Listen on: Apple Podcasts · Spotify
Welcome to the first episode of the Citrix AI Hotsheet, a monthly podcast from Citrix futurist Brian Madden and account technology strategist Dave Brear. The show is for everyone working on AI in real enterprises — the banks, hospitals, manufacturers, and regulated environments where you can’t just vibe-code your way to a new system on Tuesday.
In this episode we introduce ourselves and the show, then dig into two topics. First, Brian argues that AI is going to enter the enterprise by using the same desktops and applications human workers already use — not by rebuilding everything for agents. He walks through where computer-using agents are today, why they’re slow (screenshots), and the recent research that points to a much faster future. Then Dave introduces “context vaults” — what most people call a second brain — and why this quiet practice is already changing how knowledge workers work, even though most enterprises can’t see it. We close with our own experiment of connecting two second brains over MCP.
Links mentioned
OSWorld benchmark — measures how good AI is at operating a computer
arXiv paper on UIA-based agent navigation — ~80% fewer tokens than screenshots vs. using Windows UI Automation semantic structure
Andrej Karpathy on personal context vaults — original X post · companion gist
Transcript
Brian Madden (00:01): Hello and welcome to the Citrix AI Hotsheet. My name is Brian Madden and I am a futurist at Citrix.
Dave Brear (00:10): And my name’s Dave Brear. I’m an account technology strategist, also at Citrix.
Brian Madden (00:14): Dave and I both have decades of experience with Citrix in large enterprise environments. We’re also both what you might refer to as AI enthusiasts, and we talk quite a bit at work about how AI is entering the work world, specifically how it’s entering our customers. There’s a lot of talk about AI — it’s gonna take everyone’s job, you can vibe code these new applications tomorrow. Our customers are banks and hospitals and manufacturing. You’re not gonna vibe code a new air traffic control system with AI, at least not anytime soon. We sort of realized there aren’t really podcast conversations around what’s really happening with AI in the enterprise, because of course AI is being marketed direct to end-user consumers. Individual workers are using AI, they’re using ChatGPT, they’re using it in very powerful ways. Now you hear things like AI can use computers — how does that all work? That’s what we’re gonna try to unpack on this show, in this series we’re calling the AI Hotsheet.
Dave Brear (01:30): And the aim really is to take what’s happening on the AI frontier, distill it down, and play back how it’s relevant to the enterprise.
Brian Madden (01:38): Yeah, we want to make you sound smart in short, quick takes. With that, let’s jump right in. We have three topics this week. The first one’s one that I picked, then we’ve got one Dave picked, and then we’ll get into it from there.
The one I want to jump into immediately — to be honest, this is the reason why I joined Citrix in the first place. I have this thesis that the way AI is going to enter the enterprise is not from the top down, like big projects where we’re going to redesign some system with AI. Yes, that’s happening, but those are big and slow and traditional — gunky enterprise IT styles of happening. But there’s this other story that people aren’t talking about as much, which is that individual workers are using AI. You’re using ChatGPT and Claude a lot of the time, on personal subscriptions. They’re using it more and more, right? In the old days, it was just a fancy Google. Now people are starting to load it up with context and notes and all these kinds of things, and it’s being used more and more. A lot of times companies don’t even really understand how it’s being used or what their employees are doing.
My viewpoint is, that’s actually the important area to look at. And the reason for that is, part of the way individual workers are using AI — those AI tools can use computers now. There’s literally a thing called computer-using agents, CUA, where the AI can operate a desktop computer or a web browser. That to me is really interesting, because if you look at Citrix, our whole history has been around taking existing enterprise applications and wrapping them in a modern delivery and security layer and delivering it out to customers. That’s why enterprises use us. Back in the nineties, it was, you don’t have to rewrite your enterprise apps for web apps — you can just put them on Citrix, then you get the benefits of running it anywhere and all that kind of stuff.
Within enterprises, especially within our big traditional customers, they have these enterprise systems that have decades of compliance and regulation and process and workflow and data in them. There’s regulation, you can’t just change these things. Literally, you can’t — the government won’t let you change regulated applications. So what’s AI gonna do? It’s not changing government regulations so you can vibe code a new drug approval system. I think AI is going to actually use the same applications that human workers are using, in ways very much like the way human workers operate.
I kind of like this because you don’t have to change your applications if you’re doing this. My mental picture is that AI workers are kind of like human workers. I don’t mean literally, but if they can use a computer system, they have logins, user profiles, governance, user identity, all these kinds of things. If I have an existing process and I want AI to start to do that process, I don’t rebuild all my apps for the AI — I just have my AI use that process. I like that because I don’t have to change anything. Every company has some guy in the basement who has one job, doing one thing in one app, and they don’t have to change that process whether the worker is human or AI. To me, this is the logical pathway of how AI enters the workplace. Tell me I’m crazy, Dave.
Dave Brear (05:39): No, absolutely not. This is an evolving space as well. My personal experience of computer-using agents is very limited, and it was also very unsatisfying — it was extremely slow. It seemed like it was taking minutes between each click. Also, the environment they’re operating in is not the environment where I’m logged in, where I have my credentials sorted out. It was a really poor experience.
Brian Madden (06:06): I’m with you on that, because I had this idea like two years ago — oh my gosh, this is how you get AI in the enterprise. Don’t change your enterprise for the AI, change the AI for your enterprise. And then when computer-using agents came out, they were horrible. Part of it was just bad AI. There’s benchmarks — I’ll put it in the show notes — one’s called OSWorld, and it tracks how good AI is at operating a computer. Today’s AI now exceeds the median human. So AI not knowing how to use a computer, that’s not a thing anymore.
What is the thing is what you said, Dave — it’s so slow. And the reason it’s slow is the way these things actually work. The AI doesn’t run on your computer. When you use Claude Desktop or ChatGPT, the desktop app is just a thin client. It’s sending all your commands into the cloud, all the AI braininess is happening in the cloud, and then if it needs to use a computer — even if it’s your local computer — the AI just asks your computer, hey, give me a screenshot. It looks at the screenshot and says, you want to click here, and it sends back an X, Y coordinate, click there, then send me another screenshot. It’s a series of screenshots, and those screenshots are really slow for AI to process. It’s the same kind of processing as when AI looks at a picture and figures out, this is a horse, this is a whatever. It’s just taking a picture of your screen and thinking, hmm, probably I want to click here, let’s see. Even that takes five, ten, twenty seconds to process. It’s expensive in terms of tokens. It’s just very slow. Something that takes 15 seconds for a human to do takes AI five minutes.
That caused a lot of people to be like, Brian, you’re absurd to think AI is going to use a computer. And I’m like, it’s going to get better. But the other thing too, to your point — the AI is running in the cloud, not on the desktop being used. When AI fires up a computer or fires up a browser, it’s actually not the corporate desktop. We have corporate desktops that we manage, whether it’s with Citrix or whatever, and you’ve got your profiles and lockdown and policies and all this stuff. But when you ask your AI to use a computer, it’s some random VM in the cloud, or it’s a random worker’s desktop, and it’s not that controlled corporate environment. So there needs to be a way for the AI to connect into that corporate environment.
In preparation for the show, I was doing some research. A lot of progress is being made on these computer-using agents to make them more efficient. There’s a paper that was just published a couple of weeks ago on arXiv — I’ll put the link in the show notes — where they’re saying, hey, there’s a better way than just screenshots. To back up: AI can use web browsers also, and when AI initially used web browsers, it was screenshots. But then some smart people, not us, realized there’s a W3C web standard for website accessibility — so everything on a webpage is marked and has IDs and all these kinds of things. Let’s just send that semantic structure for the page to the AI, and then it can process it way faster — it doesn’t actually have to do a screenshot deconstruction. It can navigate very quickly. You may have noticed, if you were using AI that uses browsers, in the past six months or so they got way faster. It’s because they stopped using screenshots.
Fun fact: they still use screenshots as a fallback for video and canvas regions, and for CAPTCHAs —
Dave Brear (09:54): Yeah.
Brian Madden (09:57): — which is why CAPTCHAs now are like, drag a slider, drag the tree into the park, and you have to do it within less than two seconds or whatever, because AI can’t process a screenshot that fast. AI browser use has more or less been solved because they’re not sending screenshots. Well, this paper I was just mentioning is taking that same approach. These researchers were using — there’s some instrumentation in Windows, have you heard of the UI Automation framework, UIA they call it?
Dave Brear (10:27): Yeah, yeah.
Brian Madden (10:28): This is like what RPA and other things use. It’s a way to programmatically allow a software process to process interactive screen elements on the computer. Now, caveat caveat: of course it was only for new applications written this way. There’s always going to be old ones, and again, there’s always going to be traditional applications — we work at Citrix, that’s kind of a big part of why we exist. But my point is, we can see a future moving forward where AI computer-using agents are not just a series of screenshots, but rather where the computer they’re connecting to can send some semantic information to help the AI operate faster and not burn all those tokens. Obviously AI vendors are trying to do that, because everyone knows we’re in a token-constrained environment — GPUs are scarce, power, all that kind of thing. The more efficient AI vendors can make things, the better.
In my mind, you take that, you figure out how to have those AIs then connect into your corporate-managed environment — not some random computer in the cloud — and I think for these reasons, the more plausible path forward for AI entering the enterprise, at least in terms of end-user computing and working, is the AI is going to use the existing desktops and the existing applications that the human workers do. It’ll have its own login ID, we hope. Yeah, give it more restrictions, all those kinds of things. I don’t want to be blamed when my AI goes nuts. I want to have it so locked down. I want my AI to access my Outlook read-only mode, please.
Dave Brear (11:56): I think that’s definitely necessary.
Brian Madden (12:19): So anyway, what I’m bringing is, I think AI is literally going to use the computers and the browsers and the existing applications that humans do. And I think that’s a pathway forward in the enterprise, because enterprises spent 30 years building their desktops and applications and browsers and processes and workflows and supports and training and all the things around that. Just let AI use that. So that’s kind of — I don’t know, this is my vision of where I think things are going.
Dave Brear (12:49): I totally agree. Like I said, my hands up, it’s very slow, it needs to improve. What I like about this field is that what I thought a week ago often turns out to be incorrect and out of date. The progress is coming really quickly. The fact that that constraint of needing to require screenshots to do everything — I think it just shows the direction we’re going in and the speed of travel. It’s an interesting time.
Brian Madden (13:16): Yeah. That paper I just mentioned that talks about that as a technique — by the way, that technique uses 80% fewer tokens to process screen actions than looking at just screenshots. That paper came out literally two weeks ago. I only learned about it in preparation for this show. So yeah, that’s what I got. Dave, what have you brought for a topic to talk about today?
Dave Brear (13:40): It’s really interesting actually. I think it segues nicely from what you’re talking about, in that a lot of the enterprise AI conversation these days is about doing the same work but doing it faster — automating it, having a computer act on your behalf. But I think there’s another way AI can and is being used that’s talked about a lot less, where it’s used to deepen thinking and improve the quality of output. A lot of people are already working in this way — you just don’t know about it in the typical enterprise.
Brian Madden (14:16): I want to be very clear, let’s not even be cagey: Dave and I are working this way. So I’m on board with what Dave’s talking about, because this is how I work every day and how Dave does too. So pay attention, this is important. This is not the future — what I was talking about was the future. What Dave’s talking about is literally how he and I work today.
Dave Brear (14:20): Yeah. So let me set the frame this way. Computer-using agents, automating workflows, removing a human out of that — in my mind, that is workload compression. It’s making things less effort, it’s making them quicker, and freeing humans up to do other things. There are certain workflows that cannot be compressed, though. These workflows are the ones where human thinking is needed, where you want to ideate, where you want to produce something based on those ideas. If you try and compress that, I think this is where AI gets a bad name with AI slop — we’re taking very little input and asking an AI to produce a lot of output. It fills things in, it makes things up, it hallucinates, it’s terrible. And you can —
Brian Madden (15:29): It’s tap dancing. It’s drawn out, like, keep talking. I don’t know what to say. Yeah, dude, I never thought of that — how some things you want to compress. When I know exactly what that PowerPoint presentation I need to make, but I just need to do digital ditch-digging and go through and do it — I wish I could click my fingers. Or expense reports or travel. I wish I could click my fingers and have the flights. But when I’m thinking, okay, think about our strategy moving forward — sometimes you just need that kind of staring out the window, looking at birds, just chewing on that. I would love to be better at that. If you just put the knowledge in my head, I didn’t earn it, then it doesn’t stick. I need time deeply thinking about the stuff that’s important. I guess I could even argue that if you compress everything else, I have more time to think about these deep things. I never thought about that time compression thing, but that’s really what it is.
Dave Brear (16:18): Yeah, exactly. And the next logical argument in this is, well, AI is only good for compression workflows — the ones where you really need to think, you need to keep AI far away from that. But actually, I don’t think that’s necessarily the case. I think you can use AI in a different way to expand your thinking. You can give it more context, and the more context you give it, the more thinking you do with the AI, the better the output you get. It’s your thinking, but the value of it’s been elevated by the fact that you’re thinking externally with AI.
Brian Madden (17:10): And this is something that — I mean, this is all over LinkedIn nowadays. Everyone knows, you can have a chat conversation with AI, and the more you type, the more it has. Whisper Flow is out there literally advertising that more context is better. So you should have AI, you should be able to talk to it, so you can ramble on for hours and give it more context. Which is, ha-ha funny, but also very true. And the idea that AI is better when you don’t just ask it to do this — drag in, here’s a paper, here’s a document, here’s an example before, here’s what my thinking is here. So the more files you add into AI conversation to give the AI more background context, the better the results are, because it knows what the heck it’s doing. So that’s what you’re talking about — more context is better.
Dave Brear (18:05): Absolutely. I came across this completely by accident as I’m evolving my AI usage. Exactly like you said, the more I put in, the better it came back. So I started religiously, sort of greedily harvesting every piece of context that I could legitimately get. Meeting transcripts, or even the thoughts I’m having about the work I’m doing — I’m writing them down religiously so I can put them in a place where the AI can sort of search them and look at them and use them in my thinking. Each additional piece of context that I put in what we could call a context vault elevates and compounds — it makes it better. Something I thought about last week, if it’s relevant and useful to what I’m thinking about this week, it can be brought into the mix.
Brian Madden (18:57): I should jump in — when you say context vault, for people listening, don’t overthink that. This is just an AI term for the place it’s holding all this context. It could be just a folder on your computer that has a bunch of note files in it. It could be OneNote. It could truly be a paper notebook and you take photographs of the page and load them into your iPhoto app or whatever. The point is, don’t get stuck on, wait, what is the context vault — for every worker it’s different. It’s just where you hold your notes and ideas and to-dos and all that kind of stuff.
Dave Brear (19:39): Absolutely. As I said, the more information in this vault, the more it compounds. But what I also didn’t expect was that the sheer act of writing or even dictating — because I also use voice dictation for most of my conversations with AI — the act of talking through my thoughts and externalizing them really clarified my thinking. And this is before I even got a response from the AI. Just talking out loud about what I’m thinking about helped me to engage with my thinking process more deeply, and then what the AI gives me back.
Brian Madden (20:11): Oh, that’s interesting. So it’s not even like — you’re just like, I want to use AI to help me think, I know AI needs a lot of information, so what do I want to tell the AI? And then you push record to start dictating to it, and just the act of you speaking all the things that were important to you helped your thinking. Your pre-processing phase is already helping your thinking, and you haven’t even burned a single token yet of AI.
Dave Brear (20:41): In software engineering, there’s a term called rubber ducking, where if you have a bug you’re having trouble thinking about, you pretend that there’s a rubber duck on your desk and you explain the problem to the rubber duck. The theory is that by externalizing your thinking, by talking through the problem, you’ll stumble across the answer. It’s effectively this, but with the added insight that the duck can talk back to you, and it can provide you other insights that you’ve already given it in the past.
Brian Madden (21:11): And the duck is getting smarter and smarter every six weeks. This is like a horror movie plot. That’s awesome.
Dave Brear (21:17): Yeah, too smart sometimes. It’s getting too big for its boots. So the problem is, now I’ve started working this way, I just can’t go back. And I don’t think I’m alone in this — I know you’re doing the same. I think this is happening across the board. Knowledge workers are doing this in greater and greater numbers, and it’s happening quietly, but it’s snowballing. I think this is something that enterprises are going to have to deal with in the short to medium term, because it —
Brian Madden (21:52): Okay, pause for a second, because I want to be crystal clear on what this is. First of all, you’re not talking about an AI product. This is not some new product you’re using. This is just regular Copilot, ChatGPT, Gemini, Claude, whatever. You’re using the same chat LLM interface you always do. The difference is, instead of you loading some files, dragging in some documents to have a conversation, and then the next day you throw that conversation away and start again — instead, you’re just collecting into your context vault, which is just a folder on your computer or it’s a OneNote or it’s photos or whatever. You’re collecting all that. You tell Claude, hey, everything you need to know about me is over here. You’re basically building it like a little Wikipedia for Dave — Davepedia. And then it’s your rubber duck or whatever. Now every conversation you have with it, you don’t have to re-explain everything from scratch, because it’s all there. So the AI — you’re like, hey rubber duck, I want you to do this — it’s like, hang on a second, let me read. Yeah, I’m caught up. What’s up, man?
Dave Brear (23:10): Exactly. And enterprises stand to benefit greatly from this, because having knowledge workers just have all this information to hand, being able to articulate it really clearly — it’s a game-changer for the quality of knowledge work. Not necessarily the volume of it, but the quality of it goes up. It’s more insightful, it takes into account more lessons learned from stuff that’s happened before. But it also comes with a bunch of risks to the enterprise. Two that I’d like to talk about. First of all, the fact that it’s actually quite a blind spot and a security problem if they don’t have any visibility. If you think about the way that we’re working here, it doesn’t require any integration into corporate systems. It doesn’t require synchronizing to email, calendar, file repositories. It is just literally the knowledge worker thinking something in their head and writing it down. That’s all this needs to be, in its minimal form.
Brian Madden (24:06): And that’s how it began. Because using AI in this way — I’ve been calling it a second brain. I don’t love that term, I think it’s a thing that other people use — but you were the one that told me this. About six months ago, you explained this concept to me, and you’re like, nah man, I’m just using my own personal Claude subscription, this has nothing to do with Citrix. You said, just create a folder, talk to it about what you want, here’s who I am and what I want. So I’m just like — at Citrix, we use Citrix, so I can’t get my information out of Citrix onto my laptop, but I don’t need it, because I’m like, well, here’s a blog post, here’s all the blogs I write, those are all public. Here’s some speeches I give on YouTube, those are public. And then I just spent an hour or two pontificating to it about my worldview, and then wham bam — I got a super-smart rubber duck.
Dave Brear (25:08): Yeah, indeed. And I think I don’t hate the term second brain. What is really useful is that it highlights just how individual everybody’s first brains are. I came to you and spoke to you about this super early in this process for me. I think it was like week one of, oh my goodness, look what I can do here. So I had not really developed my system into anything concrete. It was just an early, hey look, isn’t this a cool thing? And since that —
Brian Madden (25:38): Put a pin in that — hang on, put a pin in that, because we were going down the path of you saying there are two challenges. One of them was, this is invisible to the corporation, which I can say is true. This is an official Citrix podcast — I would like to say since then, we’ll talk in future shows, we’ve moved our systems into Citrix and we’re doing it in ways that are approved and everything. There was never any super-secret corporate information in this rubber duck vault in the original, because it didn’t plug in. But you’re saying, everything we were doing was invisible to the company. And it’s funny now because even if the company supports this — at Citrix, we use Office, good Microsoft partner, we use Copilot, and there’s OneNote and Copilot can hook into all these things, and you can start to use Copilot from your corporate environment with your corporate account fully above board, in the way that probably the executives are telling you to use it — but they don’t really have visibility into how people are using this AI to, as you say, expand their thinking, I guess.
Dave Brear (27:00): Yeah, that’s it. Just to defend our initial experiments a little bit more — you could argue that it’s not really very much different from having a physical notebook on my desk and putting random ideas into that notebook, which is something a lot of knowledge workers would do as a way of interacting with their thoughts and ideas. The real difference, though —
Brian Madden (27:21): Like we pass out notebooks of swag.
Dave Brear (27:27): I’ve got a drawer full of them from various conferences. It’s more than that though, because although the individual insights going into this context vault are coming from my brain — they are just thoughts I’m having that I’m legitimately not copying out of any sensitive company system — when you take all these breadcrumbs together and put them, you can actually synthesize them into something that could perceivably become company-sensitive. So it was pretty early on in this experiment that we thought, this really needs to happen within the walls of the enterprise to ensure information is secured and managed appropriately. The scenario you gave where this information could be in OneDrive in the company’s M365 tenant, using Copilot — yes, right now the company doesn’t necessarily have visibility of what we’re doing, but it has the ability to control all those pieces of information and work with that. The OneDrive data all exists in a company-controlled repository, the Copilot is a company subscription, it doesn’t leave the tenant. By moving this type of thinking into the enterprise, we can put those security controls on it. Even if they don’t exist today, they can be built and engineered, because it’s in the right place.
Brian Madden (28:49): So I guess that’s the key. Probably everyone — I mean, since you showed me this way of working six months ago, it’s kind of blown up. It’s all over LinkedIn and Reddit, there are YouTube videos on this. Andrej Karpathy, who’s one of the co-founders of OpenAI, did a post that got like 30 million views describing this exact concept. So it’s catching on that people are starting to work this way. The challenge for the enterprise is they have all these workers using AI in this way, and their little rubber duck vaults get more and more powerful — the more you use it, the more context it gets, the more files go in there, the more ideas. It’s a risk for compliance — is there PII in there? It’s a risk for security: if the person leaves the company, this all goes with them, because it’s their personal ChatGPT subscription, and who even knows what was in there. I see how that’s something that companies — it’s kind of like in the earlier days of consumerization of IT, you couldn’t block that from people. But you can’t put the smoke back in the bottle. When I was using this thing, when you first told me to start working this way — boy, within two hours I was like, this fundamentally changes everything. I’m never going back to working without a system like this. And that was literally two hours after using it. So the companies can’t tell workers, no, you can’t work this way, but they also — yeah.
Dave Brear (30:29): They need to provide a way of doing it, basically. The enterprise’s challenge is to find a way of bringing this within the walled gardens and enabling this workflow, so they can get the benefits from it, so their knowledge workers can get the benefit from this. If they don’t, they will just do it anyway outside of the control of the enterprise.
Brian Madden (30:46): Yeah. And literally, there’s no tell. You’ll just think your employees took a can of spinach like Popeye, and we’re super workers now. You said there were two things. The one risk was that it’s invisible to the enterprise. What was the other one?
Dave Brear (31:02): The other one is, we’ve talked about this notion of the more context the better — and that is largely true until you reach a certain scale. At which point, if the context vault, the second brain, the repository of all the information gets so big that interacting with that data set becomes cumbersome. AI models can handle million-token windows these days, but that doesn’t necessarily mean you should shove a million tokens worth of context into every single request. First of all, it will cost a fortune. Ingesting an entire vault just to ask a specific question about a specific customer is a very inefficient way of working. But also, just like humans, if you give the AI too much noise, too much irrelevant context, the quality of its output actually diminishes — it struggles to focus on what’s important. So the other risk that enterprises need to do is make sure the data is sanitized, that it’s relevant still, that it’s pruned so old data sets are removed, that it’s organized in such a structured way — this is how we understand this is relevant to this customer, this project, this different sphere of responsibility that the knowledge worker is working with. So we need to be able to —
Brian Madden (32:22): It’s interesting because you can’t just turn this on and tell employees, now you have it. First of all, probably everyone who’s using Copilot today or Gemini with their corporate subscriptions theoretically could do this today — or some version of this. But you have to show the workers that it’s a thing. And then also, I can say, after working with this myself — you have to be careful that you don’t outsource too much thinking to it. Maybe I say, you know everything about me, write a blog post about this. It’ll do it. It’ll be good. It’s not a Brian blog post — I wouldn’t want to just publish it — but maybe it had some ideas I wouldn’t have thought of. If you were an employee who’s not really engaged, they’re like, you know what, it’s just work, who cares, publish. But now you can sort of get to the point where, especially within large knowledge worker enterprises where your workers are letting AI do more, it’s more of a risk of — there’s a lot there. This is not an IT issue, by the way.
Dave Brear (33:34): Yeah. No, it’s like management information. Every single data source within an enterprise suddenly becomes something that is even more valuable. And I think the enterprises that will rise to the challenge are the ones that will look at every system of record, every application, everything, and look at it as a consumer service that needs to be provided to the business in a way that can be consumed by AI. Putting an MCP in front of it, having it be accessible, and making sure the data is relevant, clean, and secure as well — only providing information that a user is entitled to.
Brian Madden (34:18): And then plus all that boring IT stuff like governance and compliance and audit and all that. This is interesting too, because this kind of ties back into the earlier conversation about AI using your existing applications and your existing enterprise systems. On the one hand, you can see how this rubber duck context vault thing does empower employees, especially those who are particularly engaged, with more powerful thinking — they truly are starting to use AI as an extension of their thinking, not just a fancy Google lookup engine or whatever. And you can see, as you’re using the system more and more, it knows more about you and your work and the things you’re doing — you have the ability to actually connect it into your enterprise environments. So, like, into my desktop environment. If there are things I want to do and I’m working with my context system in this way, where it knows how to use the various applications it needs, it can connect into my corporate email. It can connect into spreadsheets. It knows, you need to do this — and like, this thing, ClickUp — and this is in Salesforce, and this is in Gainsight, and this is over here, plug that one in there. Here’s the process.
So the whole thing about, why would you want AI to use a computer? It’s not so you can automate things. We’re knowledge workers, we don’t really have tasks that are that automatable — if we did, we’d be task workers. It’s just so that as AI is helping us work more and more, and it’s having access to my files and my emails, why wouldn’t I also want it to be able to interactively process the systems and put it into the systems that are used, which by the way have existing governance for, and we can sort of extend that. That to me is why computer-using agents, and why AI using computers and AI using browsers, is a thing. It takes what you’re saying — that kind of rubber duck context cognitive extension — combine it with access to the existing corporate data and applications, and now you got the future.
Dave Brear (36:33): Yes, indeed.
Brian Madden (36:35): Is that the future or is that now?
Dave Brear (36:39): Well, that’s the thing — if it’s the future, it’s the future that’s coming up really, really fast, probably faster than anybody could have predicted.
Brian Madden (36:47): Yeah, yeah. So I think we just go on.
Dave Brear (36:50): I was going to say, coming back to the thread of the second brain stuff, because I think that’s probably the last topic that we wanted to cover. We’ve kind of talked around this a little bit. You and I have talked about the fact that these context vaults that we have, we think of them as second brains, because they are repositories that contain our thoughts and can be used and leveraged in such a way. One of the interesting things we’ve done with our individual second brains is looked at ways we could actually make this information available to other people as well. Isn’t that right?
Brian Madden (37:24): Both of us being kind of techie people, we were doing this context vault thing with all these files. We store them in GitHub, just because it’s centralized — privately stored on GitHub, but it’s centralized — it gives us version control and rollback, and you can look through history, it’s interesting. We’re like, hey, we should connect our two second brains together. That worked, and it was chaos. Because, like you were saying, it was getting confused, whose ideas were whose.
Dave Brear (38:05): But we figured out that with very clear delineation, getting another person’s perspective is still very feasible and possible. By having an MCP connector to your public vault, I can say, what does Brian think about this? I can make a very conscious request to, well, what does your cognition say about this? And there’s a very clear guardrail between my thinking and your thinking, and never the twain shall meet, unless we actually ask for your perspective.
Brian Madden (38:37): Yeah. It’s interesting because in each of our own personal context vaults — it is personal for each of us, they are architected in different ways just as they make sense for us to work with. But it’s also your inner thoughts. There’s customer information, there’s roadmap, there’s strategy. So I don’t want all that going out the door. It’s the difference between what you really think and what you say. So we each, as sort of an experiment, decided to make public versions of our second brain — context vault, rubber duck archive, whatever — and actually make them available so that anyone can plug into them.
This is something that we both do, we’ll put the links in the show notes. Every AI product today, you can go into the settings, connectors, push a button, and connect it via MCP to other data sources. You can actually connect it into the Dave or the Brian or both data sources, and sort of, now I’m using it and saying, what would Dave think about this idea? It sort of knows it’s not merging the brains together, but it knows there’s this MCP pipe I’m going to, and that’s someone else’s opinion. I’ll pull it in and help you do your own processing. It’s wild. It’s very experimental and very early, but it’s out there. You can read more about this.
I think in our final couple of minutes as we close up today — Dave, you read about this all the time on LinkedIn. That’s where people can find you.
Dave Brear (40:14): Yeah, absolutely. I publish every week or so on LinkedIn, on topics like this — personal knowledge management, thinking with AI, that kind of thing.
Brian Madden (40:25): I’m writing on LinkedIn also, and on the Citrix blog site — we’ll put the links in the show notes as well. I’m really more writing about this kind of stuff and how it ties into enterprise IT, and how we connect together and how we think about all this. I think we have a show, man. Even right now I can think of like 20 different episodes I want to have about these conversations. I would say to you, the audience: reach out to us, you can find us on LinkedIn, and let us know about future topics you might want to hear. This show is in audio and video version, so by the time you’re actually seeing or watching this — if you’re watching this on YouTube, you can listen to it like a regular podcast on Spotify and Apple and stuff like that, and vice versa. If you want to see a video version, that’s available too. So with that, we’re going to wrap up this first episode of the Citrix AI Hotsheet. Dave, thanks for taking the plunge and doing this. This is fun. I think we’ll keep the conversation going.
Dave Brear (41:28): Yeah, thanks. It’s been a pleasure talking to you as always. See you later.
Brian Madden (41:33): Thanks.