3513: How Dropbox Is Rethinking Work With AI And Dropbox Dash

[music] Welcome back to the Tech Talks daily podcast and today I want [music] to start with a question. How often do you catch yourself drowning in tabs,

files, chats, and random scraps of information that [music] all seem to scatter themselves the very moment you need them or a colleague ask you for one of them. I think most of us live in that

of them. I think most of us live in that digital fog every day and it feels impossible to keep track of anything with any kind of clarity. Well, my guest

today, he spent the last 20 years building products that solve these kind of problems. His name is Josh Clem. He's

the VP of engineering at Dropbox and he's leading the company's AI efforts and the push behind Dropbox Dash. What

is it? What problems does it solve?

Well, we'll talk about all that today.

But he's also going to bring a mix of scale engineering, product curiosity, and a whole heap of hardearned lessons from LinkedIn and Uber. These are the

kind of stories that move from early experience in personalized data products all the way to global outages triggered by free donuts, which is exactly the

kind of chaos that shapes better systems. So, my conversation today will dig into the real meaning of AI fluency, why context beats hype, and how Dropbox

is trying to make work feel lighter with knowledge management that adapts to you?

So, here's my question for you. What

would your day look like if all your work context just surfaced itself without you having to hunt it down? And

while you ponder that question, I think you're perfectly set for today's interview. Before I bring today's guest

interview. Before I bring today's guest on, I just want to give a massive thank you to my friends at Denodo because after visiting over 25 different events

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work harder. But now, let's get today's

work harder. But now, let's get today's guest on. [music]

guest on. [music] So a massive warm welcome to the show, Josh. Can you tell everyone listening a

Josh. Can you tell everyone listening a little about who you are [music] and what you do?

>> Hey everybody, I'm Josh Clem. I am

currently the vice president of engineering at Dropbox. I'm in charge of our AI initiatives including building out a new product called Dropbox Dash

and I've previously been at Uber. I

worked on Uber Eats for quite a number of years and before that I was at LinkedIn. Um, yeah.

LinkedIn. Um, yeah.

>> Awesome. Well, there's so much I want to talk with you about today, especially Dropbox Dash, of course, but I always love to find out a little bit more about my guest origin story. And you mentioned

there you was at Uber and then I think there was LinkedIn. And then when I was doing a little research, I read that uh you were you witnessed some of the main key initiatives that shaped how Uber

underlined architecture worked and how Uber managed to scale the processing of millions of trips to uh a day and operating in over 70 countries. It it

feels phenomenal to be a part of that but especially from the outside looking in. But tell me more about that journey.

in. But tell me more about that journey.

Yeah. So, I was at Uber for almost eight years and I just particularly absolutely love history. I love understanding why

love history. I love understanding why are we doing the things we're doing?

What was sort of the evolution? And

these these companies like Uber just go through tremendous amount of scale. As

an engineering leader, you want to really understand, okay, what were the things that that you know the early team did? What were those key decisions? And

did? What were those key decisions? And

I think it's just very helpful to kind of, you know, write stories like that.

Um, I had written a very similar story at my time at LinkedIn about 10 years ago called a brief history of scaling LinkedIn. And so I thought I'd do a very

LinkedIn. And so I thought I'd do a very similar one at Uber. And of course, I was on the Uber Eat side. And so, you know, I saw a lot of those stories. I

lot really understood how that scaled.

Uh, so a lot of that story that you're describing was really a combination of things that I had to kind of personally look up from even before my time, what things I was personally involved with and really kind of putting together a

nice story overall.

And you know, when you think about kind of scaling as an engine leader, you really want to learn. You want to reflect on some of those decisions. And

a lot of times it's when things don't go right and you learn a lot and you have to really uh update your processes and think about your technology. I'll give

you a quick story. So back in 2017, Uber Eats was just getting going and we had all of our global operations team. They

were trying to make Uber Eatats work and be very very successful in their country or their city. And so our UK team decided, let's do a promotion. They went

and they talked with Crispy Cream Donuts and they decided, you know what? We're

going to hand out free donuts offered on Uber Eats. And not just free donuts, but

Uber Eats. And not just free donuts, but free delivery as well. And Neil, it turns out people absolutely love free.

We ended up getting about a hundred times more traffic during that promotion than we ever were expecting.

All hell broke loose. Everything went

down. The app went down.

Nobody got their free donuts. Everyone

in the UK was incredibly upset. But even

worse is when, you know, the UK had that issue. It actually took down all of Uber

issue. It actually took down all of Uber Eats across the globe. And it really kind of kicked off this this initiative.

We we've got to improve the resiliency of this product. We have to figure out how to make sure you know a local city can do these sort of promotions but at the same time building in resiliency

patterns so that it can't necessarily affect the rest of the world. So we did a lot of both very short-term fixes as well as

think about our underlying architecture and ended up being very very successful.

I don't think we had another outage for at least another three years.

>> What a great story. Absolutely love

that. One of the reasons I wanted to mention it is there's so much hype around AI at the moment and a lot of people forget. I think that when it

people forget. I think that when it comes to disruption around technology, we've been here a few times before from the um arrival of cloud to mobile and

obviously AI now. And I think it's also important to point out that you've been working in AI long before the hype. So

tell me more about your work in AI and and how that would lead you to Dropbox.

>> Absolutely. Yeah. So

I would say my first real exposure to building personalized data products was probably back at LinkedIn. You know they were an early pioneer. They had some

phenomenal data sets and a lot of the product experiences that you see both on LinkedIn and really any social network really came from LinkedIn. Things like

people you may know. Um you know I was part of the profile team. So we would show similar profiles. We would show users skills. a lot of times inferred

users skills. a lot of times inferred skills that you could endorse. Uh later

I worked on this initiative. We were

trying to get more students and universities on the on the platform and we really leaned into data insights. So

better school search. We ended up building our own school rankings. And

one of my personal favorite features was something called notable alumni. And you know, you could go to

alumni. And you know, you could go to any school and we're able to really mine the LinkedIn uh data set and find, hey, these are very successful uh alumni from

these universities. The universities

these universities. The universities actually love this cuz it it it gave them uh a really great list of folks that hey, maybe they should know about and and engage with and actually get

them to come speak with them more at the university and things like that. And so

you really learn, hey, there's a lot of power in building these AI products. And

so then when I went over to Uber Eats, I understood, hey, we we need to really think about bringing in more personal personalized data insights there.

Uh, and I really leaned into investing in our search and discovery team. When

you think about Uber, like Uber rides, when you open up the app, you know where you're going.

>> Yeah. Uber Eats very very different. You

don't necessarily have a particular restaurant in mind. You know, you're hungry. In fact, about I think 20% of

hungry. In fact, about I think 20% of users that open up Uber Eats knew exactly what restaurant they wanted.

Most of them were were open to discovery. They maybe had no idea and

discovery. They maybe had no idea and you could really help them. Maybe they

had a cuisine type. I want pizza. Great.

Here's some options. And so Uber Eats ended up being a very powerful machinelearned product, very similar to how a Spotify might work or a Netflix and all those recommendations that

you're going to see. Uh it was really important for us to to take in all these different insights whether it was the time of day, your past orders, sometimes even the weather. We were taking that

into account to find the right personalized recommendation.

Towards the end of of my experience at Uber, we started to use a lot more with natural language, conversational AI, even large language models, and I started to see how impactful that

technology could be.

And so for me, you know, coming to Dropbox really felt like this natural continuation of everything that I had been building for the last 20 years.

It's AI, but it's grounded in reality.

You're solving universal problems around just information overload. You're trying

to help people get their job, their task done faster. Uh and now we have that

done faster. Uh and now we have that technology that can that matches that ambition.

>> Lovely. And I think we are at a time now if we want fast forward to present day where every business is coming to terms with not just being a tech business but also evolving into an AI business. So

obviously with Dropbox that has continued to grow over the last decade.

So how is Dropbox dealing with this latest shift?

>> Absolutely. I mean it Dropbox recognizes that like like many companies AI is reducing a lot of the busy work and it allows

employees to really free up their focus on what matters. Uh so there's just a lot of excitement going on right now in the company at Dropbox around AI.

We'll do things like hack weeks. We had

one uh earlier this summer and we didn't even necessarily identify AI as the key topic or key theme, but almost 90, you know, 95% or more of the projects that

ended up coming out of the team were all about leveraging AI, leveraging AI tools, trying to figure out how to do things in in just more novel ways. And

that was really encouraging. A lot of those even ideas have been added to our product roadmap and we continue to do this within the company. We highly

encourage a lot of showand tells, demo days, etc. Uh to tap into that that excitement, that curiosity and make sure that other employees know that it is

okay uh to use these types of tools. Now

some of the tools that we do deploy within uh Dropbox things around you know better ways of coding or better ways of trying to build prototypes it it really

does change the nature of the work. The

approach to work is very different.

You're almost having to think about the upfront task of planning what you want.

If I'm an engineer a lot of times it's okay I need to I know what I need to do.

let me just start coding. And the script flips a little bit. It's more let me take a breath. Let me really understand the requirements. Let me really

the requirements. Let me really understand what good looks like when I'm done with this this task. How do I know it worked? And almost defining that

it worked? And almost defining that upfront and then leveraging AI to really help with some of that more busy work.

And so the the you know, you are shifting in how you think about work.

And I think that's actually really exciting.

Um, we've got designers who are mocking up examples of our new products, uh, all with live code that you can test out, even put in front of customers and and

do some user research and get really, really valuable insights overall.

And ultimately, you know, how are we deploying AI at at Dropbox? We are

building a custom solution called Dropbox Dash. Of course, it's for

Dropbox Dash. Of course, it's for external customers, but we are our its number one customer as well.

>> So for people listening hearing about Dropbox Dash for the very first time, how would you describe it? What what

does it bring to them?

>> So Dropbox Dash is really the Dropbox version of 2025.

>> Yeah. When Dropbox started many many years ago, you had all of your files on, you know, your thumb drives or your your, you know, personal PC and it was

very hard to keep everything synced and organized. You know, one file over here and and then it gets it gets lost. And that problem just becomes even

lost. And that problem just becomes even more apparent at work when you're trying to share physical files with one another and one other, you know, different co-workers. Uh, so what does Dropbox

co-workers. Uh, so what does Dropbox Dash do? It recognizes that a lot of the

Dash do? It recognizes that a lot of the files today, whether it's in your personal life or at work, they're all in the cloud or they're all a tab in your

browser. I don't know about you, Neil,

browser. I don't know about you, Neil, but I've got probably about 50 open tabs right now in in my browser, and you're always trying to jump from one place to

the other and trying to find where you left off or where was that file. That's

what Dropbox Dash does. We connect to all of these different thirdparty apps, including a lot of your browser history.

We bring all that in in one place. We

apply AI to autoorganize it. And then we allow you to do extremely effective search. And once you have that, you can

search. And once you have that, you can start doing AI answers. Then you really can start to collaborate with your co-workers in a much more efficient way.

And for people listening that already have a Dropbox account, which I would imagine will be for most people, is this an add-on for them? How does that work?

Is it something else that they they need to subscribe to or is it part of their membership, for example?

>> So, I'd say there's two things here.

One, Dropbox Dash is a standalone product.

>> Yeah, >> it is a completely separate URL. it is a completely separate uh purchase because it provides

that rich experience connecting to these third party apps beyond just your Dropbox content. And so you can go to

Dropbox content. And so you can go to dropbox.com-today

dropbox.com-today we do have a self-s serve option and you can kind of get going right away.

A couple of weeks ago, we did introduce more robust AI features within the core Dropbox app. So, if you do have just

Dropbox app. So, if you do have just your Dropbox files, you absolutely can start doing and leveraging more powerful search. You can start using uh chat

search. You can start using uh chat across your different files. We have

this new feature called stacks, which is a really a smart collection of any kind of content that you can then share with co-workers.

And so the Dropbox experience does have some of those features. But if you want the the full comprehensive AI knowledge management across all of

your cloud apps, that's where you need to go and get Dash.

>> Awesome. And before you came on the podcast, I was doing a little research on your work, especially adding AI into knowledge management. And I was reading

knowledge management. And I was reading that your perspective that fluency with AI rather than hype, how that is the true the true differentiator in your eyes, especially for business success

today. But tell me more about that

today. But tell me more about that perspective and your experiences adding AI into this.

>> There's a lot of hype out there right now, Neil. Um there's we you know you

now, Neil. Um there's we you know you see these different studies that are always saying hey these these executives are greenlighting AI projects

because they feel they need an AI initiative.

>> Yeah.

>> Uh a lot of the customers we've talked to with Dash echo a lot of that same thing. Hey, I keep hearing about AI.

thing. Hey, I keep hearing about AI.

Do you have AI? It's like yes, you know, we we do have AI, but let's let's go more into it because it's really important to understand what exactly are you looking for? What outcomes are you

hoping to accomplish? And I think that's really really critical for executives and and various leaders to understand.

They need to be fluent in this technology. They need to understand

technology. They need to understand what's possible, what's not, what the the security profile is. because I know there's a lot of questions there and there's a lot of buzzwords out there. AI

was the first one. Then you had terms, oh MCP, is that something I need? And

then later it became agents, I want that. And unless you really sort of

that. And unless you really sort of think through these these outcomes and understand what are the what what technology can potentially get you that

you're going to have a potentially failed AI deployment. Um, and you're starting to see that. You know, there was that famous uh article a few months

ago from MIT where 90 they said 95% of AI deployments are failing. Uh McKenzie

was reporting 80 80% of companies aren't seeing tangible ROI from Gen AI. And

there was that really fun article I loved about work slop. I think the uh Harvard Business Review talked about work slop at work and what is that, you

know, and why why what what are what's happening with all these projects that aren't necessarily successful?

Well, again, it goes to oh, we need AI.

Let's roll out a bunch of tools. And if

you don't explain the guardrails, if you don't explain what success looks like, employees might be generating a bunch of work slop. It's lowquality work. They're

work slop. It's lowquality work. They're

not reviewing it. They're not treating it as a first draft and it ends up creating a downstream negative effect where there's a lot of correction. And

so really understanding that AI isn't the problem. It's really how people are

the problem. It's really how people are using it. That's I think going to be

using it. That's I think going to be important overall.

Uh and then again when we when we start to talk about Dash with customers, we do see some of these similar themes when customers say, "Hey, I want to add

a agents to my work." It's like, "Oh, great." You know, describe your use

great." You know, describe your use cases and you get some blank stairs. Uh

you know, I'm not sure. I'm not sure. So

then you really work with them and try to understand um what's possible, come up with some examples, show some examples, etc. And even just

the blank chat bot box that you might see if you go to any of your your favorite chat providers today, that's intimidating.

>> A lot of employees just don't know what to put in there. They need examples.

They really need to understand how it can be used. And I think a lot of that starts from the top. That leaders

really need to be fluent themselves to understand what works and what doesn't. Yeah, I completely agree with

doesn't. Yeah, I completely agree with you there and I've attended 25 tech events around the world this year and AI seems to be the topic of every situation, every event and every brand

is desperately trying to be part of that AI narrative. But I'm curious from your

AI narrative. But I'm curious from your work, what have you learned at Dropbox about AI tools as you build and scale your own AI products?

>> We've learned a lot, especially building Dash.

when you build a tool and you deploy it to your company, that's the best feedback you can get.

>> Uh, a lot of the best products are going to be what's considered, you know, you're dog fooding it. Um, your

company's using it themselves every day, day in and day out. And you get amazing feedback, very, very fast feedback from um, you know, your different employees.

And so, you know, what are we learning?

Well, first AI tools really should get better the more you use it as you put in a a chat or as you interact with the product. If it doesn't work, there

product. If it doesn't work, there should be a feedback loop. That's really

important for companies building AI tools. What is that feedback loop? Am I

tools. What is that feedback loop? Am I

doing anything with both positive or negative outcomes? And am I making that

negative outcomes? And am I making that system better? In our case for for Dash,

system better? In our case for for Dash, if you know somebody has a great chat experience, they hit thumbs up and we get that information, we can we can definitely improve it. Same thing

with thumbs down. Okay, clearly this this answer missed the mark. We have to do a better job. And just, you know, the more you can build feedback loops like

that, the faster your AI tool quality will improve.

Um I mentioned before we've learned a lot about where AI tools do hit limits.

Uh that same thing that empty chat box very intimidating. What do I need to do?

very intimidating. What do I need to do?

Uh for Dash we are moving more towards proactive suggestions. The chat box will

proactive suggestions. The chat box will always be there but what are suggested searches? What are suggested chats that

searches? What are suggested chats that you could do? Um, and the more you can move AI into kind of your normal work mode where it isn't just the chat b box,

I think you're going to see a lot more success. And same thing where can AI

success. And same thing where can AI work in the background. I mentioned

before about Dash. Dash is connecting to all

about Dash. Dash is connecting to all these third party apps bringing all this content in. Well, we want to

content in. Well, we want to autoorganize that information. And we

want to sort of create pockets of, you know, these are topics or this might be your working set. That is AI also. And

just being able to surface, hey, here's exactly what you're working on where you left off is incredibly empowerful. It

doesn't require employees to have to type anything. It's just there and it

type anything. It's just there and it just works.

Another thing we've learned is uh specifically from customers is there's still a lot of skepticism out there around

how my data is used in AI. Oh, are you training off my data? Um are you sending it off prem? And obviously for Dropbox

and and as we're building Dropbox Dash, privacy and security is a key principle that we're building within. But it's

also important to explain that and be very very transparent with uh the folks that you're deploying these these um systems to. There should be great help

systems to. There should be great help center articles explaining how you're using the information. But even within the product, I think it's important to highlight that hey, your data is safe.

It's secure. A lot of the the techniques we're doing to bring in your more personalized work context is done in a very very secure way.

Another lesson we've learned, uh, a lot of AI today is what I would call single player. Um, think about your favorite

player. Um, think about your favorite chatbot, whatever it's a chat GPT or Claude. It's pretty much you and this

Claude. It's pretty much you and this chatbot. You're typing in some question

chatbot. You're typing in some question and you get back an answer. There's

really no concept of you and a team.

Your team's not seeing that answer.

You're not really getting any kind of collective wisdom or collective intelligence.

And we're we're starting to think more and more at Dropbox as we're building Dash is how do we continue to make

these AI products more multiplayer? Can

I collaborate more effectively? Can I

share uh project updates with a group, my project team? um can I see potentially other example agents or chats and just make it feel a little bit

more like AI is part of your team and not just oh it's my kind of assistant. I

think that's going to be a really interesting lesson and and potential trend going forward.

And then lastly, the most important thing we're learning is context is king. What do I mean by that?

Um, AI and these large language models are incredibly incredibly knowledgeable, but they're knowledgeable about stuff you don't need at work.

>> Yeah.

>> I don't need to figure out a recipe for a omelette. You know, I want to understand

omelette. You know, I want to understand how to draft a really amazing strategy document uh using all my kind of work context. And so really providing work

context. And so really providing work context using the the more proprietary private information that works have is a

huge huge frontier um that we're looking into and building towards. But I think a lot of companies are going to start to look into more. And I'm glad you mentioned that because a few minutes ago

we were talking about the dangers of AI slop and work slop. And I think we've all seen examples of that. But on the flip side of of this, it doesn't need to be that way. And as you said, context is

king. So tell me more about why

king. So tell me more about why contextaware AI, how you think that could be the next step in the future of work and how it could enhance all your content and and ultimately achieve

clarity because there's a a feel-good story here too, isn't it? It's not all doom and gloom.

>> It's not all doom and gloom. And these

these large language models are unbelievably powerful if you can provide it the right context.

>> And for me it really starts with you have to kind of understand how large language models fail. And there's

actually a few very distinct ways. And

once you understand that then you can know how to provide and and counteract some of those failure modes. So let me go through a few of those right now.

Um the first thing that large language models fail is if you give them too much information they can get overwhelmed with with knowledge. Uh this is a

concept called context rot and our brains sort of fall into the same thing.

There's these various psychological studies, these like theoretical use cases. Uh, you know, for example, Neil,

cases. Uh, you know, for example, Neil, if I said, "All right, and then uh name in the next 10 seconds, name as many red things as you can as you can do." You're

probably going to lock up and you're probably going to say, "That's a lot of stuff." Versus if I said, "Tell me all

stuff." Versus if I said, "Tell me all the red things in your fridge."

>> Yes.

>> You're immediately going to be able to list a ton, a lot more.

Large language models are very very similar in that if I dumped every work document I ever had and said, "All right, large language model, write me my

help me write my strategy." It doesn't know where to start. It's going to be completely overwhelmed. You have to be

completely overwhelmed. You have to be able to provide a much more narrow specific uh set of documents or context and it's going to it's going to be a lot

more successful. So that's something

more successful. So that's something called context rot. It gets lost when it has too much context. [snorts] A second failure mode

is the classic it hallucinates. And what

does that mean? Well, if it doesn't know the answer, it'll just make something up. Very, very dangerous. Overall, you

up. Very, very dangerous. Overall, you

have to ensure you're not overly trusting the the output. Like I said before, do treat these as a first draft.

Never turn in your first draft. Uh, I

don't know about you in school, but when you write an essay and you wrote your first draft, you didn't turn it in. You

made sure you you you did a couple revs on it. Um, and yet people are still

on it. Um, and yet people are still turning their first draft, and that ends up being work slop.

>> Um, now you again, if you provide the right facts, grounded facts to these large language models, it will use those facts. And when it uses those facts, it

facts. And when it uses those facts, it doesn't hallucinate.

Uh, third failure mode. I like to say large language models are gullible.

Whatever you tell it, whatever you provide it, it kind of just repeats.

It'll parrot back anything you say. And

so if you're giving it the right information, fantastic. It's going to

information, fantastic. It's going to tell you that. But if you give it the wrong information, it'll also tell you the wrong information.

Uh, and this this is actually quite problematic in the security use case.

This is where you you might see stories where LLMs get tricked to reveal um and exfiltrate people's data. It's because

they can kind of get tricked. You can

prompt it to, hey, why don't you go do this other thing and it will. Um and so putting in the kind of safeguards around that is going to be really really

important overall. Uh so those are just

important overall. Uh so those are just a few ways that they fail. And the

answer is this term that's been popularized as context engineering. So

I'm going to go search and retrieve the right context in a very narrow way and then provide it to a language model and it's going to be far more accurate.

It's going to be far more reliable and frankly it's going to be magic. To your

point earlier, it's not all work slop.

It can be absolutely magic. And I think that's that's kind of this the step of kind of this contextaware AI. It's it's

almost like if uh you know you don't you're not going to ask Albert Einstein to be uh the a magician at your kid's party, right? They they they you know,

party, right? They they they you know, these language models are incredibly smart, but they don't have everything.

They don't have sort of your work context. And with that, I'd love to kind

context. And with that, I'd love to kind of actually walk through how Dash works a little bit more on the technical side, if you don't mind, Neil. Sure, go for it.

>> So, I mentioned before Dropbox Dash will connect to different third party apps.

We'll go and retrieve various documents or images, media from all these different sources. This could be, you

different sources. This could be, you know, different SAS apps. This could be your HR apps. It could be um Google doc, you know, Google Drive. It could be, you know, your your project management

tickets, etc. and we'll bring that all together and we do something called content understanding on this material.

And what that is is, you know, if I get a let's say a a Google doc, well, that's a bunch of text. That's easy enough to understand. But what if I get an image?

understand. But what if I get an image?

How do I extract any sort of relevant information from that? Or what if I get a PDF? PDFs are images, there's text,

a PDF? PDFs are images, there's text, there's sort of a combination. and we we do a bunch of work to really pull out all of the the right um information from

things like PDFs or imagine it's a video.

Think about for a moment that uh scene from Jurassic Park where, you know, they they saw the dinosaurs for the first time and they sort of turn to the side

and they turn take off their sunglasses and they have this look of dismay and awe as they see the dinosaurs for the first time. What if that's a video that

first time. What if that's a video that you want to find later? How would you do that? How would you retrieve that

that? How would you retrieve that information? Well, we use different uh

information? Well, we use different uh multimodal large language models to extract what's happening in that scene.

So once you start to build all the the understanding of these different documents, then we take it a step further and we build a knowledge graph and we start to

connect relevant information across apps.

So for example, you know, you're working on a project. There's people involved.

There might be a document. There might

be a meeting transcript. All of that you could form as a a almost a an insight, a bundle of knowledge. Then we go ahead and we will index all that information.

So all the content has been understood, it's been indexed, and we've even created these knowledge bundles. And

that's what makes Dash so powerful that we're able to do phenomenal search, phenomenal retrieval.

Once you do that, chat becomes far more accurate. Agentic work becomes far far

accurate. Agentic work becomes far far more accurate.

And this is the kind of the key. This is why contextaware AI is really the future of how we think about AI, especially at work.

>> Feels like an incredibly exciting time for you there. And I appreciate you've already shared so much with us today, but trying to get a few teasers out of you. Are there any other upcoming

you. Are there any other upcoming product announcements that might demonstrate how Dropbox is continuing to evolve beyond a file storage company and

also the the grand vision for AI at Dropbox? Any teasers you can leave us

Dropbox? Any teasers you can leave us with there?

>> Yeah, absolutely. We are integrating a lot of the AI features that Dash teams built into Dropbox, effectively making the platform smarter

and extending its capabilities from just file storage to more understanding your team's content. So you have more you faster

content. So you have more you faster access to your information. You have

smarter search and of course you have the ability to act on content without switching tools.

And like I said be uh a little bit before, Dash is also now available as a self-s serve option. So if you're a small team out there, you can go and sign up and start using using Dash in

minutes rather than just going through the sales team.

In general, you know, Dropbox, we do want to create a world where we are the most intuitive place for work, where content is easy to find and teams

can focus on the bigger picture items rather than that busy work. And that's

really the key on where we think AI can be incredibly incredibly impactful.

>> And for anybody listening that would like to stay in touch with all the kind of announcements that we're going to be seeing over the months ahead and equally get uh find out not just about Dropbox

but Dropbox Dash. Any uh websites you just want to mention one more time just so people can go and and check those out and keep up to speed with everything.

>> Absolutely. So, if you are interested in Dash, head over to dropbox.comdash.

[sighs] Of course, you can follow Dropbox and Dash on Instagram, LinkedIn, and X. The

handle is Dropbox. And you can find me.

I'm on LinkedIn and X. And I do provide quite a bit of updates as well.

>> Oh, okay. Okay. Well, I will have links to everything you mentioned there, including your ex and LinkedIn channel.

And I think this year in particular, we've seen more and more work slop, but today it was great hearing about the cause of it, the context rot that you mentioned there, but equally the flip

side, where we're heading, what we can do now, and there's so many great things coming. It' be interesting to get you

coming. It' be interesting to get you back on next year in 2026 and and how to hopefully see how we're moving beyond these things and really unlocking new opportunities for increased productivity

and better working etc. But Josh, thank you so much for shining a light on this today.

>> Yeah, thanks Neil.

>> For me, I think today's conversation was one of those that leave you looking for your own workflow in a slightly new way.

And Josh broke down why the massive reality of information overload and digital clutter, but showing how contextaware AI can take the sting out

of these things. And we've all seen work slop, AI slop, and context rock. And I

think it reflects the honest tension that many teams are feeling right now.

But thankfully, Josh offered a path forward. One where smarter retrieval,

forward. One where smarter retrieval, tighter context, and steady feedback loops create something that feels useful. rather than overwhelming. And

useful. rather than overwhelming. And

Dropbox Dash seems to sit at the right intersection of everything we're talking about here. It's treating AI as a

about here. It's treating AI as a practical tool, not a spectacle. And I

think Josh's thinking on AI fluency will also give leaders something very real to work with. And I appreciated just how

work with. And I appreciated just how much he shared about the lessons inside Dropbox and the shift from single player AI to multiplayer collaboration of

sorts. But I'd love to know what stood

sorts. But I'd love to know what stood out for you in this conversation. Does

contextaware AI feel like the missing piece in your own workflow or do you see other changes coming first? Love to hear your thoughts. techtalks network.com and

your thoughts. techtalks network.com and you can also send me a DM on LinkedIn X Instagram just neilcues. But that is it for today. So thank you as always for

for today. So thank you as always for listening and I'll return again tomorrow with another guest. Bye for now. [music]

>> [music]