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

[00:00:03] Welcome back to the Tech Talks Daily Podcast. And today, I want to start with a question. How often do you catch yourself drowning in tabs, files, chats, and random scraps of information that all seem to scatter themselves the very moment you need them or a colleague asks you for one 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

[00:00:33] last 20 years building products that solve these kinds 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 hard-earned lessons from LinkedIn and Uber. These are the kind of stories that

[00:01:03] 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.

[00:01:29] 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 on, I just want to give a massive thank you to my friends at Donodo. Because after visiting over 25 different events in 2025, one of the phrases I keep hearing

[00:01:58] is no data, no AI, and agentic AI simply needs better data. Now agentic AI is here, but it only works when the data behind it is complete, governed, and in real time. And this is one of the areas that Donodo helps. Because Donodo gives you a logical data foundation that accelerates AI, boosts Lakehouse performance, and turns your information into reusable data products, and for every team.

[00:02:27] So CIOs, architects, and business owners each get the data that they need instantly. And their global partners help you get up and running faster than ever. So if you want AI that doesn't hallucinate, but actually delivers real business outcomes, visit Donodo.com and start making your data work harder. But now, let's get today's guest on.

[00:02:53] So a massive warm welcome to the show, Josh. Can you tell everyone listening a little about who you are 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,

[00:03:22] I was at LinkedIn. 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's origin story. And you mentioned there, you was at Uber and then I think there was LinkedIn. And when I was doing a little research, I read that you witnessed some of the main key initiatives that shaped how Uber

[00:03:45] underlined architecture worked and how Uber managed to scale the processing of millions of trips a day and operating in over 70 countries. It feels phenomenal to be a part of that, but especially from the outside looking 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 are we doing the things we're doing? What was sort of the evolution?

[00:04:14] And these companies like Uber just go through a tremendous amount of scale. As an engineering leader, you want to really understand, okay, what were the things that the early team did? What were those key decisions? And I think it's just very helpful to kind of, you know, write stories like that. 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

[00:04:39] similar one at Uber. And of course I was on the Uber Eats side. And so, you know, I saw a lot of those stories. I really understood how that scaled. 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 engineering leader,

[00:05:06] 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 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 Eats work and be very,

[00:05:36] very successful in their country or their city. And so our UK team decided, let's do a promotion. They went in, they talked with Krispy Kreme 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 free delivery as well. And Neil, it turns out people absolutely love free. We ended up getting about a hundred times

[00:06:03] 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 Eats across the globe. And it really kind of kicked off this, this initiative. We've got to improve

[00:06:29] the resiliency of this product. We have to figure out how to make sure, you know, local city can do these sorts 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.

[00:06:57] What a great story. Absolutely love that. And one of the reasons I wanted to mention it is there's so much hype around AI at the moment. A lot of people forget. I think that when it comes to disruption around technology, we've been here a few times before from the 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 how that

[00:07:26] 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,

[00:07:52] 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 skills that you could endorse. Later, I worked on this initiative. We were trying to get more students and universities 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

[00:08:17] personal favorite features was something called notable alumni. And, you know, you can go to any school and we were able to really mine the LinkedIn data set and find, hey, these are very successful alumni from these universities. The universities actually love this because it gave them a really great list of folks that, hey, maybe they should know about and engage with and actually get them to

[00:08:43] 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 need to really think about bringing in more personalized data insights. There. And I really leaned into investing in our search and discovery team. When you think about

[00:09:09] Uber, like Uber rides, when you open up the app, you know where you're going. 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 users that open up Uber Eats knew exactly what restaurant they wanted. Most of them were open to discovery. They maybe had no idea and you could really help them. Maybe they had a cuisine

[00:09:34] type. I want pizza. Great. Here's some options. And so Uber Eats ended up being a very powerful machine learned product, very similar to how a Spotify might work or a Netflix and all those recommendations that you're going to see. It was really important for us 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 my

[00:10:04] experience at Uber Eats, 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

[00:10:29] universal problems around just information overload. You're trying to help people get their job, their task done faster. And now we have that technology that matches that ambition. I love it. 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

[00:10:58] how is Dropbox dealing with this latest shift? Absolutely. I mean, Dropbox recognizes that 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. 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.

[00:11:25] We had one 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 just more novel ways. And that was really encouraging. A lot of those even ideas have been added to our product

[00:11:52] roadmap. And we continue to do this within the company. We highly encourage a lot of show and tells, demo days, et cetera, to tap into that excitement, that curiosity, and make sure that other employees know that it is okay to use these types of tools. Now, some of the tools that we do deploy within Dropbox, things around, you know, better ways of coding or better ways of

[00:12:18] trying to build prototypes. 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 needed, 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 understand what

[00:12:47] good looks like when I'm done with this, this task. How do I know it worked? And almost defining that upfront and then leveraging AI to really help with some of that more busy work. And so the, you know, you are shifting in how you think about work. And I think that's actually really exciting. We've got designers who are mocking up examples of our new products, all with live code that you can

[00:13:15] test out, even put in front of customers and do some user research and get really, really valuable insights overall. And ultimately, you know, how are we deploying AI at Dropbox? We are building a custom solution called Dropbox Dash. Of course, it's for external customers, but we are our, it's number one customer as well. So for people listening, hearing about Dropbox Dash for the very first time, how would you describe it? What does it bring to them?

[00:13:45] So Dropbox Dash is really the Dropbox version of 2025. 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,

[00:14:10] and then it gets, it gets lost. And that problem just becomes even more apparent at work when you're trying to share physical files with one other and one other in a different coworkers. Uh, so what does Dropbox 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,

[00:14:34] 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 third-party apps, including a lot of your browser history. We bring all that in, in one place. We apply AI to auto

[00:15:00] organize it. And then we allow you to do extremely effective search. And once you have that, you can start doing AI answers and you really can start to collaborate with your coworkers 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 this something else that they, they need to subscribe to or is it part of their membership, for example?

[00:15:28] 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 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.com slash dash today. We do have a self-serve option and you can kind of get going

[00:15:56] 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 your Dropbox files, you absolutely can start doing and leveraging more powerful search. You can start using chat across your different files. We have this new feature called

[00:16:19] Stacks, which is a really a smart collection of any kind of content that you can then share with coworkers. 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,

[00:16:49] especially adding AI into knowledge management. And I was reading that your perspective that fluency with AI rather than hype, how that is the true differentiator in your eyes, especially for business success today. But tell me more about that perspective and your experiences adding AI into this. A lot of hype out there right now, Neil. There's, you know, you see these different studies

[00:17:16] that are always saying, Hey, these, these executives are greenlighting AI projects because they feel they need an AI initiative. A lot of the customers we've talked to with Dash echo a lot of that same 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

[00:17:44] critical for executives and various leaders to understand they need to be fluent in this technology. They need to understand what's possible. What's not what does the security profile is. 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.

[00:18:09] I want 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. Um, McKinsey was reporting

[00:18:36] 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, 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.

[00:19:03] 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 low quality 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

[00:19:29] are 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 cases and you get some blank stares. Uh, you know, I'm not sure. I'm not sure. So then you really work with them and

[00:19:55] try to understand, um, what's possible, come up with some examples, show some examples, et cetera. 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

[00:20:23] from the top that leaders really need to be fluent themselves to understand what works and what 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 work, what have you learned at

[00:20:47] Dropbox about AI tools as you build and scale your own AI products? We've learned a lot, especially building dash. And when you build a tool and you deploy it to your company, that's the best feedback you can get. A lot of the best products are going to be what's considered your, you know, your dog food in

[00:21:12] it. Um, your company's using it and 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 chat or as you interact with the product, if it doesn't work, there should be a feedback loop. That's really important for

[00:21:41] companies building AI tools. What is that feedback loop? Am I doing anything with both positive or negative outcomes? And am I making that 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

[00:22:06] 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 that same thing, that empty chat box, very intimidating. What do I need to do? Uh, for dash,

[00:22:31] we are moving more towards proactive suggestions. The chat box will always be there, but what are suggested 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 box box. I think you're going to see a lot more success and same thing where can AI work in the background. I mentioned before about dash dash is

[00:23:01] connecting to all of these third-party apps, bringing all this content in. Well, we want to auto-organize that information. 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 powerful. It doesn't require employees to have to type anything.

[00:23:26] 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

[00:23:51] 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 center articles explaining how are you 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

[00:24:17] 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 chat bot, whatever it's a chat GPT or Claude. It's pretty much you and this chat bot. You're typing in some question and you get back an answer. There's really no concept of you in a team.

[00:24:48] 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?

[00:25:10] Can I share a project updates with a group, my project team? Um, can I see potentially other example agents or 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 potential trend going forward. And then lastly, the most important thing we're learning is

[00:25:39] context is King. What do I mean by that? Um, AI and these large language models are incredibly, 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 omelet. You know, I want to understand how to

[00:26:01] draft a really amazing strategy document, uh, using all my kind of work context. And so really providing where 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,

[00:26:28] 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 context aware AI, how you think that could be the next step in the future of work and how it could enhance all your content and ultimately achieve clarity because there's a feel good story here too, isn't it? It's not all doom and gloom. It's not all doom and gloom.

[00:26:56] Um, 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

[00:27:21] 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. I, there's these various psychological studies, these like theoretical use cases. Uh, you know, for example, Neil, if I said, all right, and then a name,

[00:27:51] 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, well, that's a lot of 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

[00:28:17] 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 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 called context rot. It gets lost when it has too much context.

[00:28:42] Uh, 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 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

[00:29:11] an essay and you wrote your first draft, you didn't turn it in, you made sure you, you, you did a couple of revs 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 doesn't hallucinate a third failure

[00:29:35] 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 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

[00:30:00] in the security use case. This is where you, you might see stories where LMs 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 a few ways

[00:30:26] 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 light 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

[00:30:53] absolutely magic. And I think that's, that's kind of the, this, the step of kind of this context aware 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, they, you know, these language models are incredibly smart, but they don't have everything. They don't have sort of your word context. And with that, I'd love to kind of actually walk through how dash works a little bit

[00:31:22] more on the technical side, if you don't mind, Neil. Sure. 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 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,

[00:31:48] your, your project management tickets, et cetera. 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 Google doc, well, that's a bunch of texts that's easy enough to understand, but what if I get an image, how do I extract any sort of relevant information from that?

[00:32:11] Or what if I get 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 right information from things like PDFs or imagine it's a video. Think about for a moment that a scene from Jurassic park where,

[00:32:38] 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 you want to find later? How would you do that? How would you retrieve that information? Well, we use different, uh, multimodal large language

[00:33:05] 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

[00:33:31] 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,

[00:33:57] chat becomes far more accurate. Agentic work becomes far, far more accurate. And this is the kind of the key. This is why context aware AI is really the future of how we think about AI, especially at work. It 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 product

[00:34:26] announcements that might demonstrate how Dropbox is continuing to evolve beyond a file storage company and also the grand vision for AI at Dropbox? Any teasers you can leave us with there? Yeah, absolutely. We are integrating a lot of the AI features, the dash teams built into Dropbox, effectively making the platform smarter and extending its capabilities from just file storage

[00:34:56] to more understanding your team's content. So you have more, you have 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 a little bit before, Dash is also now available as a self-serve option. So if you're a small team out there, you can go and sign up and start 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

[00:35:26] we are the most intuitive place for work, where content's 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, 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,

[00:35:52] and equally get, find out not just about Dropbox, but Dropbox Dash. Any websites you just want to mention one more time, just so people can go in and check those out and keep up to speed with everything? Absolutely. So if you are interested in Dash, head over to dropbox.com slash Dash. Of course, you can follow Dropbox and Dash on Instagram, LinkedIn, and X. The handle is at Dropbox,

[00:36:20] and you can find me. I'm on LinkedIn and X, and I do provide quite a bit of updates as well. Oh, okay. Well, I will have links to everything you mentioned there, including your X and LinkedIn channel. And I think this year in particular, we've seen more and more work slog, 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'd be

[00:36:49] interesting to get you back on next year in 2026 and hopefully see how we're moving beyond these things and really unlocking new opportunities for increased productivity and better working, et cetera. 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

[00:37:17] information overload and digital clutter, but showing how context-aware AI can take the sting out of these things. And we've all seen work slop, AI slop, and context rot. 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, tighter context, and steady feedback loops create something

[00:37:42] that feels 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 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 much he shared about the lessons inside Dropbox and the shift from

[00:38:07] single-player AI to multiplayer collaboration of sorts. But I'd love to know what stood out for you in this conversation. Does context-aware AI feel like the missing piece in your own workflow? Or do you see other changes coming first? Love to hear your thoughts. techtalksnetwork.com. And you can also send me a DM on LinkedIn X Instagram, just at neilchughes. But that is it for today. So thank you as always for listening.

[00:38:34] And I'll return again tomorrow with another guest. Bye for now.