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A special edition of the Citrix AI Hotsheet. Instead of the regular conversation with Dave Brear, Brian Madden shares the keynote he gave at EUCTech in Norway: “The Last Chapter of EUC.” He lays out where the end-user computing industry is in the summer of 2026 and where it’s heading. Why the “AI isn’t worth it” story became the “AI is too expensive” story (capabilities vs. diffusion). Why most of knowledge work is invisible, and why that’s the part AI has to reach. The updated seven-step roadmap for how AI enters work — from faster search to the published self. An honest audit of every EUC primitive (VDI, image management, profiles, group policy, performance monitoring, endpoints, the control plane) translated into its AI-era successor. The close: the last chapter of EUC is the last chapter of book one. Book two starts now, and the people who run workspaces are the ones who get to write it.
Links mentioned
Brian’s Citrix blog — the 7-stage roadmap (2026 edition), “the SaaSpocalypse won’t touch the enterprise software moat,” “skills are all you need”
brianmadden.ai — Brian’s published second brain (MCP at mcp.brianmadden.ai)
Build your own AI second brain — starter prompt
Transcript
Hello, my name is Brian Madden, and you are listening to a special edition of the Citrix AI Hotsheet podcast. I’m calling this a special edition because instead of the regular conversation between me and my Citrix colleague Dave Brear, which we’ll pick up next week, in this episode I’m actually going to share with you a keynote speech that I gave a couple weeks ago in Norway at EUCTech.
Great event. I think it was a good talk. The talk was called “The Last Chapter of EUC,” and I really focused on where, right now, summer of 2026, the EUC industry is today and where we’re heading: how AI is going to impact things and what this all means. So I laid that out and unpacked it. And I thought it was good enough that I wanted to share it more broadly than just the audience at that conference. So I’m sharing it here with you as episode two of the podcast. If you’re listening to this in audio-only format, it’s also on video if you want to see the slides on YouTube. So, anyway, today, episode two, I’m recording this on June 13th, 2026. This is my talk, the last chapter of EUC.
To set the stage: if we look at EUC and AI and how AI is impacting knowledge work, a few months ago and last year the narrative was basically “AI is not worth it.” How good is it really? It’s not as good as we’re expecting it to be. Companies are spending all this money on AI and they’re not getting the results, they’re not getting the ROI. So the story was, I don’t know, is AI even that good? It seems expensive, and who even knows.
Compare that to where we are today, and the narrative is almost completely different. The narrative today is about cost. You hear all these news stories about companies spending too much on AI. There’s that talk about tokenmaxxing and individual workers trying to maximize their AI tokens. And it’s just really expensive. Companies are clamping down. They’re putting usage caps or spend caps, which is kind of funny. So it’s like, okay, so AI works. That’s what it says to me. The AI is working — it’s actually working too well, and now it’s too expensive. It’s still a story about ROI, certainly, and I think that’s what this bigger talk is going to focus on. But it’s definitely a different story today than it was six months ago, twelve months ago.
I think the reason we have two stories is because you have to look, on the one hand, at the capabilities of AI — what can AI actually do, what are the raw capabilities? And then the second angle is diffusion. Diffusion is a term that’s used in the industry — it’s not a word I invented. Diffusion is basically how fast AI is absorbed. On the one hand you have capabilities: how good is AI? But just because something exists and is good, it has to be absorbed to change whatever it’s trying to change. AI diffusion could be how fast it’s absorbed by an individual worker into your workflows. It could be applied to a department: how fast is it absorbed into a company? It could even be an entire society or an entire economy. You talk about, oh, if artificial general intelligence changes the world — well, if that were invented tomorrow, it’d still take a few years for it to filter down and diffuse into the economy and into the world.
So capabilities and diffusion are two very different things. Capabilities are going up and up and up. For a while it kind of got to be: well, we think AI is going to plateau, it’s not going to keep scaling, and eventually it’s just not going to work. So far that has proven not to be true. Everyone that said a year ago, two years ago, that AI was going to plateau — I mean, I spent a few days playing with that new Claude Fable model they released to the public (Fable is the general-public version; Mythos is the secure one). It’s crazy, it is way better. It’s insane. So the capabilities of AI are still growing and growing and growing. The diffusion, though — how fast we can absorb AI — that’s what’s hitting a wall. It’s not scaling to the same speed as capabilities.
I think the reason diffusion doesn’t scale is because we have AI-caused congestion. If AI is able to speed things up — let’s say I’m an individual worker at Citrix, and however I’m using AI, let’s say before AI I create four TPS reports a day, and then thanks to AI I can now create ten TPS reports a day. That’s great. That’s more than doubling my output. But does everyone else absorb that? Can the business absorb the additional work that I’m doing? Or is it just creating backlogs? That’s what we see: individual companies or departments or workers or groups that start to use AI quite a bit and spin it up really hard. You hear this with developers. They can use AI to write all this code, but the system can’t check in code fast enough, it can’t validate code, it can’t create tests, it can’t do security checks. So you just end up moving where the bottleneck is.
That’s what’s happening. A lot of the conversation today around the ROI of AI actually stems from diffusion. It’s not that AI can’t do something, it’s that it can’t be absorbed into the business in a way that’s big enough to actually make the transformation people are thinking of. So it’s still a problem, but again, it’s not that AI can’t do it, it’s that companies can’t absorb it.
I feel like the reason this is the case — and this is a slide I put in the blog, and when I say the blog I mean my blog on Citrix, citrix.com/blogs, where I’m writing almost every week, and I’m writing real stuff, not Citrix marketing blogs, real stories about what I’m thinking about with AI and its impact on business — I did a post on this where I talk about how, in order for AI to impact knowledge work, we have to think about what knowledge work actually is. For the past decades, when we think of knowledge work, we think of emails, documents, transcripts from meetings, chats, all the files and folders and stuff that lives in Office. And yes, that is knowledge work, but I argue all of these things, what we traditionally think of — especially when we think about IT applying to knowledge work — are just the visible portion of knowledge work, some small percentage. I’m making up the number: maybe 20% of knowledge work is these things.
Actually, emails and documents and transcripts aren’t the knowledge work itself. These are the outputs of knowledge work. The real bulk of knowledge work, let’s call it the other 80%, is invisible. You can’t really see it, because it’s the thinking, it’s the worker’s reasoning, it’s using your skills and your knowledge and your education to know about things, it’s the judgment calls you get from years working at a company or decades working in an industry. It’s the times when you just have to stare out a window, look at the birds. Here, I’ll give a tour — there’s my actual window, and I spend a lot of time doing knowledge work staring out that window, looking at birds, and just thinking about what I need to think about. Taken together, these are what actually make up knowledge work. But most of our focus from IT has only been on that 20% portion. So no matter how well we optimize or fix or analyze or figure out what’s happening, we’re missing the bigger picture of what actually happens in knowledge work.
If you want to change knowledge work, you need to change the invisible portion, not the visible portion. And the invisible portion is really hard, especially for us in IT. If you think about us as IT people, I could argue that 20% visible is where end-user computing is. That’s where IT lives. That 80% that’s invisible is not really the kind of thing IT people think about. This is what HR, MBAs, and consultants — not IT consultants, but McKinsey, Bain, Boston Consulting, PwC, the companies that do business transformation consulting — they spend their time living in this invisible part of the world. And we in end-user computing spend our time in IT living in that 20% visible part of the world.
The reason I mention this is because, let’s say that’s our breakdown. What happens when AI enters this world? If AI is a technology, and if the tech universe before AI was just this little tiny wedge, the 20% — well, if AI is going to truly transform work, AI has to transform all of work, that visible portion and the invisible portion. So if you think about the role of EUC, or the role of IT, what used to be our focus on just that 20%, we all now have to focus on this 100%, this really big piece of the pie, and go outside of our EUC bubble.
This is real transformation. This is real org redesign, and this is complicated. This is why, so far, we’re not really seeing AI have these massive impacts people believe are possible — because most of the AI efforts have just been focusing on that 20%. That’s how we think about technology, that’s how we implement new technologies. Everything we’ve done in the past 30 years, from desktops to mobile to phones to cloud to virtualization to working from anywhere to apps to SaaS — all of that was really focusing on that 20% visible. And now it’s going to be a big shift for all of us to grow our thinking about how technology impacts the entire company, and the invisible, cognitive parts of the job, not just the visible parts.
So this is the landscape I set up for why I believe AI wasn’t like other technologies, where you just plug it in and instantly get ROI. We’re going to get there, but it’s going to be a bit different.
How AI enters work: the seven steps
Stepping through this, what I want to do next is look at: okay, if AI is going to enter work, all work, and go beyond that 20% visible and truly affect all of our cognition, let’s look at how AI is actually entering work. I’ve developed, I think, seven stages — a roadmap, and I actually published this roadmap. It was late June last year — almost exactly one year ago — that I published this seven-stage roadmap, and I’ve been using it for the past year as a way to explain: okay, first you use AI like this, then like this, then like this. It’s how you get better and better with AI. And I realized in the past year my thinking around this has evolved. So I’ve just updated it, based on this talk I’m giving today. I have the seven phases of human-AI collaboration roadmap, and this is now a 2026 edition. I wrote this updated blog on the Citrix blog, and here’s a QR code that takes you right to that story. But I’m going to walk you through it right now.
So, literally, I want to walk through the seven steps. This is how AI enters knowledge work, how humans use AI to collaborate at work. I think there are seven steps people go through.
Step one is pretty simple. I call it AI as a faster search. You have a worker, you have an AI system. This icon I’m using is just “my AI” — in this case it’s ChatGPT, it’s Claude, it’s Copilot, it’s Gemini. It doesn’t really matter; everything I’m talking about here applies equally to all AI products. The idea is, this is what happened when we all first started using ChatGPT, maybe three or four years ago. You’re just using it like a better Google or a faster answer machine. I send it a query, it sends me back the results, the end. If I want to do something else, maybe I send a new query. It’s a lot of copying and pasting prompts into the AI and copying the results out of the AI into my own thing. It really is one-and-done. I’m using AI for simple questions and answers.
This, by the way, is where I feel like most people still are today. I’m making this number up — let’s say, again, 80%. Most workers today are still using AI by putting in a prompt, having a quick little conversation about it, saving it, and they’re done. The next time they need AI, it’s starting from scratch. In the crawl-walk-run progression of using AI, this is the very basic crawl. The very beginning. But it’s interesting, and the reason I’m dwelling on it: a lot of people who talk about AI and say they don’t believe it, who think AI is all hype, that we’re living in an AI bubble, that it’s all a scam and not really real — I find almost everyone who has that position is using AI like this. They’re only at step one. Of course they don’t get it, because they haven’t really started down this journey themselves. So that’s the first step.
Step two is using AI as a thinking partner. This is where we get beyond individual, one-and-done conversations — you’re truly working with it, ideating, having ideas, going back and forth with the AI. What enabled this? AI had to get smarter, which it did over the past few years. AI had to have a longer context window to be able to have longer conversations. And AI had to have the ability for us to upload documents into it. So when you use AI as a thinking partner, most people now aren’t just talking to it, you’re loading in documents. You’re saying, okay, here’s a paper I wrote, here are a couple of papers to research, here’s the strategy for our company — you put all that in there, and then you start having a conversation with AI about all that stuff. Maybe you’re doing it by literally dragging files into the conversation. Maybe you’re using something like Google NotebookLM or Claude Projects or ChatGPT Projects.
At this point, a lot of people start talking to their AI. Even if you’re not using the actual voice interface, maybe you’re using your computer’s dictation system or third-party dictation software where you can just talk and dictate instead of typing. That’s what I do personally. I’m still using the chat interface, but when I’m staring out my window looking at birds and pontificating about the world and what I need to do at work, it’s all being transcribed and sent into my AI. This is where we move away from AI as simple questions to really diving in deep, researching and analyzing and thinking: what should we do? What’s our strategy? How do I roll this out? How do I handle this? It truly becomes a thinking partner.
I’ve been using AI like this probably two years. I think a lot of us have. A lot of you listening to this are already here as well. But look at the user base, the workers of your company. This is not everyone. I feel like using AI as a thinking partner is still only 20% of workers, maybe. My mom is not doing this. A lot of my coworkers will maybe add a document and have it summarize the document, but truly generating ideas — I’ll tell you, everyone who thinks AI is not creative, or that it’s just a stochastic parrot, or just a pattern machine, they’re not using AI like this. This is where it starts to be obvious that AI can be a true thinking partner. And now we’re starting to get into the core of helping in the invisible portion of knowledge work, not just what’s visible. So that’s using AI as a thinking partner.
Step three is using AI as a cognitive extension. This is something that blew up earlier this year. The idea is: instead of loading documents or context or papers into your AI for every single conversation, instead of doing it one at a time, flip it. Instead of you taking papers to your AI, give AI access to everything you have. For example, you can point it at a folder on your computer and say, this folder is going to be my personal Wikipedia, essentially. This is going to be my context vault. This is going to be my brain. I’m going to put all my documents in here, all my papers, everything I’ve written, all my strategy, all the important stuff from work. I also want you, AI, to feel free to write files in here — write instructions, write down skills, any facts you need to remember. If I tell you how to do something and you need to do it again, put that all in here. So I have this context vault, context library, knowledge library, personal Wikipedia, whatever you want to call it — a place where AI has access to all my stuff.
This is what a lot of people call the second brain. I actually started using AI this way — we talked about it in episode one of the podcast. I started using it like this in January of this year, so about six months ago. And I wrote about it on LinkedIn. Here’s a QR code for that LinkedIn article. If you go there on LinkedIn, by the way, in the comments I pasted a starter prompt you can use. So there’s really everything you need to get started. Again, you can do this in any AI platform you want, it doesn’t matter.
This is huge. This really blew up in the past six months. A lot of people are talking about it; there’s a lot on LinkedIn, Reddit, YouTube videos on how to do this. This is truly transformational. Since I started working this way, I basically use AI for everything. I’m only talking to AI, and it just knows everything about me, everything I’m working on. It just really changed the way I do knowledge work. I wrote this LinkedIn article and it changed the way I work, and I think it’s going to be this way for everyone once everyone starts using AI like this. We’ll come back to that at the end, because I think it’s important that if you haven’t gotten here yet, this is an important next step to take. And I don’t think a lot of people are here yet — like 1%, truly, not a lot of people. So step three is when you start to use AI as a cognitive extension, a second brain, and you start to use it more for everything.
Step four is using AI as a multi-tool agent. All these steps build off each other. Once you have that persistent context library that your AI always uses — so every conversation knows everything about you, everything you’re working on, knows how to access all your files, and knows how to do all the things you ask it — well, next is to take that AI and connect it into the world. All AI platforms today, it doesn’t matter, Claude, ChatGPT, Copilot, Gemini, whatever — you can click a little plus button, go into connectors, and you see a whole plethora of connectors into Google or Office or Salesforce or Canva or whatever applications you use. Any application that has APIs the AI can use, MCP (model context protocol), or custom connectors — this is next. You take this AI that knows everything about you and start connecting it into your applications.
If the application has an MCP interface for AI, great. You can connect it natively by clicking plus right from the AI tool you’re using. All the AIs can also use web browsers. So if there’s a website you want access to and you don’t have API access — maybe it’s a website you use at work but your company doesn’t give you the API key, or it’s on Google but it’s my Citrix login and it’s Microsoft and I need to connect it all together — it doesn’t matter, because AI can use a browser. Every AI platform right now can operate a web browser. Some operate browsers in the cloud, some operate browsers on your client desktop. Obviously it’s Citrix — if you’re using Citrix, you can have it operate your web browser published on a Citrix desktop or wherever you want. The point is, anything that you, as a human worker, can get access to via a browser, the AI can also access via a browser. It literally moves the browser around, takes screenshots, pulls data out, operates the mouse. It’s fully capable. Sure, it might get confused a little, but that’s really not a thing anymore.
The same is true for desktop applications. Fundamentally, when I’m using AI as my cognitive extension and I want to plug it into everything, I want to plug it into all of my applications. If an application is a modern app with an API or MCP, great, AI talks to it directly. If it’s a browser app, that’s fine, it can access my stuff via Chrome Enterprise Premium or whatever I’m doing. And if it’s a desktop application, or my general desktop environment, it can operate a desktop via CUA — a computer-using agent. All the AIs can operate computers today, just like humans: they move the mouse, they type keys, they take screenshots. There’s actually a benchmark called OSWorld, which tracks how good the AI is at using a computer, and the median human scores like 72 or 74 on that benchmark. AIs now are scoring in the 80s. You don’t even need the best model — even Sonnet can score in the 80s. So if you think AI isn’t able to use a computer, that’s old information. It was true a year ago, it is not true today.
The reason I want this: remember, this is the progression of how I’m using AI. In one, I’m using AI just for search. In two, I’m using it to start thinking and having real conversations. In three, I’m putting all my thoughts into a vault or library the AI can always access, and I’m only accessing that vault through my AI. And then in four, I’m taking all this interaction with the vault and extending my AI so it can use my applications. And I don’t let it use applications because I want to create workflows or automate little workflows. It’s not about that. It’s more like: hey, I have my monthly review with my manager, so let’s review my accounts. Let’s pull the first account. Okay, it’s this company. Go to my email and look at the last meeting with them. What’s that? Can you find this document? Go into our Salesforce tool and pull that down and look at the ticket updates. Am I talking to them? So my AI needs access to all my applications — desktop, web, modern — it needs all of them, because I have access to all of them. It’s not like I need to automate. I’m not using my AI to automate some process. I’m just doing my job, because I’m a knowledge worker. My job is 80% thinking, and to extend my thinking I need access to my tools and data, which is all my applications. So, once I have that cognitive extension built in phase three, I can move on to phase four, AI as a multi-tool agent, where it can also access all the tools it needs.
Step five I call AI as a fleet — or maybe I should say a fleet of AIs, because at some point it gets to where we’re not just using one AI. Maybe I have my main AI running my context vault that I’m talking to mostly, but maybe it’s going to talk to others. If I’m a Citrix administrator, I want that AI to talk to the AI that helps me interface with my Citrix environment. Maybe I’ve got an AI tool for my help desk or HR, so if I need to put in a help desk ticket, my AI can talk to the help desk AI and that gets done. So we’re going to have multiple little AIs running and talking to each other. I can sort of unwind this map a little — they’re going to be here, there, everywhere. The point is, once we’re using our own AI and it’s connected to things and has all my information, it’s going to start talking to other AIs. It doesn’t need to natively API or MCP into everything; it can talk to other AIs that represent other systems, other people, other companies, other workflows. So I go from one AI to a fleet of AIs.
Step six I’m calling AI as a pod. When I call it a pod, this is like the unit of work at a company. There’s me — I’m going to have my pod. But all our coworkers are going to have their own pods too, because it’s not just me doing this, everyone is going to be doing this. In the old days you had workers, and those workers worked eight hours a day, five days a week, and you knew their capabilities and how they fit into the org chart, and you knew who to reach out to to get different things done. The unit of measurement, the unit of thinking, was a worker. What I believe is going to happen in the future is that it’s not just the worker that’s the unit of thinking — it’s the worker plus their ten different AIs and knowledge and systems. So it’s that pod, which is a worker plus AIs. I’m a pod, my coworker’s a pod. Everyone I work with is them plus all their AIs, so they’re a pod. My AI can talk to their AI, their AIs can talk to each other’s AIs, we can pull context from each other, and we can talk to each other as humans.
But it’s interesting, because we can talk to each other as humans, but we don’t have to talk to each other as humans. I’m not saying humans aren’t in the loop, but once all my cognitive information is in that context vault, once the AIs have all the skills and know how to talk to each other, they can work without us. There are still roles for humans, but work doesn’t only have to happen Monday to Friday between working hours. As emails come in, my AI can spring up and act on it. If it doesn’t know what to do, it can flag it and tell me, next time I’m at work, what we need to do. If it can do something — oh, I need it to reach out to a colleague — it can reach out to their AI and get that done. So AI is now operating sort of by itself. We’re still there as humans, but it’s flagging things and sending things to us when we need them, and it’s able to do a lot on its own.
This is very clear to me. If you go one, two, three, four, five, six through this list — maybe what’s on the screen as six looks crazy, but back up through five, four, three, two — it’s step by step by step. This is the progression. And you have to think about how this impacts companies when the whole company is operating this way. At Citrix, I’ve been very public — I’m one of the earlier employees using AI this way. Dave, my coworker who’s usually on this podcast with me, was the early user of the second brain; he’s the one who got me using it. There were two of us, then four, then six — a small group, and it’s expanding and expanding. It’s not going to be long. The ability to use AI like this is being built into all the products. Claude has Cowork, Copilot is building stuff like this, Microsoft, everyone is. So in the next three, six, twelve months, everyone is using AI as a cognitive extension. Then everyone gets to using it as a multi-tool agent, then everyone is operating a fleet. It’s moving through the list. When we get to every worker being a pod instead of an individual worker, it’s going to be different for work. It’s going to look kind of different.
I break this into three different worker types. By the way, a lot of people are talking about this now. Paul Roetzer from the Artificial Intelligence Show has his three worker types; his are different than mine. I’m looking at this from an EUC perspective. But you’re going to see types of workers that evolve in the future when everyone is using AI as a cognitive extension with access to all their tools. You’re going to see a lot of breakdown. Here’s my version of that breakdown.
One kind of worker is the cognitive owner. These are the people who own all the context and manage all that, and the judgment — they’re the source of expertise. These are the actual business-level people doing their jobs. They’re the cognitive owners of their part of the business.
Another role is the cognitive operator. They don’t own it, but they’re operating the fleet of AIs and the agents, how they’re all plugged together, keeping everything running and operating — not just from an IT standpoint, but really the cognition: where it’s pulling its data from, how they’re layering it together, what models are being used, keeping the whole system running as a cognitive engine. That’s going to be a role.
And then there’s a role I’ll call the cognitive curator. They’re the ones maintaining the context. All of this context, the little files I was showing in these context vaults — who maintains that and makes sure it’s up to date, that it’s layered on properly? If there’s context from corporate versus department versus country versus team versus individual versus project, how do you merge it all together? How do you ensure the right context, the right AI runs in the right places? Same with skills. I haven’t talked about skills — if you Google my name plus “skills are all you need,” you can see I wrote a blog post on this too. The AI develops skills, and when an AI gets skills, that’s how it knows how to do something, but it’s just a file. A skill for an AI says, oh, here’s the skill for updating the website: here’s the URL, here’s the GitHub repo, here’s how you do it, here are the standards, here are the checks. You’re basically telling the AI what to do, and it records it, writes it down, and puts it into that context library so that next time you need it to do something, it can see, oh, I know how to do that, because it’s all written down. But someone has to maintain those skills and keep them up to date, making sure best practices are incorporated.
So you’re going to have owners of the cognition, operators, and curators. The reason I mention this is that these bottom two — cognitive operators and cognitive curators — this really seems quite a bit like what we do in IT. And I think a lot of this is going to be what we do in IT. We’re evolving a little bit, because it’s not just the mechanics of the applications and security and workspace; we’re getting into understanding how the cognition moves through the enterprise and how it’s layered together. But that will absolutely be a job. It’ll be a job for humans, and a job for smart operator-type humans, which really is a lot of end-user computing folks today. So I think there’s a very interesting future for a lot of us. I want to come back and dig into that.
But first, notice we’re only on step six of this seven-step cognition progression. Step seven— maybe I’m cheating with step seven, because it’s an optional fork that could have come off at any point from three, four, five, six. I call it the published self. If you have anyone who’s an expert, they can take their knowledge, which is in their own context library or knowledge vault, and publish it and allow other people to subscribe to it. The concept of a published self is very important and underpins a lot of the other concepts I’ll be talking about through the rest of this talk.
I did this. If you want to see it, go to brianmadden.ai, or there’s a QR code here. I wrote about it on LinkedIn — this is a LinkedIn article I’m linking to where I say “announcing brianmadden.ai: I just published my brain, that context vault, for you to merge into your AI.” It’s interesting, because I built up and curate all my context — here are my articles, here’s my thinking, here’s what I’m talking about, here are my current viewpoints, here are the frameworks I use, here’s where my head’s at. That’s all in my own context vault, and I make it available. Whatever AI platform you use, you can go in, click the add button, connect to an MCP server, add mcp.brianmadden.ai, and now your AI is connected into my second brain. You can ask your AI questions and it can reference my second brain, or you can do your own designs and ideation and thinking and incorporate my stuff.
This is very interesting as more and more experts — not just independent experts in the world, but people within your company, people you want to follow, maybe followers on LinkedIn — I imagine we’re getting to the point where blending together the contexts of all the various experts you trust in different domains is absolutely a thing. And that underpins a lot of where we’re going to see corporations evolve in the next few years. So that’s step seven.
The reason I say this: every worker is somewhere on this path. From using AI like a better Google, to using AI as a thinking partner, to using AI like a second brain, to using AI like a second brain that has access to tools and applications in the world, to managing a fleet of AIs, to having my AIs operate as a pod, sort of independent — and then all that publishing to each other. That’s the path. Every single worker is somewhere on this path. You’re on this path somewhere. Where are you?
Let’s look at me. Faster search — I did that pretty quick, like early 2023. Using AI as a thinking partner — that was probably late 2024, early 2025. The cognitive extension, AI as my second brain — that was January. I was very loud about that in January. This multi-tool agent is, for me personally, just what I’m starting to do. At Citrix we use Microsoft Office, so there’s Work IQ, the MCP interface for your Microsoft knowledge graph. We’ve got other tools with MCP interfaces. So I’m just now, literally last week, next week, starting to experiment with connecting my AI into these various systems. I’m doing it slowly, with a separate user account, because I don’t want my account with my full rights to have read-write access to everything. So we’re doing it in a very gentle, controlled way. I’m just starting to take my second brain and really connect it into the world, which is super interesting. And the published self, number seven — of course I’ve been doing that for quite a while too.
The reason I mention this is that every one of your workers is going to be different on this path, going at their own pace. So whenever anyone has an opinion on AI, you have to understand where they are on these seven steps. I shouldn’t say you have to ask, but you have to understand. If someone’s only using AI as a faster search, I honestly don’t care about their opinion of what AI might be in the world or what its impact is. If you’re not using AI like a second brain, I don’t care what you think about OpenClaw or whether we’re in a bubble. So I want to educate people and move people along. You can 100% go into this cognitive extension, second brain step today. That’s very real, very easy, very straightforward. So if people aren’t there yet, let’s get them from step one to two, from two to three. But all of your workers — your manager, everyone — is going to be at different places at different times, and understand it’s going to be kind of weird, things are jagged in our industry when not everyone is at the same place.
So that finishes that section on how AI is entering work.
The current EUC model, translated to the future
The last portion of this keynote: I want to talk about the current EUC model. Remember, the title of this talk is the last chapter of EUC. In order to look at what the last chapter is, we have to look at all the chapters before that. That’s where we are today when I look at the current end-user computing model.
There are a lot of assumptions baked into our current model of end-user computing. We assume that people are the unit of measurement. Work is done by one person. Yes, we have multiple people, but you’re doing that work, you’re doing that work. It’s one person on one screen, on one set of apps, in one set of hours. And I get it, we have multiple screens, we use different apps in different scenarios — but the unit of measurement is a person with their devices and their apps working these hours. And, as I mentioned earlier, AI is changing all this. These are our checker pieces, and then AI just threw in all these weird chess pieces, and we’re like, what do these pieces even do? We’re trying to play the same game, and the pieces have different capabilities, and everything is different, and how does that even work?
So the first thing we have to recognize is that the assumptions and foundations on which all end-user computing is based are going away. You can argue on when, you can argue on to what impact, you can argue on how and in what order. I don’t think you can argue, though, that our core assumptions are changing and going away. So the first thing to really understand how AI is going to impact things: we have to understand that our world is changing.
That said, a lot of our world is going to transition over. Let’s take VDI. I work at Citrix and I’ve been writing about this quite a bit in the past year. Actually, to take a step back: I want to go through a bunch of end-user computing things today and translate them into what they’re going to look like in the future. So VDI — I think in the future it looks like VDI, to be honest. I do think VDI still exists in the future. It’s just that today VDI is used by humans, and in the future I think VDI is going to be used by humans and AI as well. I could say the same thing for almost any application, where apps today are all used by humans. I think a lot of apps are not going to go away. I described why in the last podcast, episode one, so you can go back and listen to that for more details. I think AI is just going to use the application. AI becomes a worker, essentially.
Part of the reason I think this — and this is an article I wrote, a blog post on citrix.com/blogs a couple weeks ago — to understand why VDI is going to exist in the future, you have to understand that AI is not eating all software. There’s a famous quote, I think it’s Marc Andreessen, about AI eating software, or AI will eat software. And AI will eat software, but not all software. That’s very important. I break it down into a pyramid: shallow, middle, and deep. You can look at all software and see where it is in the stack.
Shallow software is basically existing capabilities that already exist in our utilities, with a fancy wrapper around it. A lot of these new SaaS-type companies — Zapier (that’s probably not how you pronounce it in English; I live in France, by the way), Canva, Figma, Replit, a bunch of these — yeah, these are real apps your company might depend on today, but when you really look at it, it’s just a user wrapper over a commodity capability. So these are very shallow, and there’s not much to them, frankly.
There’s a middle layer. On the middle layer of software, these are real companies that have real data, but they’re still horizontal across all businesses. This is Salesforce, Workday, ServiceNow, Snowflake, Box, Dropbox. These are real enterprise products. They have real enterprise data, they’re systems of record, they have good enterprise security, they integrate with your domain system and directory system. But these are not industry-specific — Salesforce sells to all industries, it’s not a specific thing. So it’s a middle layer, because it’s not as shallow as a simple UX wrapper over a commodity. These middle layers have real things, but it’s not industry-specific deep.
And industry-specific deep — this is your Epic, your SAP, Oracle, mainframes. These are very industry-specific. These are applications that have existed for decades. They have baked into them processes and regulations. These are not things you change. Changing out these applications costs hundreds of millions, billions of dollars. It takes decades. People focus their entire careers on these things. So, again, AI is going to maybe use these applications, but it’s not replacing them at the deep, deep level. There’s just too much business process and regulatory process tied into them.
So if I look at AI eating all software: yeah, the shallow software probably is going to have a tough time against AI. The middle layer could be squeezed, but it really could do okay, because it does have real corporate data and systems of record. They’re going to have some challenges as they try to — if they enable AI to access their stuff easily, then it’s easy to take their data out and rewrite them; but if they close it down, they protect themselves but become sort of irrelevant. It’ll be really interesting. But these deep companies aren’t going anywhere. And, again, AI will use their applications, just like SAP and Epic and all these things went from desktop apps to web apps. I’m sure the UIs change with the times, and I’m sure providing a UI to AI workers is going to be a thing for these vendors, but these cores aren’t really going anywhere.
To that end, that’s why I go back to VDI. If you have a VDI desktop today, you’re going to have a VDI desktop tomorrow. It’s just that it might be used by AI workers, not just human workers. Again, go back to the episode last week for that.
The next thing I want to look at is all of these things from end-user computing that we’ve spent years honing — image management, layering disk images and diff files on top of each other, application packages. I think that still exists in the world of AI, except instead of building up images of applications, we’re probably building up images of skills. App virtualization or app layering, which is part of image management, I think becomes skill virtualization and skill layering. Profile management — giving the worker everything they need and what they have access to and all those settings — I think becomes context management. So we have skills and context. But what we’re actually doing to these, the way we’re managing them in the future, is not that different from the way we manage images, applications, and profiles today.
Let me give an example, in skill and context management. If you have one worker, remember that worker is going to have their own context vault, context library, knowledge vault, second brain — that has all of their stuff inside: their data, their files, what they’re working on, the skills they put in the system. And, again, multiples of your workers are going to have their own context vaults. Different coworkers, third-party consultants you’re hiring in as experts, are going to have their own. Influencers and experts you follow online are going to have theirs, like my brianmadden.ai. Within your company there are going to be all these context vaults too. There will be departmental-level ones — here’s everything you need to know for our department. Then business-unit-level ones. There’s going to be company-wide ones that have here’s our legal policies and our vacation and our HR and how we do IT. So there are all these different context vaults that have to be connected into each other and wired together. And sometimes there’s going to be flowing down — the company has a baseline, then a department has their own, then your group project has its own, then you have yours, and how are those inherited? Which ones overwrite?
So we get into the same thing as app layering and multiple app versions, and policy layering and resultant set of policies — which ones inherit, which one’s blocked, which ones override. It’s all of that stuff. But the individual atomic unit here is not a file or registry setting or an application. It’s a knowledge nugget, or a skill, or a skill applied to a certain system. So I truly believe all of the skills we have built as end-user computing professionals will absolutely translate into that future world. It’s just that, again, we’re not managing policies, registry keys, files, and apps; we’re managing context, agents, and skills. But a lot of the mechanics are the same.
As I’m saying, I can look at group policy — becomes maybe agent policy. I look at things like session recording — becomes agent observability, or maybe session recording becomes cognitive observability. What’s the key here? Agent, cognitive, skills, context — the words are changing, but how and why we’re doing it isn’t going to change.
I highlighted agent and cognitive, because when it comes to applying security, I talk a lot about how I believe AI enters a workforce much like a worker. You have human workers, you’ll have non-human workers; human personas, non-human personas. So in some ways, the way you secure a human worker today is the same way you’d secure an agent worker tomorrow. You’re looking at what they’re doing, what they have access to, making sure they’re complying, checking the logs, checking policy, applying guardrails, all that. But I also highlighted cognitive observability, because there’s another security vector challenge. If I’m using my AI, my AI is only as good as that context vault that has all my files and skills and what it learns. And now I start layering together other contexts. I have my context vault. I’ve got my coworker’s context vault. I have my manager’s. I have our marketing department’s one, our product management department’s, our engineering department’s, the company-wide one, other followers I subscribe to — maybe getting their brain access now as a feed instead of a newsletter. All these are coming together.
Well, when I say to my AI, okay, we should work on this next, what do you think? And it says that’s a good idea — how do I know that when my AI tells me that’s a good idea, it has my best interest in mind? What if one of my sources got hacked, or just went rogue and changed what’s put into their context vault, and it layered on, and now my AI has bad information? Maybe it says, hey, by the way, your most important number-one goal now is not revenue for the company, but, I don’t know, petting dogs or something. Whatever. The point is there’s this new layer that comes in. So it’s not just about agents. Yes, everything we do for human workers today we have to do for agent workers. But on top of that, there’s also this layer of cognitive observability and cognitive understanding — how these layers are built in the background, knowing how that’s impacting the AI, and then what the workers are actually doing. That’s going to be a thing too. The core skills of how you investigate that, how you secure it, how you look at the chain of custody and chain of command and change logs and transaction logs and merging together — that’s all skills we already have as IT professionals. So, again, this is a huge opportunity. There’s a lot of really great stuff to do, and it’s going to be pretty interesting for us.
What else do we do as IT professionals, specifically in end-user computing? We look at performance management, performance monitoring. That’s going to be a thing too. I think we can call that token management. Look at all the stuff in the news today, what I opened this talk with, about how everyone is upset that companies are spending so much money on AI. A lot of that money might be wasted — but how do you know it’s wasted? And how do you manage all that? There’s a lot of talk in the industry now about token routing or token efficiency. To give an example: let’s say I have some task, and my AI needs to do something in Excel. How should my AI actually transact that? How should it actually get it done?
Maybe it uses Excel via one of those computer-using agents, where it actually connects to a desktop, boots up, launches Excel.exe, and uses the computer-using agent to navigate Excel. Maybe that requires a very expensive model and costs 200,000 tokens. I’m just making these numbers up. Or maybe instead of the desktop version of Excel, it can use the web version. It opens up a web browser — I don’t need the full desktop version — and uses browser automation instead of a computer-using agent. It might still be the expensive model, but maybe I can do it with only 100,000 tokens. So I get the same task done in half the tokens.
Maybe this is a task that doesn’t even need Excel. Excel files are just XML. These AIs can load files. It can literally take the Excel file itself, the .xlsx, open it up, scan through the actual XML source, make changes to the XML, and zip it back up again. This is real: AI agents can operate on Excel files, run reports, pull data, make charts, without even using Excel. They’re just pulling in and processing the raw data. Maybe I can do that with only 10,000 tokens, and with a lower-class model — I don’t even need the expensive tokens.
Maybe what I’m doing can be handled in a Python script. It just opens that Excel file and says, oh, I don’t even need to mess with Excel, I can write a very quick script in Python, and I can do that with a very cheap model for only 5,000 tokens. Maybe it’s a question the AI can actually solve within its context window — it doesn’t even need to go to Excel, it has enough information already, and it can process and reason and tell me the answer, consuming only 2,000 tokens of the best model. Or maybe it decides this request is actually better for a human to handle, or it’s a personal request, and instead of using any tokens, it should just tell the human to do it. Now it’s free, because the human is doing it, not the AI. So now it costs zero tokens.
This is a very simple example of one task, with different quality of models and different techniques. How do you know which model is best and which is most efficient? Because, as we’ve seen, companies cannot have unlimited budgets for infinity tokens. So we’re going to have to start thinking about which tokens we use — which are the expensive tokens, which are the medium, which are the cheap, and which ones are used for which jobs.
And it goes even deeper than this. We also have to factor in where the AI model is running. Cloud-based tokens cost money. Maybe on-prem tokens are free, because I’ve got some GPUs — but do I have enough horsepower? What models can I run locally, and can they handle that? Maybe we even see distributed. There are GPUs inside laptops now, models are getting more and more efficient, and laptops are getting better. So is there a point where I have models running on my laptop, and the job can be routed down to my laptop where it’s free, because I’m not paying per use? Maybe I want to use my laptop. Okay, I love that idea. Except — wait a second. Do I trust that device? Is it a corporate-owned laptop or a BYO laptop? If it’s BYO, do I have device posture understanding — is this device trusted or not? Because I probably want to look at my query and see, is there PII in here? Are there corporate secrets? I only want to give it to the laptop where it’s, quote, free, if I trust both the laptop and the data enough to do that.
So you can envision a very complicated routing and performance-management machine that has to sit in line with the work, and it’s going to be critically important as organizations adopt AI. And, again, all the core skills are fundamentally what we as end-user computing and IT professionals have been doing for decades. So I think that doesn’t really change.
What else do we do in end-user computing? How about endpoints? We do endpoint management. Today endpoint management is endpoint management. Tomorrow, I think, is endpoint management, because we can’t yell at the cloud yet — there’s some endpoint that it goes through. It’s interesting, though: with AI, endpoints were desktops, laptops, mobile devices, that’s what we traditionally used. When it comes to AI, remember we talked about AI as a fleet — step five was a fleet. With all the AIs running, they can be running on different devices, different clouds, applications. But remember, for me as the worker, really from step three onward, I’m just talking to an AI. So right now, when I use a laptop, a desktop, a phone, I’m using a different interface connecting to the same data on the back end. But as AI does more and more, I’m really only interfacing with my AI.
So, yes, the AI will transform what it shows me based on what I’m doing, what I need, what’s in front of me — but it’s almost more like the AI is creating the user interface on demand just for me, based on where I am, what I need, what kind of information. So if I’m wearing smart glasses and walking around — maybe I’ve got my watch, my smartwatch, my glasses, my earbuds, which have little mini neural processors that can process things — basically anything with a microphone and a speaker, I’m talking to and interacting with the AI. And if it needs a screen, I’ll go find a screen. Maybe if it needs a screen and there’s a TV nearby, bing, show it to me on the TV. I’m talking to my watch, the TV is connected via some secure portal, and it’s showing me the application I need, and I’m just talking to it. The AI is changing around what it needs, interacting and showing me what’s going on. Maybe the same is true of my car — maybe it’s running on the screen, maybe on the heads-up display, maybe audibly in my ears. Maybe these electronic billboards become little personal TVs. As I’m driving by — you joke, but why not? I’m talking to my car and it’s my AI, and it’s like, oh, here’s a preview of the slide, and then I look out the window at that billboard over there, and there’s a slide I need. Like, what do you think, Brian? And I’m like, ah, move the title to the left a little bit.
Because, really, it doesn’t matter what device you have, AI is going to be there. And I don’t mean AI on the device in a distributed way — yes, that will be a thing, but that’s not as important. I mean the AI itself is going to manifest in whatever form it needs to, given the moment, where you are, what you need at that moment. And if you’re not in the right modality where you can use it, where you really have to sit down and think about things, then it’ll flag that and bring it to you next time you’re in a space where you can do that. So I think the core of endpoint management — the thrust of having secure endpoints, BYO versus corporate-managed, zero trust, secure lockdown — is going to be very critical in the future. But the applications are going to be more like you’re just talking to your AI and it’s generating the UIs you need, wherever that is. Fundamentally, a lot of what you do in endpoint management today is probably going to be similar in the future.
If I look at the Citrix receiver, Citrix workspace — client agents that IT manages — I think those become the cognitive workspace in the future. So maybe instead of icons for applications, I’ve got icons for the tasks I need to work on. I’ve got icons for the different knowledge sources and what’s going on. There will be some workspace where my context and everything is pulled together. I don’t know what that looks like exactly as it evolves, but, again, it’s going to be something we’re familiar with. I just want to highlight again, the cognitive workspace — this is not just about the apps, it’s about the knowledge and working and skills being brought together and delivered to where that work needs to happen, whether that work is being executed by an AI or by a human.
The control plane — I think that doesn’t change either. What the control plane means to you, we will still have control planes in the future. Something has to manage all this: the humans, the agents, the cognition, where it’s running, where the models are, where it’s going, the security — how I enable, disable, route, all that we do today, we are going to do in the future. So I’m not super worried about it.
To put this all together: all of these things we do today have some version of what they’re going to do tomorrow, even in this very far-out-looking, AI-first world. And I think the fundamental is that what we do is going to get bigger. We’re always pushing ourselves as technology evolves; what we do gets better. I started my career in IT in 1994. What was I doing in ‘94? Tape rotation, tape management, just so much administration. There’s so much we don’t do today, but it’s not like IT jobs went away. Everything went to the cloud — I’m not unboxing and racking and stacking servers anymore, but we’re busy in IT. So some of these things might go away, because AI can do some of it for us, but you start to get into all the cognitive observability, the cognitive workspace, the layering and cognition management, and the security there — there’s going to be a lot of very complicated things we can’t imagine. So what we do is going to get bigger.
Because at the end of the day, workers still need to work. Whether that’s AI workers or human workers, workers still need to work. Work still needs to happen somewhere, and somebody has to make that somewhere work. They need to do that in a safe way, in an observable and secure way, and in a cost-effective way. And, as I said, I think that someone is you. It’s me, it’s you, it’s us.
Book one and book two
Because this presentation, I called it the last chapter of EUC. And the thing is, it maybe is the last chapter — but it’s the last chapter of book one. EUC book one covered the past 30 years. Book two is going to cover the next 30 years. Our first book of end-user computing closed, and we’re like, wow, that was something. And then we discovered there’s a book two, with a whole new universe being created.
And that new universe — going back to my slide from before — if you look at what knowledge work is, I said a small portion of knowledge work is visible, but most of it has been invisible. I think everything we’ve done in EUC for the past 30 years has been on the visible portion of knowledge work. But book two, our next 30 years in EUC, is going to be on this invisible portion. The true knowledge, the thinking, reasoning, judgment, skills, taste, staring out the window looking at birds — all of that is what’s going to be in book two.
And if you want to get a head start, and you want to look at, okay, book two, what’s on page one? I think page one is going to start on stage three of this progression I gave you. Stage three was cognitive extension, using AI like a second brain. I think truly it’s this step right here, where you take all your individual files and move them into this context library and start using AI truly as your primary interface into the way you do knowledge work — which, again, is real. I’ve been doing that myself since January, for six months now. There’s a QR code here that leads to a starter prompt you can use.
In this step, this is where you build your own second brain, because you have to feel and understand your own second brain before you can govern it for others. You have to manage one before you can manage thousands. The first step for book two is you have to get to using AI like a second brain. Once you do that — for me, it was within two hours I thought, oh my gosh, and within a day or two I’m like, I’m never going back. And then, once you’re in phase three, you can start to really understand and think about: okay, now what happens when I connect this to my apps? Now what happens when I connect it to other AIs? Now what can it do without me? The whole road in front of you becomes revealed. And then you can work on advocating that for your coworkers, for the people at your company, and you start figuring out how you need to manage it and how you protect yourself in this career.
So I do not know what book two is going to be called, but I do know that we — you watching this, me, us in end-user computing, us in IT — we are the ones who are going to write book two. So that’s my talk. That is the last chapter of end-user computing, which also leads us into the first chapter of book two, whose title I don’t know yet.
Final thoughts
I want to share a couple of resources with you. This is part of the Citrix AI Hotsheet podcast. This is episode two; there will be a new episode next week with me and Dave Brear talking about more things specifically on our minds as AI impacts knowledge work and end-user computing.
I want to give the shout out I mentioned to the blog — citrix.com/blogs is where I’m writing pretty often, and it is legitimate blogs on these topics. As I said, it’s not a marketing thing. And you can also look at my personal website, bmad.com, which is where I list everything I’m doing. So if you want to see talks, speeches, the link to the second brain, articles, interviews, podcasts, all the stuff I’m doing around this space, go to bmad.com and you can track it all there.
With that, thank you very much for your time today. I’m truly excited about the future that’s in front of us within end-user computing. AI is fascinating, it’s really an interesting time. I’ve been working 32 years. I don’t know if I have 32 more, but I’m sure going to try. So, thank you so much. Talk soon.