7 Ways How We Use AI Is Changing

Seven shifts in AI interaction

0:00The way we use AI is changing. First, we were prompt engineers, then context engineers, then harness engineers, then loop engineers. And while it's tempting to view all of these things as just one silly buzzword giving way to the next, the reality is that we are all together on this journey figuring out just the best way to use a completely new technology that is changing both how we do our current work and opening up new possibilities of what work we can even do. Today we're talking about seven ways that how we use AI is changing. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

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0:59Today's episode is about a bunch of patterns that are shifts, I think, in how, on average, we interact with AI on a daily basis. One thing that's been true since the very beginning of ChatGPT is that our patterns of interaction with AI have never been static for long. At the beginning, it was people discovering new ways to prompt the AI that had better or worse results, which was then communicated through hundreds of articles, posts, YouTube videos, podcasts, and courses. But over time, the interaction pattern shifts have gotten more significant and more substantive. Over the

1:29last week or so, I saw a number of announcements that really reinforce some fairly big shifts in these interaction patterns. And while I don't think that how we use AI will ever be truly static, I do think that some of these shifts, which I'm going to cover in this show, are fairly fundamental and likely to stick. And thus, of course, if one is trying to be efficient with their time,

Simplified user interfaces

1:48may be more worthy of attention. So first up is an interaction pattern that is not so much us changing how we interact with AI, but product design decisions that are shifting the options that we have to interact with AI. Very clearly, there is a push towards simplification and integration and user experiences that have less rather than more cognitive decision making for the end user. One thing that's been interesting for me, sitting in this seat that I have, is the extent to which early AI has tended to defy traditional Silicon Valley logic that users won't show up unless

2:21you give them an incredibly simple and clear experience. In fact, quite to the contrary of that, a lot of the early adopters, not just from tech circles, but from broader knowledge work circles, have, rather than resisting complexity, thrown themselves in headfirst. A great example of this for me was when 9,000 people, most of whom weren't even close to engineers or really technical in any way, signed up for the free claw camp program I put out earlier this year, which basically had them crawling through glass to figure out how to build their own agents. And yet it appears that perhaps, even if the portion of people who were willing

2:51to do that sort of hard work has been higher in AI than in other past tech sectors, there are still a lot of folks who want that simplicity. Certainly, simplicity is one of the watchwords for the new MetaMuse product, and something that even the power users, who fully have the capability of dealing with complexity, are lauding in the product. How IAI's Claire Vo has discussed in numerous places how well thought out she thinks the MUSE design is, specifically around what it does and doesn't obfuscate from the user. Node.js creator Ryan Dahl, who again,

3:23certainly has no shortage of complexity capability, also shouted out MUSE for having nailed the simplicity. Wrote technologist Tom Goodwin, the beauty of MUSE is that they've managed to introduce it in a very accessible way. Don't tell me how you work, just show me how to do things. David Herman wrote, I think MUSE is going to win the casual AI race. I showed my dad, who is nearly 70, and it clicked. He was amazed that I could have it create an entire contract for me, update things on it, then email and send it to a customer all through texts. Clawed, OpenAI are great, but so far I'd say

3:53less than 1% of the population gets what these models are. Meta just made it all super easy. And it's clear that this was intentional. Meta Chief AI Officer Alexander Wang wrote, A big part of the team's focus when building MUSE was to make something that just worked. So much work went into all the little details to make the product simple and easy to use. And of course, the thing that makes this a trend, not just a design decision from the meta team, is that this sort of simplification is showing up everywhere. One of the biggest product updates that we got this week came when Anthropic announced that Clawed co-work and chat are no longer separate things. Clawed is careening towards an experience

4:27where instead of it being fragmented and full of decisions for the user before they even get started, instead a single unified experience. And again, the watchword was simplicity, simplification. And by the way, as pointed out by Clawed code creator Kat Wu, this simplicity extends not only to eliminating the difference between co-work and chat, but also to integrating Clawed design so that your standard core Clawed also has all of the capabilities to do things like make slides, designs, docs, without having to switch into some specialized experience. And again, the team at Anthropic reports that this was based on direct user feedback.

5:01Anthropic Labs lead and former Instagram co-founder Mike Krieger wrote, The most common thing we hear, people aren't sure which product to start with. That friction gets in the way of getting the best from what these models can do. Now, as I mentioned in an earlier episode this week, I'm not totally sold on this on a personal level because I feel like I like having more specific control, but boy, do I seem out of sync with the average here. Christian Selig reposted the announcement and said, Can we do this with Codex 2, please? The two modes are like having two parents fighting. Shanu Matthews summed up, People don't want to interact with two to three text agents, two to three chatbot agents,

5:34two to three terminals on desktop. You want a simple unified interface and want the agents to coordinate how it gets done. You simply want to ask and receive. The platform should coordinate and delegate agents, route models for tasks, handle whether it's local cloud or VM, be proactive and reactive about tasks. Instinct, Muse, and Grokbot really hit a nerve, and it's increasingly clear we're in another paradigm shift moment, in my opinion.

Monothreads and agent fleets

5:56Now, if the first change is about the simplicity of the interfaces through which we use AI, the second change is itself also a version of simplicity that has to do with how we control AI. And this is the trend towards monothreads. The idea of a monothread is that instead of having to switch context windows and explain new needs and new prompts separate from the previous things that you've done, instead you use a single long continuously running thread that inherits the context from past conversations as a way to manage the entire experience. Now, previously, this was limited by

6:28the context window. At some point, the AI would get tired, i.e. its context window would get filled, and it would start doing weird things, making errors, losing instructions, etc. Many of the earlier courses and lessons that I did from late last year and early this year were all about how to build good handoff documents to move context between different threads. That started to shift this year, though, especially as the team at Codex really focused on compaction to change the way that people could interact specifically with Codex through a single unified thread. Codex's Nick Bauman wrote back in April, so much coding agent design is built on the assumption that breaching

7:03context windows and compacting context yields progressively worse results. When you drop this assumption, the product direction it opens up is very exciting. He was responding to developer Anthony Kroger, who had written, I literally never worry about context windows using Codex. It can compact like three times and the model still remembers the details somehow. In a post about a week later, Nick from the Codex team expanded on his thoughts in a piece he called My Codex Threads Are Alive. In it, he wrote, The most useful Codex thread I have right now is the one I've been using for the last three weeks.

7:34Every hour, it checks my Slack, Gmails, and PRs I wrote or am watching. Every hour, it turns the noise into clear signal I can act on. My Codex usage has shifted from starting lots of short-lived chats to keeping a smaller number of threads alive around recurring work streams. I still start fresh threads constantly, but some work should not reset every time I ask a question. In some, he writes, I have become monothread pilled. Nick continues, I recently said that the compaction improvements made by the Codex team are opening up new product directions for coding agents. A lot of agent product

8:05design assumes long threads eventually degrade. If that is true, the product should push users towards creating new threads and writing context summaries. The Codex team has been doing a lot of work on compaction, and that work weakens the assumption. When a thread can stay useful across many turns, I want the same thread to keep working on the recurring task. Put simply, with good context compaction, a thread's value increases over time. Now, Nick wrote about that back in April, and I have very much found myself throughout the year seeing more and more of my behavior shift to that pattern. Everything that I do on the AI Daily Brief website, for example,

8:37is managed at the moment from a single Claude code thread. When I pop in to make a change or an update, I'm not starting that thread all over, I'm just jumping into where we left off. The social pipeline through which I use Claude to generate video clips from the show is another thread, and I've even found this behavior start to influence how I interact in other parts of Claude outside of just Claude code. Over the past couple of months, for example, inside projects, I find that when it's things that I do frequently, such as updating or writing new ad copy, instead of starting a fresh thread or conversation every time I need to do that task, I just jump in at the end of the last time

9:09I did a similar task. Well, it turns out I haven't been the only one who has started to shift their behavior in this way. In fact, this is now, as of this week, officially how Claude projects work. On Thursday, the Claude account posted, Projects now run from one conversation starting in Claude code. You describe what needs doing, and Claude directs parallel threads that keep working after you close your laptop. In other words, it's not that there aren't multiple threads. It's that those multiple threads are managed by Claude, and what you do is interface with Claude through a single thread.

9:39This is very similar to the pattern that I had just described. And by the way, a week before this came to Claude, it had also come to Cursor. In fact, when Cursor introduced their version of projects, it had this logic built in. In their announcement, they write, rather than creating a chat for every task, you work with a coordinator agent in a single persistent thread. And the impacts of this shift are pretty big. Claude code creator Boris Cherny wrote, projects have changed not only how I interact with Claude, but how I code. I stopped managing sessions. I just send thoughts as they come, Claude splits them into threads, and the

10:11project remembers how I work. And this, I think, gets to a very related shift in how we work with AI, which is that chatbots are increasingly agent fleet managers. If you look at all of those examples that I just gave, what was changing was the place that we interact with a coordinator AI, but that coordinator AI was still spinning up lots of different subagents to do different parts of the work. What's interesting about this to me is that I think for a while, many of us would have assumed that the chatbot interface itself was perhaps an intermediate interface, something that

10:42was convenient and familiar for the time, but might give way to something else. What's interesting is that when you take the combination of monothreads and chatbots as agent fleet managers, what has happened is that instead of shifting away from chatbots, the model and harness companies have quietly shifted what chatbots actually are, but without pulling the interface rug out from under our feet. We still get that same comfortable experience of interacting with a chatbot. In fact, one that's even more simplified because we can do it from a single thread. But what that chatbot is doing is very, very different than what it was doing a year ago. Ethan Malek wrote,

11:13What makes Claude Project so interesting is that it handles teams of agents really well. You talk to a main orchestrator agent and it spins up specialists. Basically, it creates an organization to solve your issues, mixing expensive and cheap agents depending on your preferences. This also, by the way, has implications for the rest of our software services, which might increasingly start to look like context databases for our main coordinator agents. Signal wrote, All of my iMessages and email now run through a single agent interface. I still text, but I delete every email client from my phone and canceled every email subscription or app I had.

11:46Gmail is basically just a database to me now. Increasingly, so are the rest of Google services, i.e. I never want to manually write an email, create a doc, build slides, organize a calendar, or manage files again. I just want to express intent and have the underlying systems of record updated correctly. I think now you'll see apps disappear quickly. Many will become databases at best or entirely obsolete at worst. The agent will be the default interface to your personal and professional life. And even if that doesn't fully happen, or if it takes a lot longer than someone is forward-thinking as Signal might think, it is certainly the case that the chatbots that you

12:17interact with are doing something different than they used to.

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Voice instructions and loops

15:12which we've talked about a lot on this show, is that the way that you're interacting with that chatbot is increasingly going to be through your voice. Every free learning program I've released this year starts by imploring people to either use native chat GPT voice control or to get something like Whisperflow setup for the sheer efficiency and expanded context that voice is going to enable. And increasingly, this is just table stakes for agent products as well. This week, GrokBot joined the party with its own native voice integration. SpaceX AI's Danny Graziosi wrote, using voice mode to rapid fire tasks to your chief of staff while it orchestrates your bot team feels

15:44like magic. Parker Conrad from that same team wrote, GrokBot already took such a leap towards a more seamless interaction model. I'm bullish on voice taking that even further. Gaurav Bison pointed out that it's not just voice alone, but the fact that voice is now meeting expanded underlying capabilities. He wrote, tried voice on a bunch of agent products this year, and it always felt like a gimmick until the agent could actually go do things. Voice plus real work is the combo. And even the companies and projects that were already voice-pilled are getting even more so. OpenAI just released a new Codex ad,

16:15with two of their team's developers talking to the voice agent in Codex powered by their new GPT Live1 model while they're working out. Google is also on this same tip, releasing Gemini 3.8 Live and 3.8 Live Extended Thinking this week, writing that major upgrades in intelligence and parallel reasoning make it more intuitive to collaborate and execute complex tasks using your voice. Last week, similar to the thing that Codex was advertising in that workout ad, we got Devon Voice, which allowed developers using Devon to also tap into the new GPT Live model to code by talking. And Allie K. Miller summed it up when she said,

16:45Whenever I see a new voice-first interface, a tiny voice inside of me screams with joy. Voice is the interface of the present and future. Now alongside monothreads and more capable agent coordinators that manage their own fleets, and voice-based instructions, the nature of the instructions that we give to our AI has also changed this year. Summarizing the big shift, instead of prompting our AIs, we increasingly set goals for them. Inherently, this creates more latitude and agency on the part of the AIs for figuring out how that work

17:16should get done, and for working in recurring loops until that work gets done. This started with the introduction of primitives like Slash Goal and Claude Code and Codex. It was a formalization of this interaction pattern that allowed people to specify goals, instead of just dropping a prompt. This would later expand to the whole idea of loop engineering, which is something we've talked about throughout the summer and even did a webinar on about a month ago. The idea of loops really got a big lift when OpenClock creator Peter Steinberger wrote, you shouldn't be prompting coding agents anymore, you should be designing loops that prompt your agents.

17:47This idea of a loop is simply that, in addition to defining the goal, you define success criteria for the goal that the agent can measure itself against, so that it can run in a continuous loop until it achieves said goal based on the quantifiable criteria given. And the thing is, and the reason that it's worth mentioning in this show, is that although loops and loop engineering has all of the hallmarks of the type of buzzy terms that we love over in our early adopter circles, but which reasonably elicit eye rolls among the rest, this is actually a fairly critical mindset shift when it comes to thinking about how to get the most

18:18out of our interactions with AI, which is why we spend so much time on it here. The sixth change in how we interact with AI is, I guess you might call multi-modality, i.e. the using of multiple different models in a personal stack, not just on an organizational level, but even within the domain of an individual user's work. I have not yet settled on the exact right language to describe this multi-modality, as a number of my Patreons love to point out. But while on the one hand, many advanced AI users have always viewed model selection as a way to get benefits,

18:48in other words, not just being constrained to a single model because that's the one you pay for a subscription for, but finding the model that does the best with your particular task. The difference now is that the complexity, difficulty, depth, and duration of the work, combined with the cost of the most advanced models and shifts in the subsidy of those models in the standard plans, means that this is not just an early adopter alpha thing anymore, but basically a cost and efficiency requirement now. It's also enabled by the fact that the more time that goes on, the more that cheaper models that are just outside the state-of-the-art can do things that would have been state-of-the-art just a couple of months ago.

19:21You saw this a lot when Fable 5.1 dropped. You had people all over X and the other AI communities sharing their hacks on how to get the most out of the model without breaking the bank. And increasingly, you can see this moving from X post-hacks to actual usage systems discipline. And this interaction pattern shift dovetails very closely with the idea of chatbots as agent fleet managers. One of the things that a lot of folks have noticed is that at least at current, the default for a model like Fable 5.1 is to not just use the most advanced model as the coordinator, but also to use it for all the sub-agents.

19:53Many of us have had the experience where we give Fable what we think is a fairly standard task, only to come back and see a major percentage of our current usage cap tapped, because it turns out that the 5 or 10 sub-agents that it spun up to go do some sort of research were all using the most advanced Fable model as well, even though that was completely not required for the task at hand. TLDR, understanding where different types of models and model capabilities are needed and how to normalize that in your management of your AI is going to increasingly be an important discipline. And by the way, this is one trend that, if not careful,

20:23could kind of run at odds with that trend towards simplicity. As all of the model and harness labs try to make the average experience more simple, in many cases that will mean obscuring or even doing away with the more fine-grained controls for determining things like which tasks get which model. Now, theoretically, the labs and model companies will try to build automated systems underneath that can figure out those specifics for you, i.e. to realize that the sub-agents needed for a research task are not state-of-the-art, but I'm not sure that they're always going to get it right, and so I think that this will still be something that people need personal agency around.

Shared agents and multiplayer AI

20:55The last interaction pattern shift is one that I've talked about a lot recently, so I won't go too deep on, but it's the idea of shared agents. 2026 will be looked back at as the year that agents became real, after years and years of excitement. But for the first two-thirds of the year, almost all of that agent behavior was siloed to individuals. It was each of us individually building our open-claw teams, changing how we do work, but not necessarily changing how our teams do work overall. You're starting to see that shift. An example of that came this summer when Claude introduced Claude Tag,

21:26which replaced the old system of Claude and Slack, where each individual could call upon their own Claude instance, with a new team-based system, where each channel had its own Slack that could work across the context of that channel, not tied to any one individual. Now, the idea here is pretty clear. Some amount of our work gets done, yes, as individuals, but a lot of it gets done in teams, and we're probably going to need agents that live at the intersection of our teams, not just agents that live in our own little individual worlds. And you're now starting to see tons of efforts towards these ends.

21:56Mio XYZ launched this week as the, quote, first AI employee your whole team shares. In their announcement post, they wrote, In beta since July, Mio saved teams thousands of hours and completed tens of thousands of tasks. You don't need another agent. You need a shared one with your team. On any given day, I feel like I see a half dozen or more examples of this same impulse. Although exactly what multiplayer AI means is still very much in development. Earlier this month, Ethan Mollick wrote, Multiplayer AI, where many people in an organization can use AI together to accomplish goals,

22:27remains one of the biggest non-technical problems in using AI right now. Approaches tend to be pretty primitive and based around AI as a person in your group chat. That is limiting. Arya Bhutani writes, We still don't have a consistent mental model for who the principal is in a multiplayer agentic system. Is it you, the user? Agent is your proxy, inheriting your context, access, and authority? Is it the agent itself? Agent has its own identity, relationships, memory, and access. It's the difference, Arya continues, between agent civilizations versus human civilizations at agent speed.

22:58Realistically, the answer is both, but for different systems and use cases. Atlan's Rishi Garo of Batnagar writes, Multiplayer AI will be one of the biggest design problems of 2027. The phrase alone means something different to every person I talk to. Is it shared memory, context, org design, or something else entirely? And what's interesting is that Rishi then goes on to show why each of these sounds compelling when you look at it in isolation. The memory lens, he writes, Five agents on one project. Five different versions of what happened is none of them talking to each other. It's a mess. Fixing it, one shared memory instead of five private copies, is the whole game.

23:32But then there's the context lens. Rishi continues, Nearly everyone I talk to has built their own little context layer for their own agents. It works for them. Stretch it across the whole team and it falls apart. Personal context feels solved, org-wide is wide open. And then of course there's the organizational change dimension. Rishi concludes, The hardest part of multiplayer AI might not be technical at all. Getting a whole team to adopt one shared way of working is more org design than engineering. Nobody wants that to be true. What's clear is that there is going to be a ton of work done in this area.

24:03Josh Rosen writes, We've decided AI is going multiplayer before we've agreed on the architectural patterns for multiplayer AI. And then of course proceeds by trying to explain all of that and get us to a slightly more coherent answer. Now if this is something that you are interested in, I will show for a moment the latest free self-directed training program that we've dropped here, which is the Multiplayer AI Sprint for Teams. This does not assume that multiplayer AI is figured out, or frankly almost any of the answers to those important questions. What the Sprint does is it gets people to start to build the foundations to how multiplayer AI,

24:37which is really just another way in our case of saying shared agents, can function inside your team. Over the course of the four Sprints, which we've divided into weeks but don't have to be split up that way, we are going to inventory first what your team is actually running right now for AI, and or to the extent that you are even more new, perhaps bring in some champion or export from your organization who is a little bit farther in their stack to help you understand best practices in your specific organizational's context. And once you have that sort of inventory, you move then into Sprint 2, which is all about context, writing down what your AI knows about you and what it should know about you.

25:09Sprint 3 is looking at the overlap in work streams and in context, which is going to give you your possible places to build shared agents. And Sprint 4 is going to be where you actually do that building. And of course, you can then repeat Sprint 3 and Sprint 4 multiple times to experiment with multiple agents as much as it helps. There are going to be tons and tons of product experiences and great product thinking coming to the multiplayer AI or shared agent space in very short order. And so to the extent you want to kick that can down the road just a little bit and let the industry and norm settle a bit more, I totally understand. That said, I do think for teams who want to dive in,

25:41there is certainly some alpha to be had by getting out ahead of that. And hopefully that's what the Multiplayer AI Sprint, which is of course linked in the show notes, can maybe help you do. So that is the seven ways that how we use AI is changing. Hopefully this gives you some interesting new directions to explore and some new ways to think about your relationship with AI. For now, that's going to do it for today's AI Daily Brief. Appreciate you listening or watching as always. And until next time, peace. We'll see you next time.