots. But support actually takes us, let's say, 10 engineers, right? I'm just exaggerating here. Then our company absolutely cannot scale because just because it's fast, we can have hundreds of robots out there. But now for every Robot, we need 10 engineers to be able to support, right? So taking that extreme, it was very clear to us that as a team, we actually needed to focus on support now and build out all that infrastructure for support to be able to help us scale our product.
