Drew Houston: recommendation

Transcript context

…The thing with self-driving, maybe one of the big breakthroughs was like simulation. So it's like, okay, instead of driving, we can simulate these environments. It's really hard to do when knowledge work, you know, how do you simulate like a product review? How do you simulate these things? I'm curious if you've done any experiments. I know some companies have started to build kind of like a virtual personas that you can like bounce ideas off of. I mean, fortunately in a company you generate lots of, you know, actual human training data all the time. And then I also just like start with myself, like, all right, I can, you know, it's pretty tricky even within your company to be like, all right, let's open all this up as quote training data. But, you know, I can start with my own emails or my own calendar or own stuff without running into the same kind of like privacy or other concerns. So I often like start with my own stuff. And so that is like a one level of bootstrapping, but actually four or five years ago during COVID, we decided, you know, a lot of companies were thinking about how do we go back to work? And so we decided to really lean into remote and distributed work because I thought, you know, this is going to be the biggest change to the way we work in our lifetimes. And COVID kind of ripped up a bunch of things, but I think everybody was sort of pleasantly surprised how with a lot of knowledge work, you could just keep going. And actually you were sort of fine. Work was decoupled from your physical environment, from being in a physical place, which meant that things people had dreamed about since the fifties or sixties, like telework, like you actually could work from anywhere. And that was now possible. So we decided to really lean into that because we debated, should we sort of hit the fast forward button or should we hit the rewind button and go back to 2019? And obviously that's been playing out over the last few years. And we decided to basically turn, we went like 90% remote. We still, the in-person part's really important. We can kind of come back to our working model, but we're like, yeah, this is, everybody is going to be in some kind of like distributed or hybrid state. So like instead of like running away from this, like let's do a full send, let's really go into it. Let's live in the future. A few years before our customers, let's like turn Dropbox into a lab for distributed work. And we do that like quite literally, both of the working model and then increasingly with our products. And then absolutely, like we have products like Dropbox Dash, which is our universal search product. That was like very elevated in priority for me after COVID because like now you have, we're putting a lot more stress on the system and on our screens, it's a lot more chaotic and overwhelming. And so even just like getting the right information, the right person at the right time is a big fundamental challenge in knowledge work and these, in the distributed world, like big problem today is still getting, you know, has been getting bigger. And then for a lot of these other workflows, yeah, there's, we can both get a lot of natural like training data from just our own like strategy docs and processes. There's obviously a lot you can do with synthetic data and you know, actually like LMs are pretty good at being like imitating generic knowledge workers. just our own like strategy docs and processes. There's obviously a lot you can do with synthetic data and you know, actually like LMs are pretty good at being like imitating generic knowledge workers. So it's, it's kind of funny that way, but yeah, the way I look at it is like really turn Dropbox into a lab for distributed work. You think about things like what are the big problems we're going to have? It's just the complexity on our screens just keeps growing and the whole environment gets kind of more out of sync with what makes us like cognitively productive and engaged. And then even something like Dash was initially seeded, I made a little personal search engine because I was just like personally frustrated with not being able to find my stuff. And along that whole learning journey with AI, like the vector search or semantic search, things like that had just been the tooling for that. The open source stuff had finally gotten to a place where it was a pretty good developer experience. And so, you know, in a few days I had sort of a hello world type search engine and I'm like, oh my God, like this completely works. You don't even have to get the keywords right. The relevance and ranking is super good. We even like untuned. So I guess that's to say like I've been surprised by if you choose like the right algorithm and the right approach, you can actually get like super good results without having like a ton of data. And even with LLMs, you can apply all these other techniques to give them, kind of bootstrap kind of like task maturity pretty quickly.…

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