Speaker 12026 is undisputedly the year of agents. For years, we were talking about these things, but now they are actually here and they are changing how we do work, at least on an individual level. The thing is, not all of our work happens on an individual level. Most of us, in fact, split our work pretty comfortably between work that we do on our own and work that we do in teams. So far, agents have really only been able to impact about half of that equation. I believe strongly that that is about to change and that the best, most dynamic AI-using teams are going to shift from single-player AI to multiplayer AI. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Blitzy, Harbor, and HyperAgent. To get an ad for this podcast, for a free version of the show, go to patreon.com slash ai-daily-brief, or you can subscribe on Apple Podcasts. And to learn more about sponsoring the show, send us a note at sponsors at ai-daily-brief.ai. Finally, last call, and blissfully, for those of you who are not signing up, the last time that I will be yapping at you for a while about our super-intelligent agent executive programs, the next cohort starts this week. So one last time, you can check them out at training.bsuper.ai. Welcome back to the AI Daily Brief. The day that this episode comes out is Labor Day in the US. The training day is Labor Day in the US. It's the traditional end of summer and the beginning of back to school and back to work. This is one of those inflection moments where a lot of folks come back to the office, whether it's virtual or real, reinvigorated and ready to crush out a couple great months before the holidays descend. In fact, I think in many ways, outside of New Year's, this is the time where I see the most excitement around new ways of working on an individual and a team level. Now this year, Labor Day also happens to fall on my birthday. And I thought that I would give all of you guys a present. So far this year, we have received four free self-directed learning programs. We kicked off the year with the New Year's AI Resolution, a 10 week, 10 project adventure, which was really meant to provide a very broad basis of basic core AI skills that were notably in general, pretty pre-agentic. Agents would come a little bit later. In February, we released Claw Camp, which was a zero to agent team program that was not simple at all, but which gave people a guide to diving into this new crazy agentic world that had been enabled by OpenClaw. AgentOS came just a little bit later. And was the more mature grownup version of Claw Camp that was not only platform and tool agnostic, but helped people build not just a single agent or even an agent team, but an entire agentic operating system capable of taking on increasingly complex work. Finally, the AI Summer Adventure was a choose your own adventure style program that provided a bunch of fun skills at a variety of different levels to people who wanted to stay sharp over the summer. Now, there are a few things that all these experiences have in common. They were all free self-directed project-based learning experiences. The whole goal of which was to provide a framework for you to actually go do this work. The pedagogy behind them is pretty simple. It's that to learn how to use AI, you just have to use AI. They're all kind of anchored in the truth that at this point, there are still no AI experts. There are just people who have practiced with it more. But the other thing that they all are ultimately and at core is individual. Now with AIDB New Years, you could form a team, but it was a team only in the sense of mutual support. People going through an individual experience in parallel. All the other programs follow the pattern that pretty much all agentic work has so far, which is people building and leveraging individual agents for their individual work. The thing is, not all of our work happens individually. In fact, for most of us, some major and meaningful portion of our work happens as part of a team. A recent survey of around 16,500 office workers found that something like 39% of the workday is spent working alone, while 42% of the workday is spent working alone. Another survey found that 57% of time is spent communicating, i.e. meetings, email, chat, etc., versus 43% creating individually. And yet another survey found that about 60% of time goes to work about work, communication, search, coordination, and process. In other words, a significant portion of knowledge work happens on a team, and the majority of knowledge work runs through team context. Collaboration, meetings, email, chat, coordination, search, those are the substance of a huge amount of work. And yet, so far, the vast majority of agentic efforts have been entirely personal. Think about all of the early experiments that you've heard about. It's all people building their agent teams, their researchers, their writer agents, their coding agents, their personal chief of staff agent. Advanced users are, yes, experimenting with agent teams, but it's agent teams that serve only the individual. Now, to be clear, I don't think that's going away. I think the fact that all of us now gets to be a manager of a big, extensive team of agents that themselves can spawn sub-agents to do lots of different work is now just part and parcel of being an effective knowledge worker. However, what I don't think that has changed is the fact that much of the meaningful work that we do will not be in our own individual silos, but at the intersection of where we work with other people. And my strong, strong belief is that the next frontier of agent design is going to move agents from the front of the team, from the front of the team, from the individual silos in which they have operated so far, to the shared spaces that teams inhabit together. That means team-owned context. Not repeated context where everyone's individual folders have the same documents in them, but one single repository that is shared across the team. It means shared sessions, observable work, and live steering and handoffs, where the agent is working, again, not in the solo of someone's individual computer, but in some sort of shared environment where multiple people can work together. Where multiple people can have input, all at the same time and in the same session. This is the move from single-player AI to multiplayer AI, from individual AI to team AI. The move from single-player to multiplayer AI is a move from private outputs, where teammates can only see the final answer, to visible work, where everyone can see what the agent is doing. It's the move from feedback prompts, where teammates can only interact after some output has already happened, to live participation, where teammates can redirect, annotate, and join while the work is happening. The shift from single-player AI to multiplayer AI is a shift from personal memory to shared context, where the durable context belongs to the team, to the channel, or to the project. And ultimately, the shift from single-player to multiplayer AI represents a shift from agents being about only individual leverage to becoming team capability, not just personal efficiency tools, but agents as reusable organizational infrastructure. Now, as some evidence that this is, in fact, a trend and not just something that lives in my head, I think that there are a few examples from the last 90 days or so that have made this pattern pretty clear. One, which I don't know, it might actually have been longer than 90 days ago because they're pretty ahead of the curve. At the beginning of the year with the ClawCamp moment, the team at Evry started their agentic experimentation by having everyone have an individual agent that was the mirror of the person who owned that agent. Before long, they realized that that wasn't really how work got done, and they started to shift to a model that had more agents in their shares. They started to build shared spaces, doing work that intersected the teams. A big product moment for this, and this might be the clearest product expression of multiplayer AI so far, was when Anthropic introduced Clawed Tag. Clawed Tag lives inside Slack, but it's different than the previous implementation of Clawed in Slack that existed before. The original Clawed integration in Slack was one where an individual could tag in their personal Clawed, bringing them into a conversation, and then sending them off to do things and managing their personal Clawed from that work interface. Clawed Tag was something different. Clawed Tags are instances of Clawed that are shared across an entire team as expressed by specific channels that can each have their own context, tool access, data access, or other types of access that allow that shared Clawed to do the work. In other words, when you tag in Clawed now in your coding channel, it is not your personal Clawed that you're tagging in, but the shared Clawed agent that works across your team. The team at Anthropic wrote, "Tagging Clawed comes with a few new advantages. @Clawed is multiplayer. Within a given Slack channel, there's one Clawed that interacts with everyone. That means that anyone can see what it's working on and can pick up the conversation from where the last person left off. This makes tagging Clawed very different from working within a single chat or for a single task. It's much more like interacting collaboratively with a teammate. Because Clawed is multiplayer, it can also learn over time. It follows along with the channel that it operates on, but it also creates and builds more context about the work. Because it is operating in that shared team multiplayer space, each individual user that comes in and needs something doesn't have to explain things over and over again from scratch. The multiplayer team shared Clawed also has the ability to take initiative. Teams can enable a setting that allows ambient behavior that proactively interacts with the information from the channel that might be relevant for its type of work. Now, at this point, Clawed tag is only just starting to dissipate across teams, but certainly for Anthropic themselves, it has fundamentally changed the way that they work. In fact, in that same announcement post, they wrote that 65% of their product team's code was now created by their internal version of Clawed tag, i.e. not a bunch of individual developers using their own Clawed agents to contribute PRs, but a shared space with that shared agent working between them that's pushing nearly 2/3 of their code. Another recent example of this pattern comes from the Open Claw 2.0 launch. After pushing relentlessly for the first part of the year, delivering an update every couple of days, the OpenClaw maintainers went quiet for about seven weeks to coordinate around a much bigger and more extensive rebuild. Initially, they tried to collaborate by having all of their different individual agents work together in Discord. But what they found is that that still wasn't collaborative enough. One of the company's maintainers, Colin, wrote, what we wanted was a way for both developers to see the work itself. If an agent paused because it needed clarification, either of us should be able to jump in. If something needed a second set of eyes, we should be able to open the same session and look at the same context. No screenshots, no copied transcripts, no here's what the agent has done so far data dump. Just open the work and continue. And so they ended up building a new multiplayer web UI for OpenClaw that allowed for exactly that. Colin continued later, the feature that made the difference wasn't simply seeing another avatar online. It was being able to share a session while work was happening. When something needed another opinion, we could both open the same thread. When the agent needed information one of us had, that person could add it directly. There was no need to copy the conversation into Discord, explain what happened and then carry the answer back. We were working inside the same context. That sounds like a small interface improvement, but it changes the way you collaborate with an agent. The session stops being a private conversation between one developer and a model. It becomes a shared piece of work another trusted developer can inspect, steer, or take over. A new study from KPMG in the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes. Researchers studied more than 500 early career professionals and found that the best performers consistently amplified the value of AI by guiding, evaluating, and refining its outputs. These top performers, called AI amplifiers, weren't defined by what they knew alone, but by how they worked with AI. Learn more about what separates AI amplifiers from everyone else at KPMG.com. Learn more about what separates AI amplifiers from everyone else at KPMG.com. Learn more about what separates AI amplifiers from everyone else at KPMG.com. 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If you do choose to use our tool, it can contribute to a shared space that is just for you and the rest of the members of your team, where you can see all of the answers to how people are using AI all together in one single spot, kind of creating a living artifact of the type of thing that you would discuss in that shared meeting. Now, for each of these different weeks or different sessions, we wanted to give people a lot of different options. You can, as I said, do everything inside of our platform, but if you have security considerations that you don't want to deal with, you can also just download the worksheets and do them in your own spaces without ever touching or leaving, anything about your team on our site. You are also welcome, of course, to simply take inspiration from this and do your own version of it. I don't care at all about you doing the work in our space. All I care about is you getting ahead of everyone else by understanding that multiplayer AI is coming and getting there first. Now, some of you might be thinking to yourselves, what if our team is still really nascent and actually doesn't have a lot of AI agents running yet? Does that mean we shouldn't do this inventory week? The short answer is no, and I think that there are two valuable things that you could do with it. The first is the other type of inventory that you could take would be to explore what your barriers have been to see if those are things that your team as a group can address to make this multiplayer AI a little bit easier than the single player AI that hasn't happened yet. The other approach, however, that I really like is to go find someone or some set of people in your company who are advanced, even if they're not on your team, and ask them to present to your team how they're using AI in agents, presumably within the same guardrails and constraints and governance protocols, that your team will face as well. That can be a great way to see what power users inside your own company are doing, even if that's not exactly what your team's experience is so far. Week two, or sprint two, is all about context. Remember, one of the key things that we're going to be doing as we move from single-player AI to multiplayer AI is moving from individual memory to shared context. But to do that, the members of your team kind of have to figure out what that shared context repository needs to know. So in the second sprint, we have a tool that can help you extract context about yourself that can be shared with the team. Or again, you can download the worksheet and do that in some other shared space. Again, there's an individual and a sharing component of this. You write down your context on your own with the assistance of our AI or some other AI, and then you bring it together with your team to put together that shared repository. Week three, we move from figuring out what context we share to mapping out the areas where our work actually overlaps. Now a note here, the multiplayer AI sprint is assuming nothing about how we're going to be able to do that. So we're going to have to figure out how we're a team's understanding of itself is. It is absolutely possible that some teams will right out of the gate, even as you are listening to this, know exactly what shared work you want to focus agents on. And so to the extent that that's the case, you certainly don't have to go through this process. All of this is just designed as an asset. So you can go from zero to multiplayer agents inside the context of this sprint. But the idea of this sprint or week three of discovering the shared work is once again, to use AI to do a short interview, that walks you through your common work and maps your work streams. For each recurring work stream, the interview is going to ask what it is, who else touches it, what context it needs, and how often it's run. When you have work that overlaps with others, that becomes a candidate for where a shared agent could live. And from there, we're going to give you a framework for how to score a candidate. I suggest looking at it across four different dimensions. The first is shared need. How many people need the same context for this? Obviously, more is a better candidate for a multiplayer agent. And so we're going to give you a framework for how to score a candidate. The second is a staleness cost. How much does it hurt when each person's version drifts? The third category is permission sensitivity. How much restricted data does it touch? And the fourth is checkability. Can you tell quickly whether an agent got it right? A simple way to figure out where you want to start experimenting would be to score each of those axes on a one to five scale, and then squint at the ones with the highest scores as your potential candidates. Now, the fourth and final part of the sprint is actually one that you can use. The idea is to ship and start to use one shared agent on whatever tools you already have access to, loaded with your team context, used by at least two of you on real work. Now, certainly if you have access to CloudTag, that's a really easy way to test this, but we'll have some ideas for other ways that you can set up a shared agent as well inside the system. The goal then is to test one shared agent, give it some amount of time to see if and how it changes the work, and then ask if it actually improved things. It may be that some use cases, despite the fact that it's not there being a lot of shared overlap between you and others, just aren't the right shape for an agent to get a lot of value currently. That's okay. That means you move on to the next shared use case to see if an agent can help there. And again, theoretically, after a single rotation of this, you can be done if you'd like, but you can also run this basically repeatedly, working through each potential space that you uncovered that could be a good fit for a shared multiplayer agent. So that's the idea. As with all of our programs, this is just meant to provide a loose new out through experimentation. When you sign up, you'll get an invite code that you can share with other members of your team to make sure that you're all working in the same shared space together. And if you choose to share your artifacts, can all resolve to the same spot. Now, if you'll permit me, I would also be remiss at this point to not note that if you are sitting here feeling a bit overwhelmed, and like you don't exactly have the foundations even on single player AI to really be effective with this, I would recommend either A, going back through some of our programs, or if you want even more support than that, checking out some of our paid executive programs, specifically the executive catch-up program and the executive agent leadership program. In particular, the agent leadership program is going to leave you in the top 1% of people who actually understand how to build and interact with agents, which obviously gives you a huge advantage as you shift from thinking about single player agents to multiplayer agents. And the next iteration of that program is actually starting this week, just following Labor Day. There's a link, of course, to that in the show notes. The important thing to me here is that it seems fairly obvious to me when I squint at it that this is the direction things are headed. Again, it's not that you won't use individual agents. You absolutely will. And not just one or two, but you will probably have entire fleets and teams that you manage. What I'm convinced of, though, is that that only represents one part of the work that we do. And so it is only natural that we start to figure out agents for the other part of work, which is the work that lives between us. I think that if you and your team work your way through this, you're going to be able to do a lot of work. And I think that you will be in a significantly better position to use these new multiplayer AI tools as they come online and be a leader in this exciting new world of team and multiplayer AI. Again, you can find all these links at ai-dailybrief.ai or go direct to multiplayerai.ai. Yes, that is two AIs in a row. And I'm excited to join this experiment with all of you. We'll also set up a community for this on the AI DB operator circles. So look for a link for that in the show notes as well. All right, guys, for now, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching. As always, and until next time, peace.
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