# Building the Silicon Brain - Drew Houston of Dropbox

Latent Space · 2024-10-18

<https://addtry.com/1e9de637-5a02-4eaf-9207-a028c2fac528>

Drew Houston, CEO of Dropbox, details his hands-on AI engineering journey and the company's strategic pivot to AI-first products like Dropbox Dash for universal search and access control. Having spent over 400 hours coding with LLMs, he built personal tools that seeded Dropbox AI, including a file question-answering system. He advocates 'rent, don't buy' for AI infrastructure, relying on open-source models and keeping options open as costs drop 10-100x yearly. Houston explains Dropbox's advantage in trust and data privacy, positioning it as a neutral platform that integrates with Google Drive and OneDrive. He discusses staying relevant through constant learning and founder mode, and advises founders to systematically train skills ahead of their company's growth.

## Questions this episode answers

### What is Dropbox's 'silicon brain' concept?

Drew Houston describes the 'silicon brain' as a complement to the human brain, analogous to the Industrial Revolution offloading physical work. Just as electricity made mechanical energy available on demand, AI provides on-demand cognitive energy. Dropbox aims to build a 'silicon brain' that handles attention filtering and cognitive busy work, letting humans focus on higher-level tasks while AI reads a million things effortlessly.

[42:47](https://addtry.com/1e9de637-5a02-4eaf-9207-a028c2fac528?t=2567000)

### What is Drew Houston's personal AI engineering stack?

Drew Houston uses VS Code with the Continue.dev chat UI, proxying requests to his own backend router. For offline coding, he carries a gaming laptop with a GPU, running local models like Llama via custom inference stacks (XLama, vLLM, SGLang). He prefers lightweight frameworks (Next.js, Flask, SQLite) and focuses on embedding context to avoid copying and pasting.

[12:15](https://addtry.com/1e9de637-5a02-4eaf-9207-a028c2fac528?t=735000)

### What is Drew Houston's 'rent, don't buy' advice for AI infrastructure?

Drew Houston advises a 'rent, not buy' strategy for AI compute because the technology is evolving so rapidly. He likens it to overinvesting in 286 processors when faster chips are imminent. Open-source competition is driving rapid price‑performance improvements, so relying on cloud models preserves agility and avoids being stuck with outdated hardware.

[52:13](https://addtry.com/1e9de637-5a02-4eaf-9207-a028c2fac528?t=3133000)

## Key moments

- **[0:00] AI Journey**
  - [2:18] Drew Houston: 'Machine learning flips traditional programming: give it the answer you want, and it'll figure out the algorithm.'
  - [4:00] Drew Houston started coding AI tools on his honeymoon in Thailand after the ChatGPT launch in November 2022.
- **[4:14] AI Expectations**
  - [4:47] Drew Houston told Sam Altman seven years ago that machine learning and AI was the biggest startup opportunity, but the models weren't ready.
  - [6:13] Drew Houston on timing: 'Being early is the same as being wrong, being late is the same as being wrong.'
  - [7:44] Drew Houston draws an analogy from self-driving levels of autonomy to AI adoption: level one is Copilot's tab autocomplete, level five is full autonomy.
  - [9:08] Drew Houston believed remote work would be the biggest change to work in our lifetimes, so Dropbox went 90% remote.
  - [10:51] Drew Houston on synthetic data: 'LLMs are pretty good at being like generic knowledge workers.'
- **[12:14] Engineering Setup**
  - [12:21] Drew Houston's AI engineering stack: VS Code, continue.dev, a custom backend router, and Sonnet 3.5 as the default model.
  - [13:24] Drew Houston brings a gaming laptop with an external GPU on planes to run Llama and do offline AI coding.
- **[15:51] RAG vs Context**
  - [15:56] Drew Houston on RAG vs long context: you need both, like RAM and hard disk, and they'll keep horse trading.
  - [17:20] Drew Houston advises using the smallest AI model that works for production to optimize cost and latency, not always frontier models.
- **[18:06] Dropbox AI**
  - [18:32] Drew Houston wrote a January 2023 memo to Dropbox declaring an AI inflection point and calling for an AI-first strategy.
  - [19:34] Dropbox's first AI feature, FileGPT, was a RAG-based Q&A on file previews, built bottom-up by engineers.
  - [21:24] Drew Houston: The file browser hasn't changed since the early '80s, and the browser with 100 tabs is a medieval state.
  - [24:00] Drew Houston reframes Dropbox's mission: 'It's not keeping files in sync, it's keeping people in sync.'
- **[26:30] Product & Trust**
  - [28:42] Drew Houston on defensibility: incumbents' playbook is 'copy, bundle, kill.'
  - [29:50] Drew Houston argues LLMs are a bad business: if your model isn't on the Pareto frontier, it has zero economic value.
  - [30:45] Drew Houston predicts AI value will accrue at the application layer and the semiconductor layer.
  - [31:58] Drew Houston on Dropbox's trust moat: 'We only make money if you pay us, and you only pay us if we do a good job.'
- **[33:42] Dash & Search**
  - [34:15] Drew Houston says Dropbox is no longer reliant on being the store of record; Dash integrates Google Drive and OneDrive.
  - [35:40] Drew Houston positions Dash for Business as a security complement to Copilot, giving IT universal visibility and control over sharing.
  - [40:18] Drew Houston introduces Dropbox Stacks: a mixed-format smart collection like playlists for work, bridging files, links, and cloud docs.
  - [41:04] Drew Houston on Dash solving the 'un-share guy' problem: IT admins manually comb through shared links across platforms.
  - [42:10] Drew Houston compares Dropbox's pivot to Netflix's DVD-to-streaming transition: same value prop, different delivery.
- **[42:40] Silicon Brain**
  - [43:30] Drew Houston: AI is like the Industrial Revolution for cognitive energy — large models let us bottle up and offload cognitive busy work.
  - [44:44] Drew Houston: Today's work tools are 'if you wanted to design an environment that made it impossible to get into a flow state, like what we have is that.'
  - [46:01] Drew Houston on email overload: 'There's no product question for which 80,000 unread emails is the right answer.'
  - [47:22] Drew Houston uses a CPU/GPU analogy: humans should orchestrate AI agents, offloading repetitive tasks to the silicon brain.
  - [48:20] Drew Houston, Meta board member, says open source AI will prevent an oligopoly and accelerate innovation.
- **[48:45] AI Economics**
  - [49:56] Drew Houston: The 10–100x improvement in AI model price-performance wouldn't have happened without open source.
  - [52:27] Drew Houston's hardware advice: 'rent, don't buy' AI GPUs now; it's like buying pallets of 286s when the 386 is coming.
- **[54:50] Staying Founder**
  - [56:09] Drew Houston's cross-domain analogy: 'You can learn a lot about the future of GPUs from how Formula 1 teams work.'
  - [57:32] Drew Houston on consumer product design: 'When you sign into Netflix, you don't see a bunch of titles starting with AA.'
  - [1:00:50] Drew Houston on founder CEO evolution: 'At some point, you start being able to see the matrix.'
  - [1:02:42] Drew Houston on founder mode: after learning enough, you have 'a table flipping' moment to redesign the company.
- **[1:03:10] Founder Advice**
  - [1:04:35] Drew Houston's advice to founders: ask 'In five years, what do I wish I had been learning today?' to guide personal growth.
  - [1:06:59] Drew Houston on founder discomfort: 'Learning to walk towards that when you wanna run away from it.'
  - [1:07:09] Drew Houston on long-term growth: You won't be a great leader in five weeks, but in five years you can be really good.
- **[1:07:36] Building Teams**

## Speakers

- **Alessio** (host)
- **Drew Houston** (guest)

## Topics

Language Models, Search

## Mentioned

Anthropic (company), Dropbox (company), Meta (company), OpenAI (company), Airtable (product), ChatGPT (product), Cursor (product), Dropbox Dash (product), FileGPT (product), GPT (product), GitHub Copilot (product), Google Drive (product), Llama (product), OneDrive (product), SGLang (product), Sonnet (product), VS Code (product), continue.dev (product), vLLM (product)

## Transcript

### AI Journey

**Alessio** [0:05]
Hey, everyone. Welcome to the Leading Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and there's no Swix today, but I'm joined by Drew Houston of Dropbox. Welcome, Drew.

**Drew Houston** [0:14]
Thanks for having me.

**Alessio** [0:15]
So we're not gonna talk about the Dropbox story, we're not gonna talk about-

**Drew Houston** [0:18]
Yeah

**Alessio** [0:18]
... the Chinatown bus-

**Drew Houston** [0:19]
Some drives, yeah

**Alessio** [0:19]
... and the flash drive and all that. I think you've talked enough about it.

**Drew Houston** [0:22]
Yeah.

**Alessio** [0:23]
Where I wanna start is you as a AI engineer, so as you know, most of our audience is engineering folks, kinda 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, like, coding. So let, let's start there.

**Drew Houston** [0:38]
Sure.

**Alessio** [0:38]
What was, like, the first interaction you had with, like, an LLM API, and when did the journey start for you?

**Drew Houston** [0:44]
Yeah. Um, well, I think probably, like, all AI engineers or whatever you call an AI engineer, those people started out as engineers before that .

**Alessio** [0:51]
Yeah.

**Drew Houston** [0:51]
So, like, engineering's 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. Just really loved. I wanted to make computer games, like all this whole path.

That also led me into startups and eventually starting Dropbox. And then with AI specifically, uh, I mean, I, I studied computer science. I got my- I did my undergrad, but I didn't do, like, grad level computer science. Right?

I didn't- I sort of got distracted by all the-

**Alessio** [1:14]
Yeah

**Drew Houston** [1:14]
... startup things, so I didn't do grad level work. But about several years ago, I mean, a couple things. So one is I sort of- I, 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 couple realizations. One is that, um... I mean, there's a lot of, like, repetitive and, like, manual work you have-

**Alessio** [1:33]
Right

**Drew Houston** [1:33]
... to do as an executive that is actually lends itself pretty well to automation, both for, like, my own convenience, and then out of interest in learning, I guess, what we call, like, classical machine learning these days. I started really trying to wrap my head around understanding machine learning and information retrieval more, more formally.

So I'd say maybe 2016, 2017, started ma- writing these more successively more elaborate scripts to, like, understand basic, like, classifiers and regression and, and again, like, basic information retrieval and NLP back in those days. And there were sort of, like, 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.

**Alessio** [2:18]
Right. Yeah.

**Drew Houston** [2:18]
Right? You know, I started programming. When everyone starts programming, you're, 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, like, pretty powerful when I would write tools to, like, figure out, like, time audits-

**Alessio** [2:43]
Mm-hmm

**Drew Houston** [2:43]
... um, 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.

**Alessio** [2:55]
Right.

**Drew Houston** [2:55]
And so which also had the side effect of teaching me a lot about, um, machine learning. But then there was this big problem, like anytime you... It was very good at, like, tabular structured data, but, like, anytime it hit, you know, the usual malformed English that humans-

**Alessio** [3:08]
Right. Yeah

**Drew Houston** [3:08]
... speak, it would just, like, fall over. I had to kinda abandon a lot of the things that I wanted to build 'cause, like, you just, 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 kinda the GPT class models, and it was, like, super hard to get that, those things working.

So, like, these 500 parameter models would just be, like, hallucinating and repeating and, you know. So actually, I'd kinda, like, written it off a little bit.

**Alessio** [3:38]
Mm-hmm.

**Drew Houston** [3:38]
But then the ChatGPT launch and GPT-3 for sure, and then once people figured out, like, prompting and instruction tuning-

**Alessio** [3:45]
Mm

**Drew Houston** [3:46]
... this was sort of like November-ish 2022, like everybody else sort of had the ChatGPT launch being the starting gun for all, for the whole AI era of computing, and then having API access to three and then early access to GPT-4.

I was like, "Oh man, this, it's happening."

**Alessio** [4:02]
Yeah. Yeah.

**Drew Houston** [4:03]
Um, and so I was literally on my honeymoon, and we're, like, on a beach in Thailand, and I'm, like, coding, you know, these- ... like, AI tools to automate, like, writing or, you know, or to assist with writing and all these different use cases.

**Alessio** [4:13]
You're like, "I'm never going back to work."

### AI Expectations

**Drew Houston** [4:14]
But yeah.

**Alessio** [4:14]
"I'm gonna automate all of it-"

**Drew Houston** [4:16]
Totally

**Alessio** [4:16]
... "before I get a proper-"

**Drew Houston** [4:17]
And I was just, you know, ever since then... I mean, I, I've always been, like, coding, like, prototypes and just stuff to make my life more convenient. But, like, escalated a lot after '22, and yeah, I spent... I, I checked.

I think it was probably, like, f- over 400 hours this year-

**Alessio** [4:31]
Mm-hmm

**Drew Houston** [4:31]
... so far coding 'cause I had my paternity leave where I was able to work on some special projects. But yeah, it's a super important part of, like, my whole learning journey is, like, being really hands-on with these things, and I mean, it's probably not a typical recipe, but I really love to get down to the metal-

**Alessio** [4:46]
Yeah

**Drew Houston** [4:46]
... as far as how this stuff works.

**Alessio** [4:47]
Yeah. Swix and I were with Sam Altman in October '22. We were, like, at a hack day at OpenAI, and that's why we started this podcast eventually. But you did an interview with Sam, like, seven years ago. And he asked you, "What's the biggest opportunity in startups?"

And you were like, "Machine learning and AI." And you were almost, like, too early, right?

**Drew Houston** [5:05]
Mm-hmm.

**Alessio** [5:05]
It's like maybe seven years ago, the models weren't quite there. How should people think about revalidating, like, expectations of this-

**Drew Houston** [5:12]
Mm-hmm

**Alessio** [5:12]
... technology? You know, I think even today people will tell you, "Oh, models are not really good at X," because they were not good 12 months ago-

**Drew Houston** [5:18]
Yeah

**Alessio** [5:18]
... but they're good today. What's your process for that?

**Drew Houston** [5:20]
Heuristics for-

**Alessio** [5:21]
Yeah

**Drew Houston** [5:21]
... for thinking about that, or how is... Yeah, I think the way I look at it now is pretty, has evolved a lot since when I started. I mean, I think everybody intuitively starts with, like, "All right, let's try to predict the future," or imagine, like, what's this great end state we're gonna get to?

And the tricky thing is, like, often those prognostications are right, but they're right in terms of direction, but not when. For example, you know, even in the early days of the internet, '90s, when things were even, like, tech space and, you know, even before, like, the browser or things like that, people were like, "Oh man, you're gonna have-" You know, you're gonna be able to order food.

You, you get like a Snickers delivered to your house. You're gonna be able to watch any movie ever created, and they were right, but they were like, you know, it took, you know, 20 years for that to actually happen.

**Alessio** [6:02]
Right.

**Drew Houston** [6:02]
And before you got to DoorDash, you had to get... You started with like Webvan and Kozmo. And before you get to Spotify, you had to do like Napster and Kazaa and Limewire and like a bunch of like broken Britney Spears MP3s

**Alessio** [6:13]
Right. Yeah.

**Drew Houston** [6:13]
And stuff like, and malware. So I think the big lesson is, um, and being early is the same as being wrong, being late is the same as being wrong. So really how do you calibrate timing? And then I think with AI, it's the same thing that people are like, "Oh, it's gonna completely upend society in all these positive and negative ways."

I think that's pro- like most of those things are gonna come true. The question is like, when is that gonna happen?

**Alessio** [6:32]
Right.

**Drew Houston** [6:33]
And then with AI specifically, I think there's also a, in addition to sort of the general tech category or like jumping too fast to the future, I think that AI is particularly suscep- susceptible to that, and you look at self-driving, right?

This idea of like, oh my God, you can have a self-driving car captured everybody's imaginations 10, 12 years ago. And you know, people are like, "Oh man, in two years there's not gonna be another, you know, there's not gonna be a human driver on the road to be seen."

It didn't work out that way, right? We're still 10, 12 years later where we are in a world where you can sort of sometimes get a Waymo in like one city on earth.

**Alessio** [7:05]
Right. Yeah. Yeah. Yeah.

**Drew Houston** [7:06]
Exciting, but just took a lot longer than people think. And, and the reason is there's a lot of other like time con- there's like a lot of engineering challenges, um, but then there's a lot of other like societal time constants that are hard to compress.

So one thing I think what you can learn from things like self-driving is they have these levels of autonomy-

**Alessio** [7:22]
Mm-hmm

**Drew Houston** [7:23]
... that, that's a useful kind of framework in driving or these like maturity levels. People sort of skip to like level five full autonomy, or we're gonna have like an autonomous knowledge worker that's gonna take, you know, that's gonna...

And then we won't need humans anymore kind of projection that that's gonna take a long time. But then when you think about level one or level two, like these little assistive experiences, you know, we're seeing a lot of traction with those.

So what you see really working is the level one autonomy in the AI world would be like the tab auto complete in, in Copilot, right?

**Alessio** [7:53]
Yeah.

**Drew Houston** [7:53]
GitHub Copilot and then, you know, maybe a little higher is like the chatbot type interface. Obviously you wanna get to the highest level you can-

**Alessio** [8:00]
Right

**Drew Houston** [8:00]
... to build a good product, but the reliability just isn't, and the capability just isn't there in the early innings. And so, and then you think of other level one, level two type things like Google Maps probably did more for self-driving than, than, than literal self-driving.

Like, you know, a billion people have like the ability to have like maps and navigation just like taken care of for you autonomously. So I think the timing and maturity are really important factors to include.

**Alessio** [8:23]
The thing with self-driving, maybe one of the big breakthroughs was like simulation.

**Drew Houston** [8:27]
Mm-hmm.

**Alessio** [8:27]
So it's like, okay, instead of driving we can simulate these environments. It's really hard to do with knowledge work, you know?

**Drew Houston** [8:32]
Mm-hmm.

**Alessio** [8:32]
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 sort of built kind of like, uh, virtual personas that you can like bounce ideas off of, um.

**Drew Houston** [8:42]
I mean, fortunately in a company you generate lots of, you know-

**Alessio** [8:46]
Mm-hmm

**Drew Houston** [8:46]
... actual human training data-

**Alessio** [8:48]
Right

**Drew Houston** [8:49]
... all the time. And then I also just like start with myself, like, all right, I can triage, 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, um, the same kind of like privacy or other concerns. Um, 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? We decided to really lean into remote and distributed work because I thought, you know, this is gonna 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 find work was decoupled from your physical environment, from being in a physical place, which meant that things people had dreamed about since the '50s or '60s, like telework, like you actually could work from anywhere and that was now possible.

So we decided to really lean into that 'cause we debated should we sort of hit the fast forward button or should we hit the rewind button, go back to 2019? You know, 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 gonna 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 with our working model and then increasingly with our products. And then absolutely, like we have products like Dropbox Dash, sort of universal search, uh, 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 to 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 LLMs are pretty good at being like gen-

**Alessio** [11:06]
Right

**Drew Houston** [11:06]
... imitating generic knowledge workers. So it's, it's, um, 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 gonna have is just the complexity on our screens just keeps growing and, 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, I made a little personal search engine 'cause I was just like personally frustrated with-

**Alessio** [11:33]
Mm-hmm

**Drew Houston** [11:34]
... not being able to find my stuff.

**Alessio** [11:35]
Right.

**Drew Houston** [11:36]
And along that whole learning journey with AI, like the vector search or semantic search, 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 pr- 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, c-kind of bootstrap, kinda like task maturity pretty quickly.

### Engineering Setup

**Alessio** [12:15]
Before we jump into Dash, let's talk about the Drew Houston AI engineering stack. So IDE, let's break that down.

**Drew Houston** [12:21]
Yeah, sure.

**Alessio** [12:21]
What ID do you use? Do you use Cursor, VS Code? Do you use any coding assistant? Like which chat? Is it just auto-complete?

**Drew Houston** [12:28]
Yeah, yeah. Uh, both. So I use VS Code as, like, my daily driver, although I'm, like, super excited about things like Cursor or the AI agents. I have my own, like, stack underneath that. I mean, some off-the-shelf parts, some pretty custom.

So I use the continue.dev, just like AI chat UI basically as just the UI layer, but I also proxy the request. I don't-- Or I proxy the request to my own backend, which is sort of like a router.

You can use any backend. I mean, Sonnet 3.5 is probably the best all around.

**Alessio** [12:56]
Yeah.

**Drew Houston** [12:56]
But then these things are, like, pretty limited if you don't give them the right context, and so part of what the proxy does is, like, there's a separate thing where I can say, like, "Include all these files by default with the request."

Then it becomes a lot easier and, like, without, like, cutting and pasting. And I'm building mostly, like, prototype toy apps.

**Alessio** [13:11]
Yeah, yeah.

**Drew Houston** [13:11]
So there's usually like a front-end React thing and a Python backend thing, and so it can do these, like, end-to-end diffs, basically. And then I also, like, love being able to host everything locally or do it offline. So I have my own...

When I'm on a plane or something or where, like, you don't have access or the internet's not reliable, I actually bring a gaming laptop

**Alessio** [13:30]
Uh-huh.

**Drew Houston** [13:31]
On, on the plane with me. It's like a little, like, blue briefcase-looking thing. And then I, like, literally hook up a GPU, like, into one of the outlets. And then I have-- I can do, like, transcription. I can do, like, auto-complete.

And, like, I have, you know, eight billion mo- like, Llama will run fine on it.

**Alessio** [13:45]
And you're using, like, a Ollama to run the model, like-

**Drew Houston** [13:47]
Uh, no, I use, um... I have my own, like, LLM inference stack.

**Alessio** [13:51]
Okay, yeah.

**Drew Houston** [13:51]
I mean, it uses-- The backend's somewhat interchangeable, so everything from, like, XLama to vLLM or SGLang. There's, there's a bunch of these different backends you can use. And then I started, like, working on stuff before all this tooling was, like, really available.

So, you know, over the last several years, I've built, like, my own, like, whole crazy environment and, like, and stack here. So I'm a little nuts about it.

**Alessio** [14:12]
Yeah. What's the state-of-the-art for... I, I guess not state-of-the-art, but, like, when it comes to, like, frameworks and things like that, do you like using them? I think maybe a lot of people say, "Hey, things change so quickly."

They're, like, trying to abstract things.

**Drew Houston** [14:23]
Yeah.

**Alessio** [14:23]
It's maybe too early today.

**Drew Houston** [14:25]
As much as I do a lot of coding, I have to be pretty surgical with my time because I, I don't have that much time.

**Alessio** [14:30]
Yeah.

**Drew Houston** [14:30]
Which means I have to sort of, like, scope my innovation to, like, very specific places or, like, my time. So for the front end, it'll be, like, a pretty vanilla stack, like a, you know, Next.js React-based thing. And then these are toy apps, so it's like Python, Flask, SQLite, and then all the different...

Then there's a whole other thing on, like, the backend, like how do you get, sort of run all these models locally or with a local GPU. The sort of scaffolding on the front end is pretty straightforward. The scaffolding on the backend is pretty straightforward.

But then a lot of it is just, like, the LLM inference and control over, like, fine-grain aspects of how you do generation, caching, things like that. And then there's a lot-- like, a lot of the work is how do you take, sort of go to an IMAP, like take an email-

**Alessio** [15:11]
Yeah

**Drew Houston** [15:11]
... get a new, or a document, uh, or a spreadsheet or, you know, any of these kinds of primitives that you work with, and then translate them, you know, render them in a format that an LLM can understand.

So there's, like, a lot of work that goes into that too.

**Alessio** [15:24]
Yeah. Yeah, I built a kinda, like, email triage assistant.

**Drew Houston** [15:28]
Yeah.

**Alessio** [15:28]
And, like, I would say eighty percent of the code is, like, Google OAuth and, like, pulling the emails.

**Drew Houston** [15:32]
Right. Yeah, totally.

**Alessio** [15:32]
And then the actual AI part is pretty easy.

**Drew Houston** [15:34]
Yeah, and even same experience, and then I tried to do all these, like, NLP things, and then to my dismay, like, a bunch of regexes for, like, got you, like, ninety-five percent of the way there.

**Alessio** [15:45]
Right. Yeah.

**Drew Houston** [15:45]
And so I still leave it running. I just haven't really built, like, the LLM, LLM-powered version of it yet.

**Alessio** [15:51]
Mm-hmm. Yeah, yeah, yeah. Any thoughts on RAG versus long context? Especially, I mean, with Dropbox, you know.

### RAG vs Context

**Drew Houston** [15:56]
Sure.

**Alessio** [15:56]
Do you just wanna shove things in? Like, have you seen that be a lot better?

**Drew Houston** [15:59]
Well, they kind of have different strengths and weaknesses, so you need both for different use cases. I mean, it's been awesome in the last twelve months, like, now you have these, like, long context models that can actually do a lot.

You have... You can put a book in, you know, Sonnet's context, and then now with the later versions of Llama, you can have 128k context. So that's sort of the new normal, which is awesome, and that, that wasn't even the case a year ago.

Um, that said, models don't always u- certain- and certainly, like, local models don't use the full context well fully yet. And actually, if you provide too much irrelevant context, the, the quality degrades a lot. And so I'd say in the open source world, like, we're still just getting to the cusp of, like, the full context is usable.

**Alessio** [16:39]
Mm-hmm.

**Drew Houston** [16:39]
And then, of course, like, when you're something like Dropbox Dash, like, it's basically building this whole, like, brain that's, like, read everything your company's ever written. And so that's not gonna fit into your context window, so you need RAG just as a practical reality.

And even for a lot of similar reasons, you need, like, RAM and hard disk, um, in conventional computer architecture. And I think these things will keep, like, horse trading, like maybe if, you know, a million or ten million is the new-

**Alessio** [17:02]
Mm-hmm

**Drew Houston** [17:02]
... tokens is the new context length, maybe that shifts. Maybe the bigger picture is, like, it's super exciting to talk about the LLM and, like, that piece of the puzzle, but there's this whole other scaffolding of more conventional, like, retrieval or conventional machine learning, especially because you have to scale up products to, like, millions of people.

What you do in your toy app is not gonna scale to that from a cost or latency or performance standpoint. So I think you really need these, like, hybrid architectures that where you have very, like, purpose-fit tools. You're, you're probably not using Sonnet 3.5 for all of your normal product use cases.

You're gonna use, like, a fine-tuned eight billion model or sort of the minimum model that gets you the right output, and then a smaller model also is, like, a lot more cost and latency, versus has, like, much bes- better characteristics on that front.

**Alessio** [17:48]
Mm-hmm. Yeah, let's jump into the Dropbox AI story. So-

**Drew Houston** [17:51]
Sure

**Alessio** [17:52]
... your initial prototype wa- was FilesGPT, you called it.

**Drew Houston** [17:56]
Yeah.

**Alessio** [17:56]
How did it start, and then how did you communicate that internally? You know, I know you have a pretty strong, like, memo culture.

**Drew Houston** [18:02]
Sure.

**Alessio** [18:03]
When were you like, "Okay, hey, we gotta really take this seriously"?

**Drew Houston** [18:06]
Yeah. Well, on the latter, it was, so how do we s-- Maybe I'll say, like, how we took Dropbox, how AI seriously as a company started kind of around that ti- that honeymoon time, unfortunately. In January, I wrote this, like, memo to the company, like, around- Basically like how we need to pl-play offense in '23, in that most of the time the kinda concrete is set, and like the winners are the winners and things are kind of frozen.

### Dropbox AI

**Drew Houston** [18:32]
But then with these new eras of computing, like the PC or the internet or the phone or, the concrete unfreezes and you can sort of build, do things differently and have a new set of winners. It's sort of like a new season starts.

As a result of a lot of that sort of personal hacking and just like thinking about this, I'm like, yeah, this is an inflection point in the industry, like we really need to change how we think about our strategy.

And then, uh, becoming an AI-first company was probably the headline thing that we did. And then, and then that got... And then, you know, calling on everybody in the company to really think about in your world, how is AI gonna reshape your workflows?

Or what's sort of the AI native way of thinking about your job?

**Alessio** [19:09]
Right.

**Drew Houston** [19:09]
And, you know, FileGPT, which is sort of this Dropbox AI kinda initial concept, that actually came from our engineering team as-

**Alessio** [19:16]
Mm-hmm

**Drew Houston** [19:16]
... you know, as we like called on everybody, like really think about what we should be doing that's new or different. So it was kind of organic and bottoms up like a-

**Alessio** [19:24]
Right. Yeah, yeah

**Drew Houston** [19:24]
... a bunch of engineers just kinda hacked that together, and then that materialized as basically when you preview a file on Dropbox, you can have k-kind of the most straightforward possible integration of AI, which is a good thing.

Like, basically you have a p- long PDF, you wanna be able to ask questions of it, and so like a pretty basic implementation of RAG and being able to, to do that when you preview a file on Dropbox.

So that was the origin of that. That was like back in 2023 when we were really just, like the starting engines had just, you know, gotten going.

**Alessio** [19:53]
It's funny where you're basically like these files that people have, they really don't want them in a way. You know? Like you're storing all these files and like you actually don't wanna interact with them, you want a layer on top of it.

**Drew Houston** [20:02]
Yeah.

**Alessio** [20:03]
And that's kinda what also takes you to Dash eventually-

**Drew Houston** [20:06]
Mm-hmm

**Alessio** [20:06]
... which is like, hey, you actually don't really care where the file is, you just wanna be the place that aggregates it. How do you think about what people will know about files? You know-

**Drew Houston** [20:15]
Yeah

**Alessio** [20:15]
... are files the actual file? Are files like the metadata, and they're just kinda like a pointer that goes somewhere and you don't really care where it is? Yeah, any thoughts about-

**Drew Houston** [20:23]
Totally. Yeah. I mean, there's a lot of potential complexity in that question.

**Alessio** [20:27]
Right.

**Drew Houston** [20:27]
Right? Or is it a, you know, what's the difference between a file and a URL, and you can go into the technicals, which is, oh, is this like pass by value, pass by reference? Okay, what's the format like?

All right, well, now if it's real-time collaborative, it's not really a flat file, it's like a structured data, you're sort of collabora- you know, that's g- keeping in sync, blah, blah, blah. I actually don't start there at all.

I just start with like, what do people... Like what do humans let's work back from like how humans think about this stuff or how they should think about this stuff. Meaning like I don't think about, oh, here are my files, and here are my-

**Alessio** [20:55]
Mm-hmm

**Drew Houston** [20:55]
... links or cloud docs. I'm just sort of like, "Oh, here's my stuff. This, this, here's sort of my documents, here's my media, here's my projects, here are the people I'm working with." So it starts from primitives more like those, like how do people, how do humans think about these things?

And then, then start from like a more ideal experience. 'Cause if you think about it, we kinda have this situation that will look like particularly medieval in hindsight, where, all right, how do you manage your work stuff? Well, on a l- you know, on one side of your screen, you have this file browser that literally hasn't changed since the early '80s.

**Alessio** [21:24]
Yeah.

**Drew Houston** [21:24]
Right? You could take someone from the original Mac and sit them in front of like a computer and they'd be like, "This is it?" And that's wh- it's been 40 years.

**Alessio** [21:31]
Right.

**Drew Houston** [21:31]
Right? Then on the other side of your screen, you have like Chrome or a browser that has so many tabs open you can no longer see text, uh, or titles. This is the state of the art for how we manage stuff at work.

Interestingly, neither of those experiences was purpose-built to be like the home for your work stuff or even anything related to it, like... And so it's important to remember we get like stuck in these local maxima pretty often in tech, where we're obviously aware that files are not going away, especially in certain domains, like the format really matters, and where files are still gonna be the tool you use for like if there's something big, right?

If you have a big video file, that kind of format in a file makes sense. There's a bunch of industries where it's like construction or architecture, sort of these sp- domain-specific areas. You know, media generally, if you're making music or photos or video, that all kind of fits in the big file zone where Dropbox is really strong, and that's like what customers love us for.

But you know, it's also pretty obvious that a lot of stuff that used to be in, you know, Word docs or Excel files or something like all that has tilted towards the browser, and that tilt is gonna continue.

So with Dash we wanted to make something that was really like cloud native, AI native, and deliberately like not be tied down to the abstractions of the file system. Now, on the other hand, it would be like ironic and bad if we then like fractured the experience such that like, well, if it touches a file, it's a syncing metaphor in a d- this app, and if it's a-

**Alessio** [23:00]
Right

**Drew Houston** [23:01]
... URL, it's like this completely different interface. So there's a convergence that I think makes sense over time. But, um, you know, but I think you have to start from like not so much the technology. Start from like what, what do the humans want, and then like what's the idealized product experience, and then like what are the technical underpinnings of that that can make that good experience.

**Alessio** [23:20]
I think it's counterintuitive that in Dash you can connect Google Drive, right?

**Drew Houston** [23:24]
Yeah.

**Alessio** [23:24]
Because you think about Dropbox as like, well, it's file storage. You really don't want people to store files somewhere, but the reality is that they do. How do you think about the importance of storage? And like do you kinda feel storage is like almost solved, where it's like, hey, you can kinda store these files anywhere?

**Drew Houston** [23:37]
Yeah.

**Alessio** [23:37]
What matters is like access.

**Drew Houston** [23:38]
It's a little bit nuanced in that, um, if you're dealing with like large quantities of data, it actually does matter. The implementation matters a lot, or like if you're dealing with like, you know, 10 gig video files like that, then you sort of inherit all the problems of sync and have to-

**Alessio** [23:51]
Yeah

**Drew Houston** [23:51]
... go into a lot of the challenges that we've solved. Touching on a pretty important question, like what is the value we provide? What, what, what does Dropbox do? And probably like most people, I would've said like, "Well, Dropbox syncs your files."

And we didn't even really have a mission of the company in the beginning where I'm just like, "Yeah, I just don't wanna carry a thumb drive around and be... Life would be a lot better if our stuff just like lived in the cloud, and, and I just didn't have to think about like what device is the thing on or what operating, why are these operating systems fighting with each other and incompatible."

You know, I just want to abstract all of that away. But then so we thought, even we were like, "All right, Dropbox provides storage." But when we talk to our customers, they're like, "That's not how we see this at all.

Like, actually Dropbox is not just like a, a hard drive in the cloud, it's like the place where I go to work," or, "It's a place, like, I started a small business. It's a place where my dreams come true."

**Alessio** [24:39]
Mm-hmm.

**Drew Houston** [24:39]
Or it's like, "Yeah, it's not keeping files in sync, it's keeping people in sync, in sync. It's keeping my team in sync." And so they're using this kinda language where we're like, wait, okay, yeah, 'cause I, you know, I don't know, storage probably is a commodity or what we do is a commodity.

Like, but then we talk to our customers, they're like, "No, like this... We're not buying the storage. We're buying like the ability to access all of our stuff in one place. We're buying the ability to share everything and sort of..."

In a lot of ways, people are buying the ability to work from anywhere, and Dropbox was kind of... The fact that it was like files syncing was an implementation detail of this higher order need that they had. So, you know, I think that's where we start, too, which is like, all right, what is the sorta higher order thing, the job the customer's hiring Dropbox to do?

Storage in the new world is kinda incidental to that. I mean, it still matters for things like video or those kinds of workflows. The value of Dropbox has never been we provide you, like, the cheapest bits in the cloud, but it is a big pivot from Dropbox is the company that syncs your files to now where we're going is Dropbox is the company that kinda organ- helps you organize all your cloud content.

I started the company 'cause I kept forgetting my thumb drive, but the question I was really asking was like, why is it so hard to like find my stuff, organize my stuff, share my stuff, keep my stuff safe?

You know, I'm always like one washing machine and I would leave like my little thumb drive with all my prior company stuff on-

**Alessio** [25:57]
Right. Yeah

**Drew Houston** [25:57]
... in the pocket of my shorts and then almost wash it and destroy it. And so I was like, why do we have to... This is like medieval that we have to think about this. So that, that's, that same mindset is how I approach where we're going.

But I think, and then unfortunately the... We're sorta back to the same problems. Like, it's really hard to find my stuff, it's really hard to organize my stuff, it's hard to share my stuff, it's hard to secure my content at work.

Now, the problem is the same. The shape of the problem and the shape of the solution is pretty different. You know, instead of 100 files on your desktop, it's now 100 tabs in your browser-

**Alessio** [26:28]
Yeah

**Drew Houston** [26:28]
... et cetera. But I think that's the starting point.

### Product & Trust

**Alessio** [26:30]
How has the idea of a product evolved for you? So, you know, famously, Steve Jobs started by Dropbox and he's like, "You know, this is just a feature, it's not a product," and then you build like a $10 billion feature.

**Drew Houston** [26:40]
Yeah.

**Alessio** [26:40]
Uh, how... In the age of AI, how do you think about, you know, maybe things that used to be a product are now features because the AI on top of it is like the product? Like, what's your mental model to think about it?

**Drew Houston** [26:50]
Yeah. So I don't think there's lit- really like a bright line. I, I don't know if, like, I use the word features and products in my mental model that much of how I break it down, 'cause it's, it's kind of a break...

Yeah, but no, it's, it's a good question. I mean, I don't not think about features-

**Alessio** [27:05]
Yeah, yeah

**Drew Houston** [27:05]
... or not think about products. But it does start from that place of like, all right, we have all these new colors we can paint with.

**Alessio** [27:10]
Mm-hmm.

**Drew Houston** [27:11]
And all right, what are these higher order needs that are sort of evergreen, right? So people will always have stuff at work. They'll always need to be able to like find it or, you know, all the verbs I just mentioned.

So like, okay, how can we make like a better painting and how can we... And then how can we use some of these new colors? And then, yeah, it's like pretty clear that after the large models, the way you find stuff, organize stuff, share stuff is gonna be completely different.

After COVID, it's gonna be completely different. So that's the starting point. But I think it is also important to, you know, y- y- you have to do more than just work back from the customer and like what they're trying to do.

Like, you have to think about, and, you know, we've, we've learned a lot of this, a lot of this the hard way sometimes. Okay, you might start with a customer, you might start with a job to be done.

Then you're like, all right, what's the solution to their problem? Or like, can we build the best product that solves that problem, right? So can we build the best way to find your stuff in the modern world? Like, well, yeah, right now the status quo for the vast majority of the billion, billion knowledge workers is they have like 10 search boxes at work that each search 10% of your stuff.

Like, that's clearly broken. Obviously, you should just have like one search box, right? So we can do that. And that by... also has to be like, I'll come back to defensibility in a second, but like can we build the right solution that is like meaningfully better from the status quo?

Like, yes, clearly. Okay. Then can we like get distribution and growth? Like, that's sort of the next thing you learn is you s- as a founder, you start with like, what's the product? What's the product? What's the product?

**Alessio** [28:32]
Mm-hmm.

**Drew Houston** [28:32]
Then you're like, wait, wait, we need distribution and we need a business model. So those are the next kinda two dominoes you have to knock down or sort of needles you have to thread at the same time. So, all right, how do we grow?

I mean, we have Dropbox 1.0. It was really this like self-serve viral model that there's a lot of... We sort of took a, borrowed from a lot of the consumer internet playbook and like what Facebook and social media were doing, and then translated that to sort of the business world.

But how do you get distribution, you know, especially as a startup, and then a business model like, all right, storage happened to be some... in the beginning, happened to be something people were willing to pay for. They recognize that.

You know, okay, if I don't buy something like Dropbox, I'm gonna have to buy an external hard drive. I'm gonna have to buy a thumb drive. I have to pay for something one way or another. People were already paying for things like backup, so we felt good about that.

But then the last domino is like defensibility. Okay, so you build this product or you get the business model, but then, you know, what do you do when the incumbents... The next chess move for them is just like copy, bundle, kill.

Um, so they're gonna copy your product, they'll bundle it with their platforms, and they'll f- like give it away for free or no added cost. And, you know, we had a lot of, you know, scar tissue from being on the wrong side of that.

Now, you don't need to solve all four for all four or five variables or whatever at once, or you can sort of have, you know, some flexibility, but the more of those gates that you get through, you sort of add a 10X to your valuation.

And so with AI, I think, you know, there's been a lot of focus on the large language model, but it's... Like large language models are a pretty bad business from a, you know, if you sort of take off your tech lens and your sort of business lens.

Like, there's sort of this weirdly self-commoditizing thing where, you know, models only have value if they're kind of on this like Pareto frontier of size and quality and cost. Being number two-

**Alessio** [30:13]
Yeah. Right

**Drew Houston** [30:13]
... you know, if you're not on that frontier, the second, the second the frontier moves out, which it moves out every week-

**Alessio** [30:17]
Mm-hmm

**Drew Houston** [30:18]
... like your model literally has zero economic value 'cause it's dominated by the new thing. LLMs generate output that can be used to train or improve. So there's these weird, peculiar things that are specific to the large language model, and then you have to like be like, all right, where's the value gonna accrue in the, in the v- in the stack or the value chain?

And, you know- Certainly at the bottom with Nvidia and the semiconductor companies, and then, then it's gonna be at the top, like the people who own-- who have the customer relationship, who have the application layer.

**Alessio** [30:45]
Mm-hmm.

**Drew Houston** [30:45]
Those are a few of the like lenses that I look-

**Alessio** [30:47]
Yeah

**Drew Houston** [30:48]
... at a question like that through.

**Alessio** [30:49]
Do you think AI is making people more careful about sharing their data at all? People are like, "Oh, data is important," but it's like, "Whatever, I'm just throwing it out there." Now everybody's like, "But are you gonna train on my data?"

And like your data's actually not that good to train on anyway. But like how have you seen especially customers like think about what to put in, what to not?

**Drew Houston** [31:07]
I mean, everybody should be-- Well, everybody is concerned about this, and everybody should be concerned about this, right? Because nobody wants their personal info or company's information to be kinda ground up into little pellets to like sell you, sell you ads or, or train the next foundation model.

I think it's like massively top of mind for every one of our customers like, and me personally, and with my Dropbox hat on is like so fundamental. And you know, we had experience with this too at Dropbox one point now, the same kind of resistance.

Like, "Wait, I'm gonna take my stuff on my hard drive-

**Alessio** [31:37]
Mm-hmm

**Drew Houston** [31:37]
... and put it on your server somewhere. Are you serious? Um, like what could possibly go wrong?" And you know, before that, I was like, "Wait, you're gonna tell me I'm gonna put my credit card number into this website."

And before that, I was like, "Hey, you're gonna-- I'm gonna take all my cash and put it in a bank instead of under my mattress." You know, so there's a long history of like tech and comfort. So in some sense, AI's kind of another round of the same thing, but the issues are real.

And then when I think about like defensibility for Dropbox, like that's actually a big advantage that we have is, one, our incentives are, are very aligned with our customers, right? We only get-- we only make money if you pay us, and we-- you only pay us if we do a good job.

So we don't have any like side hustle.

**Alessio** [32:13]
Mm-hmm.

**Drew Houston** [32:13]
You know, b- we're not training the next foundation model. You know, we're not, uh, trying to sell you ads. Actually, we're not even trying to lock you into any ecosystem. Like the whole point of Dropbox is it works-

**Alessio** [32:22]
Mm-hmm

**Drew Houston** [32:22]
... you know, everywhere. Because I think one of the big questions, and we, we've circling around this, we're like, "All right, in the world of AI, where should our lane be?" Like every startup has to ask, or and every big company has to ask like, "Where can we really win?"

But f- to me, it was like a lot of the like trust advantages, I mean, platform agnostic, having like a very clean business model, not having these other incentives. And then we also are like super transparent. We were transparent early on.

We're like, "All right, we're gonna establish these AI principles." The very table stake stuff of like here's transparency. We wanna give people control. We wanna cover privacy, safety, bias, like fairness, all these things. And we put that out up front to put some sort of explicit guardrails out where like, "Hey, we're..."

You know, 'cause everybody wants like a, a trusted partner as they sort of-

**Alessio** [33:03]
Yeah

**Drew Houston** [33:03]
... go into the wild world of AI. And then, you know, you also see people cutting corners and, you know... Or just there's a lot of uncertainty or, you know, moving the p- moving the pieces around after the fact, which, which no one feels good about.

**Alessio** [33:14]
I mean, I would say the last ten, 15 years, the race was kinda being the system of record, being the storage provider.

**Drew Houston** [33:20]
Mm-hmm.

**Alessio** [33:20]
I think today it's almost like, hey, if I can use Dash to like access my Google Drive file, why would I pay Google for like their AI feature? So like vice versa, you know, if I can connect my Dropbox storage to this other AI assistant, how do you kinda think about that?

About, you know, not being able to capture all the value and how open people will stay? I think today things are still pretty open, but I'm curious if you think things will get more closed or like more open later.

### Dash & Search

**Drew Houston** [33:42]
Yeah. Well, I think you have to get the value exchange right, and I think you have to be like a trustworthy partner. Or like no one's gonna partner with you if they think you're gonna eat their lunch, right?

Or they'll-- if you're gonna disintermediate them. And like all the companies are quite sophisticated with how they think about that. So we try to... Like we know that's gonna be the reality, so we're actually not trying to eat anyone's like Google Drive's lunch or anything.

Actually, we'll like integrate with Google Drive. We'll integrate with OneDrive, really any of the content platforms, even if they compete with file syncing. So that, that's actually a big strategic shift.

**Alessio** [34:12]
Mm-hmm.

**Drew Houston** [34:12]
We're not really reliant on being like the store of record. And there are pros and cons to this decision. But if you think about it, we're basically like providing all these apps more engagement. We're like helping users do what they're really trying to do, which is to get, you know, that Google Doc or whatever.

Then we're not trying to be like, "Oh, by the way, use this other thing." This is all part of our like brand reputation. It's like, no, we, we give people freedom to use whatever tools or operating system they want.

We're not taking anything away from our partners. We're actually like making it, ma-making their thing more useful or routing people to those things. I mean, on the margin, there might be something like, well, okay, to the extent you do RAG and summarize things, maybe that doesn't generate a click, okay.

You know, we also know there's like infinity investment going into like-

**Alessio** [34:51]
Right.

**Drew Houston** [34:51]
... the work agents. So we're not really building like a Copilot or Gemini competitor. We-- Not because we don't like those, we don't find that thing like captivating. Yeah, of course, but just like, you know, you learn after some time in this business that like, yeah, there's some places that are just gonna be such kind of red oceans or just like super big battlefields.

Everybody's kinda trying to solve the same problem, and they just start duplicating all each other effort. And then meanwhile, you know, I think the concern would be is like, well, there's all these other problems that aren't being properly addressed by AI, and I was concerned that like...

Yeah, and everybody's like fixated on the agent or the chatbot interface, but forgetting that like, hey guys, like we have the opportunity to like really fix search or build a self-organizing Dropbox or environments, or there's all these other things that can be a complement 'cause we don't really want our customers to be thinking like, "Oh, do I use Dash or do I use Copilot?"

And frankly, none of them do. In a lot of ways, actually, some of the things that we do on the security front with Dash for Business are a good complement to Copilot. Because as part of Dash for Business, we actually give admins IT, like universal visibility and control over all the different-- what's being shared in your company across all these different platforms.

And as a precondition to installing something like Copilot or Dash or Glean or any of these other things, right? You know, IT wants to know, like, "Hey, before we like turn all, all the lights in here- ... like let's do a little cleaning first before we let everybody in."

And there just haven't been good tools to do that, and post-AI, you would do it completely differently, and so that's like a big... That's a cornerstone of what we do and what sets us apart from these tools. And actually, in a lot of cases, we will help those tools be adopted because we actually help them do it safely.

**Alessio** [36:27]
Yeah. How do you think about building for AI versus people? It's like when you mentioned cleaning up, it's because maybe before you were like, well, humans can have some common sense when they look at data on what to pick versus models are just kinda like ingesting.

Do you think about building products differently, knowing that a lot of the data will actually be consumed by LLMs and like agents and whatnot versus like just people?

**Drew Houston** [36:47]
I think it'll always be I aim a little bit more for like, you know, level three, level four kinda automation, 'cause even if the LLM's like capable of completely autonomously organizing your environment, it probably would do a reasonable job, but like I think you build bad UI when the sort of user has to fit itself to the computer versus something that you're, you know, it's like an instrument-

**Alessio** [37:07]
Yeah

**Drew Houston** [37:07]
... you're playing or something where you have some kind of good partnership, and, you know, and on the other side, you don't have to do all this like manual effort. And so, like the command line was sort of subsumed by like, you know, graphical UI.

We'll keep toggling back and fl- you know, maybe chat will be... Chat will be an increasing, especially when you bring in voice, like will be an increasing part of the puzzle, but I don't think we're gonna go back to like a million command lines-

**Alessio** [37:29]
Yeah

**Drew Houston** [37:29]
... either. And then as far as like the, the sort of plumbing of like, well, is this gonna be consumed by an LLM or a human, like fortunately, like you don't really have to design it that differently. I mean, you have to make sure everything's legible to the LLM, but it's like quite tolerant of, you know, malformed -

**Alessio** [37:43]
Yeah, yeah

**Drew Houston** [37:43]
... everything, and actually the more... The easier you make something to read for a human, the easier it is for an LLM to read to some extent as well. But we really think about what's that kinda right... How do we build that right like human-machine interface where you're still in control and driving, but then it's super easy to translate your intent into like the, you know, however you want your folder-

**Alessio** [38:02]
Yeah, yeah, yeah

**Drew Houston** [38:02]
... just set your environment set up or like your preferences.

**Alessio** [38:05]
What's the most underrated thing about Dropbox that maybe people don't appreciate?

**Drew Houston** [38:10]
Well, I think this is just such a natural evolution for us. It's pretty true, like when people think about the world of AI, file syncing is not like the you know, next thing you would auto-complete mentally. And I think we also did like our first thing so well that there were a lot of benefits to that, but I think there are also...

Or like we hit it so hard with our first product that it was like pretty tough to-

**Alessio** [38:31]
Right

**Drew Houston** [38:32]
... come up with a sequel, and we had a bit of a sophomore slump and, you know, I think actually a lot of kids do use Dropbox through in high school or things like that, but you know, they're not, they use...

They're a lot more in the browser than their file system, right? And, and we know all this. But still, like we're super well positioned to like help a new generation of people with these fundamental problems and these like...

That, that affect, you know, a billion knowledge workers around just finding, organizing, sharing your stuff and keeping it safe, and there's, there's a ton of unsolved problems in, in those four verbs, right? We've talked about search a little bit, but just even think about like a whole new generation of people like growing up without the ability to like organize their things .

And yeah, search is great, and if you just have like a giant infinite pile of stuff, then search does make that more manageable. But you know, you do lose some things that were pretty helpful in prior decades.

**Alessio** [39:23]
Yeah.

**Drew Houston** [39:23]
Right? So even just the idea of persistence, stuff still being there when you come back, like when I go to sleep and wake up, my physical papers are still on my desk. When I reboot my computer, the files are still on my hard drive.

But then when in my browser, like if my operating system updates the wrong way and closes the browser, or if I just more commonly just declare tab bankruptcy-

**Alessio** [39:41]
Mm-hmm

**Drew Houston** [39:41]
... it's like your whole workspace just clears itself out and starts from zero, and you're like, "On what planet is this a good idea?"

**Alessio** [39:48]
Right.

**Drew Houston** [39:48]
You know, there's, there's no like concept of like, "Oh, here's the stuff I was working on. Yeah, let me get back to it." And so that was like a big motivation for things like Dash. Huge problems with sharing, right?

If I'm remodeling my house or if I'm getting ready for a board meeting, you know, what do I do if I have a Google Doc and an Airtable and a 10 gig 4K video? There's no collection that holds mixed format things, and so it's another kinda hidden problem hidden in plain sight, like these missing primitives like, yeah, files have folders, songs have playlists, links have...

You know, there's no-

**Alessio** [40:19]
Right

**Drew Houston** [40:19]
... somehow we missed that, and so we're building that with, with Stacks in Dash where it's like a mixed format smart collection that you can then, you know, share whatever you need internally, externally, and, and have it be like a really well-designed experience and platform agnostic and not tying you to any one ecosystem.

We're super excited about that. You know, we talked a little bit about security in the modern world, like I-IT signs all these compliance documents, but in reality has no way of knowing where anything is or what's being shared.

It's actually better for them to not know about it than to know about it and not be able to do anything about it. And when we talked to customers, we found that there were like literally people in IT whose jobs it is to like manually go through, log into each like, log into Office, log into Workspace, log into each tool, and like go comb through one by one the links that people have shared, and like un-share.

S- there's like an un-share guy-

**Alessio** [41:05]
Yeah, yeah

**Drew Houston** [41:06]
... in all these companies, and that, that job is probably about as fun as it sounds, like my God. So there's, you know, fortunately, I guess what makes technology a good business is for every problem it solves, it like creates a new one.

**Alessio** [41:17]
Right.

**Drew Houston** [41:18]
Um, so there's always like a sequel that you need. And so, you know, I think the happy version of our act two is kind of, it's kind, it's kind of similar to Netflix or like a lot of these companies that really had multiple acts, and you know, Netflix had the vision to be streaming from the beginning, but broadband and everything wasn't ready for it.

So they started by mailing you DVDs, but then went to streaming, and then... But the value prop the whole time was just like, "Let me press play on something I wanna see."

**Alessio** [41:40]
Mm-hmm.

**Drew Houston** [41:40]
And they did a really good job about bringing people along from the DVD mailing off. You would think like, oh, the DVD m-mailing piece is like this burning platform-

**Alessio** [41:48]
Mm-hmm

**Drew Houston** [41:48]
... or it's like legacy, you know, ankle weight, and they did have some false starts in that transition. But when you really think about it, they were able to take that DVD mailing audience, move, like migrate them to streaming, and actually bootstrap a, you know, take their season one people and bootstrap a vic- a victory in season two because they already had...

You know, they, they weren't starting from scratch.

**Alessio** [42:10]
Mm-hmm.

**Drew Houston** [42:10]
And like both of those worlds were like super... It's, it's super easy to sort of forget and be like, "Oh, it was all kind of destiny."

**Alessio** [42:17]
Mm-hmm.

**Drew Houston** [42:17]
But like no, that, that was like an incredibly competitive environment, and Netflix did a great job of like activating their act one advantages and winning in act two because of it. So I don't think people see Dropbox that way.

I think people are sort of thinking about us just in terms of our act one, and they're like, "Yeah, Dropbox is fine. I used it 10 years ago, but like what have they done for me lately?" And I, and I don't blame them.

So fortunately, we have like better and better answers to that question every year.

**Alessio** [42:40]
And you call it like the silicon brain, so you see like-

### Silicon Brain

**Drew Houston** [42:42]
Yeah

**Alessio** [42:42]
... Dash and Stacks being like the silicon brain interface-

**Drew Houston** [42:46]
Yeah

**Alessio** [42:46]
... basically for people?

**Drew Houston** [42:47]
I mean, that's part of it, yeah, and, and writ large. I think what's so exciting about AI, and everybody's got their own kind of take on it, but if you like really zoom out civilizationally and like what, what allows humans to make progress and, you know, what, what sort of is above the fold as in terms of what's really mattered, certainly one of-- I mean, there are a lot of points, but some that come to mind are like you think about things like the Industrial Revolution.

Like before that, like mechanical energy, like the only way you could get it was like by your own hands, maybe an animal, maybe some like clever sort of machines or, you know, machines made of like wood or something Right.

**Alessio** [43:22]
Yeah.

**Drew Houston** [43:22]
But you were sort of quite like energy limited. And then suddenly, you know, the Industrial Revolution, things like electricity, it suddenly is like, all right, mechanical energy is now available on demand.

**Alessio** [43:32]
Mm-hmm.

**Drew Houston** [43:32]
It's like very fungible kind of... And then suddenly we consume a lot more of it, and then the standard of living goes way, way, way, way up. That's been pretty limited to the physical realm. And then I believe that the large models, that's really the first time we can kinda bottle up cognitive energy or, or and, and offload.

You know, if we started by offloading a lot of our mechanical or physical busy work to machines, that freed us up to make a lot of progress in other areas. And then with AI and computing, we're like, now we can offload a lot more of our cognitive busy work to machines, and then, then we can create a lot more of it.

The price of it goes way down. Importantly, like it's not like humans never did anything physical again. They're sort of like, no, but we're more leveraged. We can move a lot more earth with a bulldozer than a shovel.

And so we're gonna-- that's like what is at, at the most fundamental level, what's so exciting to me about AI. And so what's the silicon brain? It's like, well, we have our human brains, and then we're gonna have this other like half of our brain that's sort of coming online, like our silicon brain.

And, and it's not like one or the other. They complement each other. They have very complementary strength and weaknesses, and that's, that's a good thing. There's also this weird tangent we've gone on as a species to like where knowledge work- knowledge workers have this like epidemic of, of burnout, great resignation, quiet quitting, and there's a lot going on there.

But I think that's one of the biggest problems we have is that be like people deserve like meaningful work and, you know, can't solve all of it, but like in at least in knowledge work, there's a lot of own goals you know, unforced, unforced errors that we're doing where it's like, you know, on one side with brain science, like we know what makes us like productive and, and fortunately, it's also what makes us engaged.

It's like when we can focus or when we're in some kind of flow state. But then we go to work, and then increasingly going to work is like going to a screen, and you're like, if you wanted to design an environment that made it impossible to ever get into a flow state or ever be able to focus, like what we have is that.

**Alessio** [45:21]
Hmm.

**Drew Houston** [45:21]
And that was the thing that just like seven, eight years ago just blew my mind. I'm just like, I cannot understand why like knowledge work is so jacked up on this dimension. It's like we, we put ourselves in like the most cognitively polluted environment possible, and we put so much more stress on the system when we're working remotely and things like that.

And, you know, all of these problems are just like going in the wrong direction. And I just, I just couldn't understand why this was like a problem that wasn't fixing itself. And I'm like, maybe there's something Dropbox can do with this.

And, you know, things like Dash are the first step. But then well, so like what? Well, I mean, now the, the... like well why are humans in this like polluted state? It's like, well, we're just-- all of the tools we have today, like this generation of tools, just passes on all of the weight, the burden to the human.

**Alessio** [46:01]
Right.

**Drew Houston** [46:01]
Right. So it's like, here's a bajillion, you know, eighty thousand unread emails.

**Alessio** [46:05]
Mm-hmm.

**Drew Houston** [46:05]
Cool. Here's twenty-five unread Slack channels. Here's... And it just-- we all get sort of like, just like jittery like thinking about it. And then you look at that and you're like, "Wait, I'm looking at my phone. It says like eighty thousand unread things."

There's like no question-

**Alessio** [46:19]
Right

**Drew Houston** [46:19]
... product question for which this is the right answer. Fortunately, that's why things like our silicon brain are pretty helpful 'cause like they can serve as like an, an attention filter where it's like actually computers have no problem reading a million things.

Humans can't do that, but computers can. And to some extent, this was already happening with computing. You know, Excel isn't a version of your silicon brain or, you know, you could draw the line arbitrarily. But with larger models, like now so many of these like little subtasks and tasks we do at work can be like fully a-automated.

And I think, you know, I think it's like an important metaphor to me 'cause I-- and it mirrors a lot of what we saw with computing, computer architecture generally. It's like we started out with the CPU, very general purpose, then GPU came along much better at these like parallel computations.

We talk a lot about like human versus machine being like substituting, but it's like CPU, GPU. It's not, it's not like one is categorically better than the other. They're complements. Like if you have something really parallel, use a GPU.

If, if not, use a CPU. That whole relationship, that symbiosis between CPU and GPU has obviously evolved a lot since, you know, playing Quake II or something.

**Alessio** [47:21]
Right.

**Drew Houston** [47:22]
But, but right now we're-- we have like the human CPU doing a lot of, you know, silicon CPU tasks, and so you really have to like redesign the work thoughtfully such that, you know, probably not that different from how it's evolved computer architecture, where the CPU is sort of an orchestrator of these really like heavy lifting GPU tasks.

That dividing line c- does shift a little bit, you know, with every generation. And so I think we need to think about knowledge work in that context. Like what, what are human brains good at? What's our silicon brain good at?

Let's re-segment the work. Let's offload all the stuff that can be automated. Let's go on a hunt for like anything that could save a human CPU cycle.

**Alessio** [47:55]
Right.

**Drew Houston** [47:55]
Let's give it to the silicon one. And so I think we're at the early earnings of actually being able to do something about it.

**Alessio** [48:00]
It's funny, I gave a talk to a few government people earlier this year with a similar point where we used to make machines to replace human labor, and then the kilowatt hour was kinda like the unit of growth-

**Drew Houston** [48:11]
Yeah

**Alessio** [48:11]
... for a lot of countries, and now you're doing the same thing with the brain. And these data centers are kind of computational power plants, you know? They're kinda on demand-

**Drew Houston** [48:18]
Yeah. That's true

**Alessio** [48:18]
... on demand tokens. You're on the board of Meta, which is the number one donor of flops for the-

**Drew Houston** [48:23]
Sure, yeah

**Alessio** [48:23]
... open source world. The thing about open source AI is like the model can be open source, but you need to carry a briefcase to actually maybe run a model- ... that is not even that good compared to some of the big ones.

How do you think about some of the differences in the open source ethos with like traditional software where it's like really easy to run and act on it versus like models where it's like it might be open source, but like I'm kinda limited-

**Drew Houston** [48:45]
Sure

**Alessio** [48:45]
... to what I can do with it?

### AI Economics

**Drew Houston** [48:45]
Yeah. Well, I think with every new era of computing, there's sort of a Tug of war between is this gonna be like an open one or a closed one? And, you know, there's pros and cons to both. It's not like, oh, open is always better, um, or open always wins.

But, you know, I think you look at how the mobile-- like the PC era and the internet era started out being more on the open side, like it's very modular, everybody... The sort of party that everybody could, you know, come to.

Some downsides of that, security. But I think, you know, the advent of AI, I think there's a real question, like given the capital intensity of what it takes to train these foundation models, like are we gonna live in a world where oligopoly or cartel or we're all...

So yeah, there's a few companies that have the keys, and we're all just like paying them rent. You know, that's one future. Or is it gonna be, you know, more open and accessible? And I'm like super happy with how that's just I find it exciting on many levels with, with all the different hats I wear about it.

You know, fortunately, you're see- you've seen in real life, yeah, even if people aren't, you know, bringing GPUs on a, on a plane or something , um, you, you've seen like the price performance of these models improve ten or a hundred x year over year, which is, it's sort of like many Moore's laws compounded together for, for a bunch of reasons.

Like that wouldn't have happened without open source, right? You know, for a lot of the same reasons, it's probably better that we can-- anyone can sort of spin up a website without having to buy an informa- internet information server license.

Like there was some alternative future, so like things were Linux and really good, and there was a good balance of trade, too, where like people would contribute their code and then also benefit from the community returning the favor.

I mean, you're seeing that with open source. Like you wouldn't see all this like, you know, this flourishing of research and of just sort of the democratization of access to compute if without open source. And so I think it's been like phenomenally successful in terms of just moving the ball forward.

And, and pretty much anything you care about, I believe, even like safety-

**Alessio** [50:34]
Yeah

**Drew Houston** [50:35]
... if you can have a lot more eyes on it and transparency instead of just something is happening and there was three places with nuclear power plants attached to them, right? So I think it's, um, you know, it's been awesome to see.

And then, and again, for like wearing my Dropbox hat, like anybody who's like scaling a service to millions of people, again, are probably not using like frontier models for every request. It's, you know, there are a lot of different configurations, mostly with smaller models, and even before-- you even talk about getting on the device, like, you know, you need this whole kinda constellation of different options.

So open source has been great for that.

**Alessio** [51:07]
And you were one of the first companies in the cloud repatriation- ... effort. You kinda brought back-

**Drew Houston** [51:12]
Yeah

**Alessio** [51:12]
... all the storage into your own data centers. Where are we in the AI wave for that? I don't think people really care today to-

**Drew Houston** [51:18]
Yeah

**Alessio** [51:18]
... bring the models in-house. Like-

**Drew Houston** [51:20]
Yeah

**Alessio** [51:20]
... do you think people will care in the future? Like especially as you have more small models, like you wanna control more of the economics? Or are the tokens so subsidized that like it just doesn't matter, it's more like a principle stand, like-

**Drew Houston** [51:31]
Yeah. Yeah. I mean, I think this is another one where like thinking about the future is a lot easier, or if you start with the past.

**Alessio** [51:36]
Right.

**Drew Houston** [51:36]
Um, so I mean, there's definitely this like big surge in demand as like there's sort of this FOMO-driven bubble of like all of big tech taking their earnings-

**Alessio** [51:46]
Right

**Drew Houston** [51:46]
... and shipping them to Jensen for a couple of years. And then you're like, all right, well, first of all, we've seen this kinda thing before, and in late '90s with like fiber-

**Alessio** [51:56]
Yeah

**Drew Houston** [51:56]
... you know, this huge race to like own the internet, own the information superhighway, literally-

**Alessio** [52:00]
Yeah.

**Drew Houston** [52:00]
And then way overbuilt, and then there was this like crash. I don't know to what extent. Like maybe it is really different this time, or, you know, maybe if we create AGI, that will sort of solve the rest of the-- or we'll just have a different set of things to worry about.

But, you know, the simplest way I think about it is like this is sort of a rent, not buy phase, 'cause, you know, I wouldn't wanna be-- We're still so early in the maturity. You know, I wouldn't wanna be buying like pallets of over like of 286s-

**Alessio** [52:27]
Mm-hmm

**Drew Houston** [52:27]
... at a 5X markup when like the 386 and 486 and Pentium and everything are like clearly coming. They're around the corner. And again, because of open source, there's just been a lot more competition at every layer in the stack, and so product developers are basically beneficiaries of that.

You know, the things we can do-- Well, the, the sort of cost estimates I was looking at a year or two ago to like provide different capabilities in the product, you know, cut-

**Alessio** [52:48]
Right. Yeah, yeah

**Drew Houston** [52:48]
... you know, just slash 'em by 10, 100, 1,000X. I think they're all coming back around. I mean, I think, you know, at some point, you have to believe that the sort of supply and demand will even out as it always does.

And then there's also like non-Nvidia stacks like the Groq or Cerebras or some of these custom silicon companies that are super interesting and all, and outperform Nvidia stack in terms of latency and things like that. So I think it's gonna be pretty exciting change.

I think we're not close to the point where we were with like hard drives or storage when we sort of went back from the public cloud, 'cause like there it was like, yeah, the cost curves are super predictable.

We know what the cost of a hard drive and a server and, you know, terabyte of bandwidth and, you know, all, all the inputs are gonna just keep going down, riding down this cost curve. But to like rely on the public cloud to pass that along is sort of we need a better strategy than like relying on the kindness of strangers.

Um, so we decided to bring that in-house and still do, and you still get a lot of advantages. Um, that said, like the public cloud has like scaled and been like a lot more reliable and just good all around than we would have predicted, 'cause actually back then, we were worried like, is the public cloud gonna even scale fast enough to where-

**Alessio** [53:52]
Right. Yeah, yeah

**Drew Houston** [53:53]
... to keep up with us. But yeah, I think we're in the early innings. It's a little too chaotic right now, so I think renting and not sort of-- preserving agility is pretty important-

**Alessio** [54:00]
Yeah

**Drew Houston** [54:01]
... in times like these.

**Alessio** [54:01]
Yeah. We just went to the Cerebras factory to do a, an episode there. We saw one of their data centers-

**Drew Houston** [54:06]
Yeah

**Alessio** [54:07]
... inside. Yeah, it's kinda like, okay, if this really works, you know, it kinda changes-

**Drew Houston** [54:10]
It can be a lot of difference available.

**Alessio** [54:11]
Yeah, exactly.

**Drew Houston** [54:12]
Yeah.

**Alessio** [54:12]
It's like, yeah, it kinda changes everything. Um-

**Drew Houston** [54:14]
And, and that is one of those things. There-- Like this is one where you could just have these things that just like, okay, there's just like a new kinda piece on the chessboard, like recalc everything.

**Alessio** [54:22]
Yeah.

**Drew Houston** [54:22]
So I think there's still... I mean, this is like not that likely, but I think this is an area where it actually could, you could have these sort of like, you know, and out of nowhere, sudden- all of a sudden-

**Alessio** [54:31]
Right. Yeah

**Drew Houston** [54:31]
... you know, everything's different.

**Alessio** [54:33]
Yeah. I know one of the management books you reference is Andy Grove's, uh-

**Drew Houston** [54:36]
Yeah

**Alessio** [54:36]
... Only the Paranoid Survive.

**Drew Houston** [54:37]
Yep.

**Alessio** [54:38]
Maybe if you look at Intel, they did a great job memory to chip, but then it's like maybe CPU to GPU, they kinda missed, missed that thing.

**Drew Houston** [54:45]
Yeah.

**Alessio** [54:46]
How do you think about staying relevant for so long? Now, it's been 17 years-

**Drew Houston** [54:49]
Yeah

**Alessio** [54:49]
... you've been doing Dropbox. What's the secret? And maybe we can touch on Founder Mode-

### Staying Founder

**Drew Houston** [54:53]
Sure

**Alessio** [54:54]
... you know, and, uh, and all of that.

**Drew Houston** [54:55]
Yeah. Well, first, what makes tech exciting and also makes it hard is like, there's no standing still, right? Your... And your customers never are like, "Oh, no, we're good now."

**Alessio** [55:03]
Right.

**Drew Houston** [55:03]
They always want more, just... And then the ground is shifting under you, or it's like, oh, yeah, well, files are not even that relevant to the modern... I mean, it's still important, but like-

**Alessio** [55:12]
Yeah

**Drew Houston** [55:12]
... you know, so much has tilted elsewhere. So I think you have to, like, always be moving and think about on the one level, like, what is... And, and think of these different layers of abstraction. Like, well, yeah, the technical service we provide is f- file syncing and storage in the past, but in the future it's gonna be different.

**Alessio** [55:27]
Mm-hmm.

**Drew Houston** [55:28]
The way Netflix had to look at, well, technically, we mail people-

**Alessio** [55:30]
Yeah

**Drew Houston** [55:30]
... physical DVDs in fulfillment centers, and then we have to switch to, like, streaming and codecs and bandwidth and data centers. So you, you, you do have to think about that level, but then it's like, all right, what's the evergreen problem we're solving is an important problem.

Can we build the best product? Can we get distribution? Can we get a business model? Can we defend ourselves when we get copied? And then having, like, some context of, like, history has always been, like, one of the m- you know, reading about the history, not just in tech, but of business or government or sports or military.

These things that seem, like, totally new, you know, and to me would have been, like, totally new as a 25-year-old, like, oh, my God, the world's completely different now and everything's gonna change. You're like, well, you know, there's not a lot of great things about getting older, but you do see like, well, no, this actually has, like, a million, like, precedents .

And you can actually learn a lot from, you know, about, like, the future of GPUs from like, I don't know, how, you know, how Formula 1 teams work. Or you can draw all these, like, weird analogies that are super helpful in guiding you from first principles or through a combination of first principles and, like, past context.

But like b- you know, build shit we're really proud of. Like, that's a pretty important first step. And really think about, like, you sort of become blind to, like, how technology works, as that's just the way it works.

And, you know, even something like carrying a thumb drive, you're like, well, it's... I'd much rather have a thumb drive than, like, literally not have my stuff or, like, have to carry a big external hard drive around. So you're always thinking like, "Oh, this is awesome.

Like, I can rip CDs into these, like, MP3s and these files and folders. This is the best." But then you miss on the other side, you're like, this isn't the end, right? MP3s and folders is like, then Apple comes along and is like, "This is dumb.

You should have, like, a catalog, an artist, playlist, you know?" Then Spotify's like, "Hey, this is dumb. Like you should... Why are you buying these things a la carte? It's the internet. You should have access to everything."

**Alessio** [57:05]
Mm-hmm.

**Drew Houston** [57:05]
And then by, by the way, why is this, like, such a single player experience? You should be able to share. You sh- and they should have y- a, they should be AI curated, et cetera, et cetera. Yeah, and then s- a lot of it is also just, like, drawing, connecting the dots between different disciplines, right?

So a lot of what we did to make Dropbox successful is, like, we took a lot of the consumer internet playbook-

**Alessio** [57:21]
Mm-hmm

**Drew Houston** [57:21]
... applied it to business software from a virality and kind of ease of use standpoint. And then, you know, I think there's a lot you can draw from the consumer realm and what's worked there and that hasn't been t- ported over to business, right?

So a lot of what we think about is like, yeah, when you sign into Netflix or Spotify or YouTube or any consumer experience, like, what do you see? Well, you don't see, like, a bunch of titles starting with-

**Alessio** [57:44]
Right

**Drew Houston** [57:44]
... AA, right?

**Alessio** [57:45]
Yeah.

**Drew Houston** [57:45]
You see, like, this whole... And it, it went on ev- evolution, right? Like, we talked about music, I mean, TV went through the same thing, like 10 channels over the air broadcast to 30 channels, 100 ch- but then suddenly like 1,000 channels, you're like, this has totally lost the plot.

So we're sort of in the thousand channels era-

**Alessio** [58:01]
Mm-hmm

**Drew Houston** [58:01]
... of productivity tools, where it's just like, wait, wait, we just need to, like, rethink the system here. And we don't need another thousand channels, we need to redesign the whole experience. And so, you know, I think the consumer experiences that are, like, smart, you know, when you sign into Netflix, it's not like 1,000 channels.

It's like, here are a bunch of smart defaults. Even if you're a new sign-up, we don't know anything about you, but because of what the world is watching, here are some, you know, reasonable suggestions. And then it's like, okay, I, I watched Drive to Survive, I didn't watch Squid Game.

You know, the next time I sign in, it's like a complete... It's a learning system, right?

**Alessio** [58:30]
Mm-hmm.

**Drew Houston** [58:30]
So a combination of design, machine learning, and just, like, the courage to, like, rethink the whole thing. I think that's y- that's a pretty reliable recipe. And then you think, you're like, all right, there's all that intelligence in the consumer experience.

There's no filing things away. Everything's just all sort of auto-curated for you and sort of self-optimizing. Then you go to work and you're like, there's not even an attempt to incorporate any intelligence or organization anywhere in this experience.

And so like, okay, can we do something about that?

**Alessio** [58:58]
You know, you're one of the last founder CEOs, like you, Zuck, then you're like Toby Lut- ... some of these folks. How, how does that change-

**Drew Houston** [59:05]
I'm like 300 years old and like-

**Alessio** [59:06]
No, but, but-

**Drew Houston** [59:07]
... founder

**Alessio** [59:07]
... I'm saying like when you run, when you run a company, like, uh, you've had multiple executives over the years, like how important is that for the founder to be CEO and just say, "Hey, look, we're changing the way the company and the strategy works."

**Drew Houston** [59:18]
Yeah.

**Alessio** [59:18]
It's like we're really taking this seriously versus like you could be a public CEO and be like, "Hey, I got my earnings call," and like, whatever. I just need to focus on getting the, the right numbers. Like, how does that change the culture in the company?

**Drew Houston** [59:29]
Yeah. Well, I think it sort of dovetails with the Founder Mode whole thing. You know, I think Founder Mode's kinda this Rorschach test. It's, it's sort of like ill-specified. So it's sort of like whatever you, you know, it is whatever you see it.

I think it's also like a destination you get to more than like a state of mind, right? So if you think about, you know, imagine someone, there was something called surgeon mode.

**Alessio** [59:50]
Mm-hmm.

**Drew Houston** [59:51]
You know, giving a med student a scalpel on day one it's like, okay, hold up. Um, you know, so there's something to be said for like experience and conviction and, you know, you're gonna do a lot better. A lot of things are a lot easier for me like 17 years into it than they were one year into it.

**Alessio** [1:00:06]
Right.

**Drew Houston** [1:00:07]
I think part of why Founder Mode is so resonant is, or it's like striking such a chord with so many people is, yeah, there's, so there's a real power when you have like a directive, intuitive leader who can like decisively take the company like into the future.

It's like, how the hell do you get that? Um, and I think every founder who makes it this long like kinda can't help it but to learn a lot during that period. And you talk about the, you know, Steve Jobs or Elons of the world, they, they did go through like wandering, a period of like wandering in the desert where like nothing was working and they weren't the cool kids.

I think you either sort of like unsubscribe or kinda get off the train-

**Alessio** [1:00:42]
Right

**Drew Houston** [1:00:42]
... during that, and I don't blame anyone for doing that. There are many times where I've thought about that. But I think at some point, you sort of-- it all comes together, and you sort of start being able to see the matrix.

So you've sort of seen enough and learned enough, and as long as you keep your learning rate up, you can kinda surprise yourself in terms of, like, how capable you can become over a long period. And so I think there's a lot of, like, founder CEO journey, especially as an engineer, like, you know, I never, like, set out to be a CEO.

In fact, like, the more I, like, understood in the early days what CEOs did, the more convinced I was that I was, like, not the right person, actually. And it was only after some, like, shoving by a previous mentor, be like, "Hey, don't...

Just, just go try it, and if you don't like it, then you don't have to do it forever." So I think you start Founder Mode. You're, you, you sort of default that because there's, like, you l- you realize pretty quickly, like, nothing gets done in this company unless the founders are literally doing it by hand.

Then you scale, and then you're like, you get, you know, a lot of actually pretty good advice, um, that, like, you can't do everything yourself. Like, you actually do need to hire people and, like, give them real responsibilities and empower people, and that's, like, a whole discipline called, like, management that, you know, we're not figuring out for the first time here.

But then you-- then there's a tendency to, like, lean too far back. You know, it's tough, and if you're, like, a 30-year-old and you hire a 45-year-old exec from, you know, high-flying company and a guy who was running, like, a $10 billion P&L and came to work for Dropbox, where we were, like, a fraction of a billion dollar P&L and, you know, what am I gonna tell him about sales, right?

And so you sort of recognize pretty, you're like, "I actually don't know a lot about all these different disciplines and, like, maybe I should lean back and, like, let people do their thing." But then you can create this, like, if you lean too far back out-

**Alessio** [1:02:18]
Yeah

**Drew Houston** [1:02:18]
... you create this sort of, like, vacuum, leadership vacuum where people are like, "What are we doing?" And then, you know, the system kinda, like, nature abhors a vacuum. It builds all these, like, kind of weird structures just to keep the thing, like-

**Alessio** [1:02:30]
Mm-hmm

**Drew Houston** [1:02:31]
... standing up, and then at some point you learn enough of this and you're like, "Wait, this is not how e- this should be designed," and you actually get, like, the conviction and the, you've learned enough to, like, know what to do and things like that.

And so, and then on the other side, you lean way back in. I think it's more of, like, a table flipping where you're like, "Hey, this company is, like, not running the way I want it. Like, some- I don't know what happened, but it's gonna be like this now."

And I think that that's, like, an important developmental stage for a founder CEO and, and if you can do it right and, like, make it to that point, like, then the job becomes, like, a lot of fun and, and exciting and good things happen for the company, good things are happening for your customers.

But it's not, it's, like, a really rough, you know, learning journey.

**Alessio** [1:03:10]
It is. It is. I've had many therapy sessions-

### Founder Advice

**Drew Houston** [1:03:12]
Yeah

**Alessio** [1:03:13]
... with, with founder CEOs. Let's go back to the beginning. Like, today, the AI wave is, like, so big that, like, a lot of people are kinda scared to jump in the water, and when you started Dropbox, one article said, "Fortunately, the Dropbox founders are too stupid to know everyone's already tried this."

In AI now, it kinda feels the same. You have a lot of companies that sound the same, but, like, none of them are really working, so obviously the problem is not solved. Do you have any advice for founders trying to navigate, like, the DMAs today and, like, what they should do, what are, like, counterintuitive things maybe to-

**Drew Houston** [1:03:44]
Yeah

**Alessio** [1:03:44]
... to try?

**Drew Houston** [1:03:45]
Well, I think, like s- you know, bringing together some of what we've covered, I think there's a lot of very common kinda category errors that founders make. One is, you know, thinking starting from the technology versus starting from, like, a customer or starting from a use case, and I think every founder has to start with what you know.

Like, you're, yeah, you know maybe if you're an engineer, you know how to build a product but don't know any of the other next, you know, hurdle. You don't know much about the next hurdles you have to go through.

So I think, I think the biggest lesson would be you have to keep your personal growth curve ahead of the company's growth curve, and for me, that meant you have to be, like, super systematic about training up what you don't know 'cause no one's gonna do that for you.

Your investors aren't gonna do that. Like, literally no one else will do that for you. And so then it, then you have to have, like, all right, well... And I think the most important, one of the most helpful questions to ask there is, like, in five years from now, what do I wish I had been learning today?

In three years from now, what do I wish I had been? In one year, you know, what, how m- how will my job be different? How do I work back from that? And so, for example, you know, when I was just starting in 2007, it really was just, like, coding and talking to customers and sort of like the YC ethos, you know, make something people want, and coding and talking to customers are really all you should be doing in that early phase.

But then if I were like, all right, what, that's sort of YC phase, what's, what are the next hurdles? Well, a year from now, then I'm gonna need-- But to get people, we're gonna need fundraise, like, raise money.

Okay, to raise money, we're gonna have to, like, have to answer all these questions. We have to blah, blah. So you, like, work back from that and you're like, all right, we need to become, like, an expert in, like, venture capital financing, and then, you know, the circle keeps expanding.

Then if we have a bunch of money, we're gonna need, like, accountants and lawyers and employees, and I'm gonna have to start managing people. Then two years would be like, well, we're gonna have this, like, product, but then we're gonna need users, we're gonna need money, revenue.

And then in five years it'll be like, yeah, we're gonna be, like, tangling with, like, Microsoft, Google, Apple, or Facebook, uh, but just like everybody, and, like, somehow we're gonna have to, like, deal with that. And then that's, like, what the company's gonna have to deal with, and as CEO, I'm gonna be responsible for all that.

But then, like, my personal growth. Okay, there's all these skills I'm gonna need. I'm gonna, like, need to know, like, what marketing is and, like, what finance is and how to manage people, how to be a leader, whatever that is.

And so, and then I think one thing people often do is they're like, oof, like, that, it's like imposter syndrome kinda stuff. You're like, oh, it seems so remote or far away that I'm not comfortable speaking publicly, or I, I've never managed people before.

I haven't this, I haven't that. And, like, and may- even learning a little bit about, about it makes it feel even worse. You're like, now I, I thought I didn't know a lot. Now I know I don't know a lot.

**Alessio** [1:06:05]
Mm-hmm. Right.

**Drew Houston** [1:06:06]
Part of it is, is more technical. Like how do I learn all these different disciplines and sort of train myself? And a lot of that's like reading, you know, having founders or community that are sort of going through the same things.

That, that was how I learned. Maybe reading was the single most helpful thing, more than any one person or, or talking to people, like reading books. But then there's a whole mindset piece of it, which is sort of like you have to cut yourself a little bit of slack.

Like, you know, I wish someone had sort of sat me down and told me like, "Dude, you may be an engineer, but, like, look at all the tech founders that, you know, tech CEOs that you admire. Like, they actually all s- you know, almost all of them started out as engineers.

They learned the business stuff on the job." So like this is actually something that's normal and achievable. You're not like broken for not knowing. And for-- And like, no, those people didn't-- weren't like-- didn't come out of the womb with like shiny hair and a Mar- Armani suit.

You know, you can learn this stuff. So even just like knowing it's learnable. And then second, like, but I think there's a big piece of it around like discomfort, where it's like, "Oh, I mean, we're like kind of pushing the edges.

I don't know if I wanna be CEO, or I don't know if I'm ready for this, this, this." Like learning to like walk towards that when you wanna run away from it. And then lastly, I think, you know, just recognizing the time constant.

So five weeks, you're not gonna be a great leader or manager or a great public speaker or whatever, you know, thing, any more than you'll be a great guitar player or, you know, play a sport that well or be a surgeon.

But in like five years, like actually you can be pretty good at, at any of those things. Maybe you won't be like fully expert, but you have like a lot more latent potential. You know, people have a lot more latent potential than they fully appreciate.

But it doesn't happen by itself. You have to carve out time and really be systematic about unlocking it.

### Building Teams

**Alessio** [1:07:36]
How do you think about that for building your team? I know you're a big Pats fan.

**Drew Houston** [1:07:39]
Yeah.

**Alessio** [1:07:40]
Obviously, the-- that's a great example of building a dynasty on like some building blocks and bringing people-

**Drew Houston** [1:07:45]
Totally

**Alessio** [1:07:45]
... to the system. When you're building a company, like how much slack do you cut people on, "Hey, you're gonna learn this," versus like how do you measure like the learning rate-

**Drew Houston** [1:07:53]
Yeah

**Alessio** [1:07:53]
... of the people you hire, and like how do you think about picking and choosing?

**Drew Houston** [1:07:56]
Great question. It's hard. Um, what you want is a balance, right? And we've had a lot of success with great leaders who actually grew up with the company, started as an, you know, IC engineer or something, then made their way to whatever level.

Like our, our exec team is populated with a lot of those folks. But, but you-- but there's also a lot of benefit to experience and having seen different environments and kinda been there, done that, and there's a lot of drawbacks to kinda learning by trial and error only.

Um, and then even your high potential people like can go up the learning curve faster if they have like some experience to learn from. Now, like experience isn't a panacea either. Like you can, you know, have various organ rejection or misfit or like overfitting from their past experience or cultural mismatches or, you know, you name it.

I've seen it all. I've done I've kind of gotten all the mistake merit badges on that. But I think it's like constructing a team where there's a good balance. Like, okay, for the high potential folks who are sort of in the biggest jobs of their lives can-- do they either have someone that they're-- is managing them that they can learn from?

You know, as a CEO, part of your job, or as a manager, like you have to like surround or help support them. So getting them mentors or getting first-time execs like mentors who have been there, done that, or, um, getting them in like, you know...

There's usually, for any function, there's usually like a social group, like, oh, chiefs of staff of Silicon Valley.

**Alessio** [1:09:09]
Mm-hmm.

**Drew Houston** [1:09:09]
Okay, like, you know, there's usually these informal kind of communities you can join. And then, um, yeah, you just don't wanna be too rotated in one direction or the other. 'Cause we've, we've done it. We've like overdone it on the high potential piece, but then like everybody's kinda making dumb mistake.

**Alessio** [1:09:22]
Right. Yeah, yeah, yeah.

**Drew Houston** [1:09:23]
The, the, the bad mistakes are the ones where like either you're making it multiple times or like these are known unknowns to the industry. But if they're not known known-- if they're like unknown unknowns to your team, then you're d- you, you have a problem.

And then again, if you have too much ex-- if you just only hire external people, like then you're sort of at the mercy. You'll be like whatever random average of whatever culture or practices they bring in can create resentment or like lack of career opportunities.

Um, so it's really about how do you get... You know, it doesn't really matter if it's like exactly fifty/fifty. I don't think about a sort of perfect balance, but you just need to be sort of tending that garden continuously.

**Alessio** [1:09:57]
Awesome, Drew. Just to wrap, do you have any call to actions, like who should come work at Dropbox? Like who should use Dropbox? Anything you want, uh, you wanna tell people?

**Drew Houston** [1:10:07]
Well, I'm super... I mean, today is a super exciting day for, 'cause we just launched Dash for Business. And you know, w- we talked a little bit about the product. It's like universal search, universal ac- access control, a lot of rethinking, sharing for the modern environment.

But you know, what's personally ex- and you know, we could talk about the product, but like the... it's just really exciting for me to like, yeah, this is like the first like most major and most public step we've taken from our kinda Dropbox 1.0 roots, and there's probably a lot of people out there who either like grew up not using Dropbox or were like, "Yeah, I used Dropbox like ten years ago and it was cool, but I don't do that much with it."

So I think there's a lot of new reasons to kinda tune in to what we're doing and, and it's a lot of... it's been a lot of fun to, I think like the, the sort of the AI era has created all these new like paths forward for Dropbox that wouldn't have been here five years ago.

And then, yeah, to the founders, like, you know, hang in there, do some reading, um, and, uh, don't be too stressed about it. So we're pretty lucky to get to do what we do.

**Alessio** [1:11:05]
Yeah. Watch the Pats documentary-

**Drew Houston** [1:11:06]
Keep coming

**Alessio** [1:11:07]
... on Apple TV. The-

**Drew Houston** [1:11:08]
Yeah, Bill Belich. I'm still a Pats fan. Really got into F1, so we're technology partners with McLaren. They're doing super well.

**Alessio** [1:11:15]
So were you a McLaren fan before you were technology partners, or did you become partners because-

**Drew Houston** [1:11:19]
Uh, it sort of like co-evolved, yeah.

**Alessio** [1:11:20]
Yeah.

**Drew Houston** [1:11:21]
Um, I was a fan before him, but I'm like a lot more of a fan now-

**Alessio** [1:11:24]
Yeah

**Drew Houston** [1:11:24]
... as you'd imagine.

**Alessio** [1:11:25]
Awesome. Well, thank you so much for the time, Drew.

**Drew Houston** [1:11:27]
Awesome.

**Alessio** [1:11:27]
This was great. Um-

**Drew Houston** [1:11:28]
Yeah, it's a lot of fun.

**Alessio** [1:11:29]
Yeah.

**Drew Houston** [1:11:29]
Thanks for having me.

---

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