Building the Silicon Brain - with Drew Houston of Dropbox

**Alessio** (0:04)
Hey, everyone, welcome to the Latent Space Podcast. This is Alessio, Partner and CTO at Decibel Partners, and there's no Swyx today, but I'm joined by Drew Houston of Dropbox. Welcome, Drew.

**Drew Houston** (0:13)
Thanks for having me.

**Alessio** (0:14)
So we're not going to talk about the Dropbox story. We're not going to talk about the Chinatown bus, and the crash drive, and all that. I think you talked enough about it.

**Drew Houston** (0:22)
Yeah.

**Alessio** (0:22)
Where I want to start is you as an AI engineer. So as you know, most of our audience is engineering folks, kind of like technology leaders. You obviously run Dropbox, which is a huge company, but you also do a lot of coding. I think I saw you spend almost 400 hours just coding. So let's start there. What was the first interaction you had with an LLM API, and when did the journey start for you?

**Drew Houston** (0:43)
Yeah. Well, I think probably all AI engineers or whatever you call an AI engineer, those people started out as engineers before that. So engineering is my first love. I mean, I grew up as a little kid. I was that kid. My first line of code was at five years old. I just really loved, I want to make computer games like all this whole path. That also led me into startups and eventually starting Dropbox. And then with AI specifically, I studied computer science. I did my undergrad, but I didn't do like grad level computer science. I sort of got distracted by all the startup things, so I didn't do grad level work. But about several years ago, I made a couple of things. So one is I sort of, I knew I wanted to go from being an engineer to a founder. And then, but sort of the becoming a CEO part was sort of backed into the job. And so a couple of realizations. One is that, I mean, there's a lot of repetitive and manual work you have to do as an executive that actually lends itself pretty well to automation, both for my own convenience and then out of interest in learning, I guess what we call classical machine learning these days. I started really trying to wrap my head around understanding machine learning and informational retrieval more formally, so I'd say maybe 2016, 2017 Started writing these successively more elaborate scripts to understand basic classifiers and regression and again, basic information retrieval and NLP back in those days.
There are two things that came out of that. One is techniques are super powerful and even just like studying like old school machine learning was a pretty big inversion of the way I had learned engineering. Right? I started programming. When everyone starts programming, you're sort of the human, you're giving an algorithm to the and spelling out to the computer, how it should run it and then machine learning. Here's machine learning where it's like actually flip that, like give it sort of the answer you want and it'll figure out the algorithm, which was pretty mind-bending and it was both pretty powerful when I would write tools like figure out like time audits, or like where's my time going? Is this meeting a one-on-one or is it a recruiting thing or is it a product strategy thing? I started out doing that manually with my assistant but then found that this was like a very like automatable task and so which also had the side effect of teaching me a lot about machine learning. But then there was this big problem like anytime you, it was very good at like tabular structure of data but like anytime it hit the usual malformed English that humans speak it would just like fall over. I had to kind of abandon a lot of the things that I wanted to build because like there's no way to like parse text like maybe it would sort of identify the part of speech in a sentence or something. But then fast forward to the LLM, I mean actually I started trying some of like this, what we would call like very small LLMs before kind of the GPT class models and it was like super hard to get those things working. So like these 500 parameter models would just be like hallucinating and repeating and you know. So actually I had kind of like written it off a little bit. But then the ChatGPT launch and GPT-3 for sure. And then once people figured out like prompting and instruction tuning, this was sort of like November-ish 2022 like everybody else. Sort of the ChatGPT launch being the starting gun for the whole AI era of computing. And then having API access to three and then early access GPT-4.