Chris Pedregal
speaker
154 appearances
1 recordings
1 series
first heard Feb 2025
last heard Feb 2025
Chris Pedregal’s voice in public audio — every appearance, attributed to the second.
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Appearances
Is this like an unsolved open problem where you need to do some exploration? first and then figure out what the right solution is. For the one where you know what you need to build, at least from our experience, it's the basic advice that everyone hears, which is build the minimal thing as quickly as possible.
Give yourself deadlines where you will ship it to real humans, maybe not to everybody, to real people, and then try to increase the shipping iteration speed as quickly as possible. I think we've gotten in trouble before and it's easy to, is you don't know what mode you're in and you use that philosophy to the open-ended problem.
And then what ends up happening is you end up shipping something crappy to people and you ticked it off. You're like, oh, we shipped it in two weeks. This is great. But actually you didn't actually solve the problem to be solved. The thing you did was you shipped as opposed to figure out what is a great solution for people and do that.
And interestingly, I'd say that is extra important in this space because there's so much pressure to move quickly that every now and then taking the extra time to think about how to do this is really important. A good example is we were working on Granola for a year before we launched. And we're so late to the AI note-taking game already. We were seven years late when we founded Granola.
We didn't launch for a year. You know how I talked about that interaction, like we completely changed the core interaction of the product. If we had launched that publicly, we never would have been able to switch it. There's no way because users would have learned a new behavior. Users would have said, oh, this is cool.
The ones who we would have retained would have liked it, but we wouldn't have retained that many users. That would have been it. I think that's very important to kind of protect your ability to change direction with the product until you have a lot of confidence that you're in the right direction. And how do you manage that while also moving in a really quickly in a fast moving space?
I ask myself if we're doing this correctly every day. Sam and I, when we started playing with LLMs, we became convinced that all the tools for work that we use are gonna be rebuilt or reinvented on top of LLMs. And we became convinced that there's gonna be like this new class of software
In the same way that if you were a developer, you probably spend all day in Cursor or Visual Studio, like some IDE. We think that there's gonna be a new class of software, it doesn't have a name yet, where people like you and I will spend all day in and we do our work in.
Folks whose jobs revolve around people and communication and projects and meetings and all that, there's gonna be a new workspace for those folks And that's what we set out to build from day one. And that's exactly what we're setting out to build now.
I think the interesting question for us is, it's really important if you're not an open AI or an anthropic, that you are really, really good at a use case today. You can't just be building a fantastic product in the future. You need to be damn useful at a very specific thing today. And every step along the way, you need to be super useful to people.
And I think there's a real tension there, which is how much time do you spend building the next obvious five things that are going to be really useful to people versus you take the big swing. And for us, we want to move from a world where you use granola for notes to use granola to do most of your work.
If you're writing a document or a memo, it should be way easier to do that in Granola because of all the context that we have about the work you're doing that's related to that. But that's a really big swing. Getting that right is going to take a lot of work and a lot of iteration.
My view on this is you can worry about a million things. You should choose selectively what to worry about because there are very few things out of your control. And the competitor that we have chosen to worry about at Granola is the one that hasn't launched yet.
It's the startup that can look at what we figured out, what other people figured out, and start at that point and execute on that more quickly than us. That's what we're thinking about. I was surprised at how quickly the big tech companies reacted to AI.
Like there's this moment, I think like ChachiBT kind of went mainstream and then you saw every big tech company pivot and try to adapt to that strategy. So I was impressed by the leadership there. I think just because you choose to do something doesn't mean it's easy for you to execute on it. So one of our investors, he has this thing, which is if you list out all the AI features,
that you use on a daily basis, how many of them were built by big tech versus how many of them were built by startups? And I think a surprising number of those were built by startups, even though every big tech company is out there investing a tremendous amount of money to build AI features. So does that get figured out over time? Maybe.
Startups are oftentimes the R&D wing of all the big tech companies. And then once something's figured out, they can incorporate that to their large user bases. But generational companies, they figured something out earlier and they were able to leverage that into becoming something massive.
I want tools that make us more human and better humans. And by that, I mean tools that kind of unlock our creativity, unlock our ability to just basically do all the things that humans are incredible at that no one else can do. I think the people who are building tools with AI need to be very intentional about that.
Because I think there's a fine line where you want to outsource all the rote work, all the boring stuff, the mindless stuff, but you really don't want to outsource the judgment. When you were talking about generating ideas and you're asking AI to generate 100 different ideas and you can choose the right ones, that's great. There's a danger, though, that that's what everyone is doing.
And now we're only looking at the ideas that are coming from AI. And that's just one example, but that trickles down to everything. It's like, oh, okay, well, this idea of writing is thinking. And if AI is doing the writing for you, well, a lot of that writing is just rote work. There's no value in any way. But some of it is where you do your thinking.
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