So, I was looking at my chat history the other day and realized it is basically a digital graveyard of one-on-one therapy sessions with different models. It is just me and Claude, or me and G P T, trapped in these little private bubbles. I have thousands of threads where I am explaining my problems, my code, and my life to a machine, but none of that knowledge ever leaves the room. It is the loneliest way to be productive. But today's prompt from Daniel suggests that this whole single-player era might finally be coming to an end. He is asking about the transition to true multi-user A I chat, where a whole team actually shares a single conversation and a single brain.
Herman Poppleberry here, and honestly, Corn, Daniel is hitting on the biggest architectural shift we have seen since the original transformer paper back in twenty seventeen. For the last few years, A I has been this solitary tool, right? You have your own account, I have mine. If I want to show you what the A I said, I have to copy-paste it into Slack or share a link like it is nineteen ninety-nine. It is incredibly fragmented. But here in late March twenty twenty-six, we are seeing the walls finally come down. We are moving from the era of the personal assistant to the era of the team member that actually sits in the room with everyone, listening and contributing in real-time.
It is about time. I mean, it always felt a bit inefficient that we are all using these incredibly powerful brains in total isolation. If we are working on a project together, why am I the only one who knows that the A I suggested a specific architectural path? I then have to go and summarize the A I's summary for you, which is just a recipe for losing information. It feels like the early days of document editing before Google Docs, where you were emailing version five point two final final to everyone and hoping nobody edited the wrong one. We have been stuck in the version control dark ages of A I.
That is a great way to look at it. The industry is calling this the multi-player shift. We have seen a massive wave of releases just in the last few weeks that prove the big players are all-in on this. OpenAI finished rolling out their Group Chats globally just this month, Google integrated Gemini directly into the shared spaces in Google Chat back in February, and Microsoft just updated Copilot Groups to handle up to thirty-two people in a single thread. It is a fundamental change in how the model perceives the user. It is no longer trying to satisfy one person; it is trying to facilitate a group dynamic. It has to understand social hierarchy, consensus, and collective intent.
But wait, if there are twenty or thirty people in a chat, how does the A I even keep track of who is saying what? I struggle to follow a group chat with five of our cousins when they all start talking about Sunday dinner, so how does a model manage a million-token context window without just turning into a confused mess? Does it get overwhelmed by the sheer volume of different voices and conflicting opinions?
That is actually where the technical breakthrough of G P T five point four comes in. It was released on March fifth, and that one-million-token context window is the secret sauce that makes this viable. In older models, if you had twenty people talking at once, the history would fill up so fast that the A I would hit its limit and start forgetting the original goal of the conversation within an hour. You would be halfway through a project and the A I would forget why you even started. With a million tokens, you can have weeks of team deliberation, document drafts, and feedback loops all staying live in the model's active memory. It does not just see the last three messages; it sees the entire evolution of the project from day one.
I can see you are vibrating with excitement over the token count, Herman, but let us talk about the human side. If I am in a group chat with twenty people and at ChatGPT is there, is it just lurking? Does it only speak when spoken to, or is it actually contributing like a sentient project manager? Because there is a fine line between a helpful assistant and a digital stalker that jumps in every time someone makes a typo.
It depends on the platform, but the goal is what Microsoft calls Agent Mode. In the latest Copilot updates, the A I is not just waiting for a prompt. It is actually reasoning over shared mailboxes and SharePoint lists in the background. So, if the team is arguing about a deadline, the A I might jump in and say, hey, according to the shared calendar, three people are out of the office that week, so maybe we should aim for Tuesday instead. It is moving from a reactive chatbot to an active participant in the workflow. It is looking for gaps in the conversation and filling them with data that the humans might have missed.
That sounds both incredibly helpful and slightly terrifying. I am not sure I want a donkey-brained A I telling me when I can take a nap or pointing out that I missed a meeting because I was playing video games. But seriously, the privacy implications here seem like a nightmare. If the A I is part of a group chat, does it know everything about me from my private chats? Does it start leaking my personal search history or my private complaints about the boss to the whole dev team because it thinks it is being helpful?
That is the big hurdle, and it is something the engineers call Memory Isolation. This is a huge part of the enterprise-grade multi-player experience. Platforms like OpenAI had to build a specific firewall between what they call personal memory and group context. When you are in a group chat, the model can see the history of that specific thread, but it is explicitly blocked from pulling in your private data from other conversations. It has to be that way for enterprise safety. You cannot have a tool that accidentally reveals a manager's private feedback about an employee just because they are both in a project thread together. If the A I says, well, based on your private chat yesterday you were feeling unmotivated, that is a lawsuit waiting to happen.
I will believe that when I see it. We have seen enough prompt injection attacks and data leaks over the last year to know that those walls can be thin. But let us look at the startups, because they usually move faster than the big three. Daniel mentioned Adapt and Poe. I know Poe has been doing the multi-model thing for a while, but what is Adapt doing differently that makes it a multiplayer agent?
Adapt is really interesting because they launched what they call a multiplayer agent for Slack in February. The core difference there is ownership. In a standard setup, if I start a chat with a bot, I own that conversation. If I leave the company or change departments, that context might vanish or be locked behind my account. With Adapt, the conversation is owned by the channel itself. Any team member can refine or extend the A I's work. It is persistent, collective knowledge. They are treating the A I as a shared infrastructure rather than a personal tool. It is like having a communal brain for the office that stays even when the people change.
I can see why that would be a massive productivity boost. I saw a report from a startup called AiZolo—pronounced Eye-Zoh-Lo, for those following along—that claimed teams using these collaborative workspaces were finishing projects thirty-five percent faster. That is a huge number. If I can get my Friday work done by Wednesday afternoon, I am interested. Is that thirty-five percent just marketing fluff, or is there a real technical mechanism behind that speed?
It is likely real, and it comes down to the A I handoff problem. We actually talked about the technical side of handoffs back in episode eleven hundred twenty, but in a multi-user chat, you eliminate the friction of catch-up. Think about how much time is wasted when a new person joins a project and someone has to spend two hours explaining everything that has happened. In a multi-player A I environment, the new person just asks the A I for a summary of the last three weeks of decisions, and they are up to speed in five minutes. That thirty-five percent gain is basically the death of the status meeting. The A I maintains the state of the project so humans do not have to.
You are speaking my language now. Anything that kills a status meeting is a win in my book. I would trade a lot of things for an extra two hours of my life back every Tuesday morning. But what about the bot sprawl? My Slack is already a disaster zone of different integrations, notifications, and random bots that nobody remembers installing. Now every team member is going to have their own favorite bot in the shared channel? It sounds like a digital version of everyone shouting over each other in a crowded bar.
That is a major controversy right now. Organizations are struggling with what people are calling A I sprawl. You have one guy using a Qwen-based bot—pronounced Kwen, by the way—another person using a Claude wrapper, and the manager trying to force everyone onto Microsoft Copilot. If the bots do not share context with each other, you just end up with fragmented silos of information inside the same channel. It defeats the whole purpose of having a shared brain. You end up with five different A I assistants all giving slightly different advice based on different data sets. It is chaos.
So the solution is probably these enterprise platforms like Okara and Juma that Daniel mentioned? I assume they are trying to be the one ring to rule them all for team A I?
Well, not exactly, because I am not allowed to use that specific movie reference without a license, but you are on the right track. Okara—pronounced Oh-Kar-Uh—and Juma are focusing heavily on Role-Based Access Controls, or R B A C. This is the boring stuff that actually makes technology work in a big company. It allows a C T O to say, okay, the A I can help the dev team write code, but it is not allowed to see the salary spreadsheets in the H R folder, even if they are in the same general workspace. You need those granular permissions before you can truly let an agent loose in a team environment. Without R B A C, multi-player A I is just a security breach waiting to happen.
It is the difference between a toy and a tool. If I am a business owner, I do not care how smart the A I is if I cannot control what it can see and do. But speaking of control, I saw some drama with Anthropic recently. People were getting cut off in the middle of collaborating because of usage caps? That seems like a massive problem if you are relying on the A I to be your team's memory.
That was a total mess in early March. Claude users were hitting their weekly limits mid-project. Imagine you are in a deep flow with your team, the A I is helping you solve a complex architectural bug that has been haunting you for weeks, and suddenly a window pops up saying you are out of tokens for the next four days. It is a total momentum killer. Anthropic tried to fix it with a two-times usage offer for off-peak hours, but it highlights the resource problem. Running these massive, multi-user, long-context threads is incredibly expensive in terms of compute. We are hitting the physical limits of the hardware when we try to give everyone a million-token shared brain.
So the multi-player future is great as long as you can afford the electricity bill and the subscription fee. It is funny, we went from worrying about the A I taking our jobs to worrying about the A I hitting its data cap and leaving us to do the work ourselves. But let us dig into the agentic workflows for a second. You mentioned the A I taking actions based on group consensus. How does that actually work without it going rogue? If three people in a twenty-person chat say, yeah, we should probably delete that database, does the A I just do it? Because that sounds like a recipe for disaster.
That is where the safety layer gets really complex. In the current March twenty twenty-six implementations, there is usually a human-in-the-loop requirement for high-stakes actions. But for lower-stakes stuff, it is getting very slick. If the A I detects that the group has agreed on a task, it can automatically generate a Jira ticket, update a shared spreadsheet, or draft an email to a client. It is looking for linguistic markers of consensus. It is not just a chatbot; it is a listener that understands the social dynamics of the group. It is basically a very high-speed secretary that never sleeps.
I wonder if it can detect when I am being sarcastic. If I say, oh sure, let us just move the deadline to tomorrow, is the A I going to update the project tracker and ruin my life? Because I say things like that at least three times a day.
We are getting there, but sentiment analysis is still a bit of a moving target for agents. What is really impressive is how these tools are being used for brainstorming. There is a feature in the new Google Gemini integration for Google Chat where it can retrieve project details buried in months of history. You can ask it, what did we decide about the color palette back in January? And it pulls the exact messages and the reasoning behind them. It becomes the team's long-term memory. It solves the problem of that one person who always says, I think we talked about this, but I cannot remember what we decided.
That is actually huge. My own memory is basically a sieve, so having a donkey-brained assistant that remembers every meeting note ever taken is a massive upgrade. But I want to go back to the productivity side. If teams are actually thirty-five percent faster, does that mean we are going to see a shift in how companies are structured? Do we need fewer middle managers if the A I is handling the coordination, the memory, and the status updates?
That is the big geopolitical and economic question of twenty twenty-six. If the A I is the most informed member of the team, the role of a traditional manager changes from information router to decision maker. You do not need someone to check in on everyone's progress if the A I is already tracking it in the shared thread and providing a daily summary. This is why experts like Fareed Mosavat are saying that multi-player A I will eat single-player A I. The value is not in the individual's output anymore; it is in the speed of the collective. We are moving toward a flatter organizational structure where the A I handles the overhead.
It feels like we are moving toward a world where the A I is the glue holding the team together. But I worry about the loss of that individual creative spark. If everyone is just prompting the same shared brain, do we all start thinking the same way? Does the team's output just become this homogenized average of the model's training data? If we all use the same assistant, do we all end up with the same boring ideas?
That is a valid concern. If the A I is always suggesting the path of least resistance or the most statistically likely solution, you might lose those weird, off-the-wall ideas that actually lead to breakthroughs. The best teams are going to be the ones that know when to listen to the A I's consensus and when to intentionally push against it. It is a new kind of skill—knowing how to disagree with a machine that has a million tokens of context telling you that you are wrong. We talked about this a bit in episode seven hundred ninety-five when we looked at sub-agent delegation. You have to maintain that human oversight or you just become a rubber stamp for the model.
I am pretty good at being stubborn, so I think I will be fine. But for everyone else, this seems like a lot to navigate. If I am a team leader right now, and I am listening to this, what is the first thing I should actually do? Because right now, my team is probably using five different bots, three different Claude accounts, and a dozen private ChatGPT accounts that I do not even know about.
The first step is an audit of that A I sprawl. You have to figure out where the data is actually going. If your engineers are putting proprietary code into a personal bot that the rest of the team cannot see, you are building a massive amount of technical debt and a security risk. You want to move toward a unified collaborative workspace. Whether that is Microsoft Copilot Groups or a specialized platform like Okara, the goal is shared memory and role-based access. You need to centralize the intelligence so the whole team benefits from it, rather than having it locked in individual silos.
And I suppose we should mention the technical protocols. We did that deep dive in episode eleven hundred twenty about standardized protocols for A I handoffs. If you are moving to a multi-user system, those protocols are even more important. You cannot have a handoff between a human and an A I if they are not speaking the same language about the status of a project. If the A I thinks a task is done but the human thinks it is just in review, the whole system breaks down.
If you do not have a standard way of labeling tasks or defining completion, the A I is just going to get confused. It is like having a nurse finish a shift without telling the next one about a patient's allergies. In a multi-user A I chat, the A I needs to know the current state of every moving part, and that requires the human team to be disciplined about how they communicate. You have to treat the chat as a source of truth, not just a place to hang out.
Discipline? In a group chat? Good luck with that, Herman. I have seen what happens to a Discord server after three days of people posting memes and talking over each other. It is absolute chaos. But maybe the A I is the one who can finally bring order to the madness. If it can summarize the last fifty messages of nonsense into three actionable bullet points, it might be the greatest invention in human history.
That is actually one of the most popular features in the new Gemini for Google Chat. It can summarize missed discussions in a shared space. So if you go to lunch and come back to a hundred messages about where to get pizza, you do not have to read them all. You just ask the A I for the highlights. It tells you that the team decided on Pepperoni and the meeting is moved to two o'clock. It saves hours of mindless scrolling. It is about reclaiming your time from the noise of modern communication.
I need that for my family group chat. If it can tell me that Aunt Martha is just complaining about her cat again and I do not need to reply, I will pay any amount of money for that service. But looking ahead, where does this go? Are we going to see A I agents actually leading meetings? Are we going to have a donkey-brained bot as the C E O of a startup by the end of the year?
I do not know about C E O, but we are definitely moving toward a future where the A I is the most informed participant in the room. It has access to every document, every email, and every previous conversation. It will not be long before the A I is the one drafting the agenda for the meeting because it knows exactly what has not been resolved yet. We are shifting from A I as a tool to A I as a teammate. It is a subtle but profound change in the human-computer relationship.
It is a wild transition. We spent decades trying to make computers understand us, and now they understand our team dynamics better than we do. It is a bit humbling, honestly. But as long as it handles the Jira tickets and the status reports, I am not going to complain too much. I will take a digital teammate over a spreadsheet any day.
It is all about leverage. If a team of five can do the work of a team of fifteen because they have a shared A I brain, the economic implications are massive. But it requires a level of trust in the technology that we are still building. The privacy and memory isolation stuff has to be rock solid before people will truly let go of their private chats.
Well, I think we have covered the landscape pretty well. From the million-token context of G P T five point four to the specialized R B A C of platforms like Juma, the single-player era of A I is definitely in the rearview mirror. It is a multi-player world now, and we are all just trying to keep up with the pace of change.
It is a fascinating time to be in tech. The pace of change just in the last month has been staggering. I cannot wait to see what the landscape looks like by the time we get to the summer. We might be looking at a completely different way of working by then.
Hopefully, by then, the A I will have figured out how to make me a decent cup of coffee. But until then, I will settle for it summarizing my emails and killing my status meetings. Thanks as always to our producer Hilbert Flumingtop for keeping the gears turning behind the scenes and making sure we do not go over our own token limit.
And a big thanks to Modal for providing the G P U credits that power this show. They make it possible for us to dive deep into these topics every week without hitting a usage cap.
This has been My Weird Prompts. If you are finding value in these deep dives into the future of collaboration, a quick review on your podcast app really helps us reach more people who are trying to make sense of this A I-driven world.
We will be back soon with more insights, more technical deep dives, and probably more token counts. Goodbye for now.
See ya.
