The Exchanges

Q You have signing. Why do you need 3000 engineers?

A It sounds crazy, but like you want to nail Europe. You need a different product. You need a different team to your local data centers because of the compliance. You cannot just run your data centers from the U.S. So you need a local team there. Oh, and by the way, the way to do digital signature in Europe, totally different. So like the stack itself is different. So like the way to make a digital signature is different. Not the same standards in the same ways. So you need dedicated team to maintain that thing. The same way some people want to have DocuSign on-prem. So you need a team building appliance to basically plug and play and like, okay, you have your DocuSign appliance.

AI assessment note: “You need a different product. You need a different team”

Q How did you design the tools to get to the agent? Just maybe give people an overview of, like, the framework, what it looks like. Like, how are you structuring these interactions? Is there just one superhuman agent that does everything, or like, do you have separate ones?

A We have separated tools, uh, clearly. So, uh, even an agent, like, I would call about, I would say tools. So there's a bunch of tools. Tools to detect your availability. Tools to understand who are the people you interact with. A tool to write an email. The tool to, like, so every single action is very tool specific, so it's not a magic B tool that can do pretty much everything. It's a set of small tools that are used, uh, within the Argentiq framework. So, like, there's a first step that is like, hey, what is the best tool to do this? Kind of like building a plan, like, for each step, what is the tool, and then making the calls.

AI assessment note: “every single action is very tool specific, so it's not a magic B tool”

Q What would you prefer? I'm curious, like, would you rather have the user just take care of the inference and rely on that, or do you want to own the whole experience?

A I mean, Superhuman will want to own the full experience. Like, we're pretty picky in the way things are, I would say, happening. Um, so, but at the same time, like, if we talk about mobile, you want the mobile experience feel like your device. So we are basically not doing React Native, we are doing Swift, we are doing Kotlin, because we want the app to feel Like the, uh, the, the user experience in generally in the, you know, on iOS or on Android. So, but for the models, um, that's a good question. Uh, I would love the device provider to be better. Right. I mean, we can question like, uh, local devices, uh, local, uh, like iOS has done some work there, but it was underwhelming so far. Uh, they're still working at it. And that's why we have like a YC companies that are spending time there and doing some, some good stuff.

AI assessment note: “Superhuman will want to own the full experience.”

Q Break them up, Rahul, break them up. Awesome. On a more broader side. So you mentioned you only have three people working on AI. What's kind of like the coding AI adoption at superhuman on the engineering team?

A Yeah. Interestingly, like our path was, so we, we started to really think about it like in Q one, like a bunch of like People are using some stuff and everything. We didn't have any data, just anecdotal feedback and all of that. The first thing we've done is like cut the red tape. Like, hey folks, free for all. I will approve the budget like in one hour. You can try anything you want and deal with the security team. 24 hours turn around to get things approved from a security standpoint because you don't want to do some crazy things. Um, huge Q-one was like, everyone was trying everything. It was really interesting to see, like, how things were, like, working super well on the front end, a bit less on the back end. We were, we were a go shop on the back end, and, um, everyone, like, working on iOS and Swift were like, eh, not that good at the time. But, like, a huge adoption in terms of, uh, in terms of tooling. Also, like, on the product side, uh, a lot of, like, uh, V-Zero, uh, who said, uh, For Next.js? No, VZero, VZero is, uh, it's kind of like a bold, uh, bold, um, it was like, um, because they, they build next year as sites, right?

AI assessment note: “huge adoption in terms of, uh, in terms of tooling”

Q Are you still involved at all with like the French startup ecosystem? I'm curious, like how you've seen things evolve since then?

A Yeah, it's pretty interesting. Like I've seen a, I've seen a change. Uh, now that I'm getting some gray hair, and I have some experience, like, uh, I try to give back to some extent, so I spend more time, uh, helping, like, the ecosystem there, but it's funny to see, like, the difference. Like, when you're here, you, we live in a small bubble, and it's crazy to see how even, like, other tech scenes are different. So, like, the grit, like, just, like, the grit, like, to get shit done, and, like, to, to, to move forward and everything. They have great education. When I said there, sorry, I'm like, we, there, I don't know where am I now. Um, so great education, great engineers and all of that, but not the mindset of like creating things. So not, not a lot of entrepreneur that much. It's changing. We had like some successes in Europe and especially in AI, like there's some, some cool stuff happening, but still like the way to think about product-led growth, like superhuman nailed it. But, you know, ways to think about like the way to structure your organization to scale fast, uh, the level of ambition as well, how to like, maybe not target France or target Italy to start with, target English and the world from the get go. And, uh, that would be something to, to think about. So I'm, I'm doing that, uh, quite some, uh, highly rewarding, uh, but it's, uh, yeah, it's, it's pretty cool.

AI assessment note: “I spend more time, uh, helping, like, the ecosystem there”

Q How do you think about evals? Like, are you evaling like one email draft at a time? Are you evaling a longer workflow? It's like, Just run us through, like, yeah, when you're testing Gemini, like, how do you decide what it's good at, what it's not good at? What's, like, the email structure?

A At first, we had a relatively naive approach, query answer, query answer, and having, like, a set of queries. We, over time, evolved into, like, thinking more about, like, the different dimensions that we want to target. Agent and off is a very typical type of problem space that you want to make sure It's like the right model for. So typically getting a bunch of queries targeting hard handoff that we've identified by through the footing or whatever, but trying to target a set of what we call canonical, I would say, queries along that dimension of, uh, I would say that specific problem space of agent handoff. But like there's more, like there's the deep search, like shit ton of emails, and you want to find that needle in the headstack That's a different type of category. So you need to have canonical queries that are like targeting that type of dimension because every single user will have their own way to, to question their own data set. And we cannot replicate every single data set of, uh, of people. The good thing is, uh, we have a bunch of users like Rahul or like myself. We receive like a shit ton of emails. Not on my French, by the way. I don't know if it's okay for the show, but He receives probably like 500 to a thousand emails a day.

AI assessment note: “evolved into, like, thinking more about, like, the different dimensions that we want to target.”