It's kind of a response, I think, to the AI populism backlash. It's saying, 'Hey, this isn't going to hurt you or do anything bad. It's just a cute little thing with eyes or a sexy little thing with eyes, whatever.'
Not only do I have an assistant or chief of staff, I have a bribable chief of staff. Like I have a corrupt butler.
I think the more money and power you have at one of these companies, you get a little bit mentally farther away from what the average consumer wants. Let's say the average consumer does want an AI agent. They're willing to deal with a little bit of creepiness in order for it to be free, but not too much.
Hello and welcome to 'Decoder.' I'm Nilay Patel, Editor-in-Chief of The Verge, and 'Decoder' is my show about big ideas and other problems. Today I'm talking with Hayden Field, The Verge's Senior AI Reporter, about the new wave of consumer-friendly AI agents. Now, if you've been paying attention to AI, you know that AI enthusiasts have been using agents for a minute now. Homebrew OpenClaw setups led to a surge in Mac mini sales earlier this year. With the launch of Meta's Muse, OpenAI's Dots, and xAI's Grok Bot has brought easy-to-use agents to millions. Dots, and in particular Muse, have had the highest profile product launches lately, and they're fascinating to compare and contrast. Both Meta and OpenAI have decided to pitch their agents to mainstream users and businesses in the form of cute animated mascots that can do everything from the boring, restaurant reservations and inbox triage, to more sophisticated tasks. In OpenAI's case, they are even offering specialist Dots for marketing, legal work, and accounting. There are other differences. Muse notably is free while Dots is not. And you'll hear Hayden and I talk about why Meta, which still doesn't have a frontier model of its own, might have a meaningful edge here because it's so much better at making and distributing consumer products. There's also a huge 'Decoder'-style tension wrapped up in the agent race, a conversation about what AI is good at today, what it still can't do, and the privacy and security implications of handing over your credit card information, your email inbox, and other sensitive data to an AI that might be able to do things for you without ever requiring an app or a phone in your hand. That might be the future of all computing, but it's unclear whether anyone wants to hand over all the data to make it happen. Okay, The Verge's Hayden Field on Muse, Dots, and trusting AI agents. Here we go. Hayden Field, your Senior AI Reporter here at The Verge. Welcome back to 'Decoder.'
I'm excited to talk to you. This time there isn't any wild interpersonal drama. No one's feelings have been hurt. I would say Elon tried to hurt some feelings and didn't get there. Alexander Wang from Meta has maybe been taking some shots, but none of the 'Real Housewives' stuff we usually end up talking about when you're on the show. Just some product launches.
It's been a bit of a pause in the soap opera antics for now, which I'm really grateful for.
Just some good old-fashioned product competition in the marketplace. Let's see who wins and loses. And maybe everything will kill us all in the end, but for now, cute mascots. So there's a lot going on. Let's start at the start. Hayden, you were at OpenAI Dev Day in San Francisco. They announced Dots, which is their new agent platform with a cute mascot. That happened just a few weeks after Meta Connect at which the company was all in on Muse, their agent platform with an adorable mascot. Tell us about the state of the industry right now. Everyone's very excited about agents. What's going on?
Yeah, it's funny to me because, you know, I've been covering agents for so long and I remember that in 2022, it was kind of like the year of ideation. That's what tech leaders called it about agents. They were just ideating. They were thinking about what it could look like. There were a lot of Jarvis from the Marvel universe references. 2023 was, they all called it the year of deployment, like let's try things, let's deploy and learn more about what's failing. A.K.A., pretty much all of them were failing at the time. Then we had 2024 and 2025. They didn't have any names for those years, but things were still pretty bad as we saw. And 2026 seems to be, to me, the year of actually, the beginnings of actually useful AI agents for the consumer, like always on. Obviously, OpenClaw was the start of all of this. But yeah, what's interesting to me is that after seeing that one man, Peter Steinberger, was able to create an actually useful AI agent for the consumer and always-on tool, despite its privacy flaws, it had a lot of privacy and security issues, but it was an agent that was useful enough that people still wanted to use it anyway and try to find ways around these privacy issues that it was having. So that inspired these companies to be like, 'Oh, if one man can do this over the course of one weekend, we've really been slacking. We got to get it together.' OpenAI hired that guy and now we have Dots. Meta, of course, was working on its own version of this and put it out sooner, Muse. So yeah, they're basically just both always-on consumer-facing AI agents that these companies are peddling as a way to essentially be your personal assistant in a lot of ways like booking flights, booking dinner reservations, buying gifts for people. But not only that, some of them are kind of going further to be your work personal assistant. And we'll get into that in a minute, but this is kind of how OpenAI and Meta are warring right now. They're trying to present these two AI agents as a little bit different from each other, even though they're essentially the same.
Just broadly speaking, to define some terms, we're going to keep saying agent. And what we mean here is an AI model wrapped in a harness that lets it use a computer.
Right, the way that I describe AI agents usually is an AI tool that can complete multi-step complex tasks on your behalf without you handholding it the whole time. So just like I was a personal assistant in one of my first jobs in New York, so I would've gotten fired if every time my boss asked me to book a flight for her, I was like, 'Well, what time? And also, what's your Sky Miles number? And also, what's your loyalty number again for this other thing? And also, oh, do you want me to try to get you first class?' I mean, no, they want you to have common sense and just book the flight. And so this is what they're peddling these AI agents as being, things that can work on their own mental kind of scratchpad in the background and not ask you every single time there's a step. They can complete multi-step processes in the background, kind of doing their own reasoning and then present you with a final completed task.
That's the big idea. Just to say it clearly, both Muse and Dots come out of the OpenClaw lineage, which is a technical approach to building an agent. There's a lot of different ways you might be able to accomplish, it'll remember your Sky Miles number and book you a flight. But the way everyone's going at it now is the idea of a harness around a model. That was the big innovation in OpenClaw. And the computer OpenClaw was using was often your own Mac mini on your own network using a wide open browser with your own data, which is where a lot of their security problems came from. It's the same model with Muse, only it's a little Linux computer that Meta's giving you in the cloud. It's the same model with Dots, although the way ChatGPT is set up with Codex is very complicated and that computer could be in a lot of different places. But this notion that what you really need is a computer with a browser and some harness is going to direct a model to do a bunch of stuff for you using that computer, that's what we've landed on today. And it just seems to be the default winning model. Are all of these things just OpenClaw? Are they just riffs on OpenClaw? Are they the same as OpenClaw?
You could say that. I mean, they're different technically. Meta keeps saying, 'Hey, we did build this from scratch, but it was inspired by OpenClaw.' There are a lot of similarities there, but they did say that they built it from scratch. And then of course, OpenAI hired the creator of OpenClaw, so there's probably a lot of similarities there as well for Dots. But I mean, technically they're their own things, but they were all heavily inspired by OpenClaw and clearly these companies are kind of trying to get it as close to OpenClaw as possible without the security issues.
The reason I wanted to stay there for one second is that I think it's important to identify the fundamental technical approach that is happening with these agents because there's a lot of different ways it could have gone and the whole industry has picked this approach. What you need is a web browser, a harness, and a model that can go use that web browser. What's really interesting to me is that Meta has done a really good job on top of that fundamental model of making a cute consumer product. Meta is good at consumer products. Obviously Instagram and Facebook and the rest are undeniably good consumer products. People really like them. The Meta Quest for all of Meta's failings in VR, I thought, has always been a great consumer product. You take it out of the box, it helps you use it, you can have fun with it right away. Whereas Dots seems like it's kind of made for software engineers, but you can also plan a wedding in it. And I'm wondering if that's how you see it too, that one is kind of an enterprise product with a little consumer gloss on it, and then Meta obviously is just dead ahead consumer product.
What's different here is OpenAI needs money a lot more than Meta needs money. And so they're honestly pretty similar, the products, it seems like. The difference is that OpenAI is marketing it to be an enterprise-friendly product a lot more than Meta is. So Meta, yeah, it's just a consumer-facing product. They're happy with that. Their goal is to make it as easy to use as humanly possible. So, you know, one-tap downloading, just putting it in your face literally everywhere. Even when I went to my Instagram profile the other day, there was a pop-up for using Muse. So yeah, they're trying to make it free, accessible, and super easy to broaden the audience. They want anyone to be able to just use it with one click and get used to it. They're trying to just flood the market with AI agents and make it normal and have a ChatGPT moment, if you will, for AI agents. They want Muse to be that. Now with OpenAI, they're still marketing it as a consumer-facing product. I mean, when you're using ChatGPT, if you have the right subscription, it's right there. It's like use your Dot to do this, that, and the other. They really want you to go for it as a consumer as long as you're paying. So this is for their 100 to $200 a month subscription tier, unlike Meta. So that's why there's a huge difference here. For OpenAI, they don't have the money, it seems like, to just be front-loading all of this and say, 'Hey, use it for free. No problem. We'll catch up with you later.' They want this to be a money-making product, and that's why they're marketing it to the enterprise. They talked a lot about privacy to try to make this set apart from Meta's Muse. They talked a lot about what you could do with this as a knowledge worker, whether you worked in marketing, graphic design, software engineering, product, tons of different industries they kind of gave examples of how you could use this as an assistant, as a coworker. Sam Altman even put it like a chief of staff. He didn't want it to be just an assistant. He wanted it to be your chief of staff. And something else that I thought was interesting is that Sam Altman seemed to kind of subtly dig at Meta on stage a few times during Dev Day. But one of the times he said, 'Look, I don't want this to be just something that's used to book flights or plan things with friends and look at your calendar. I want this to go beyond that and to really do knowledge worker tasks, things that you and your industry would need a niche assistant to perform.' And that's why they also introduced specialist Dots that are good at marketing, legal analysis, things like that. So they're really going for the enterprise angle here, but I think it's because they need money ahead of their IPO, obviously.
The revenue piece is really interesting that it's only for paid customers, that they're higher tiers. OpenAI's tiers are very confusing, but it's $100 and up. And that's just going to keep a lot of people from using Dots at first. The flip side of that is OpenAI does have a lot of customers who are paying them a lot of money. If they can get those customers to start using Dots, then those customers will be on their agent platforms and maybe you get some lock-in, right? You've given those agents a lot of data. They're part of your workflows. And that might solve the problem I think we've seen all the Frontier Labs have where a new model comes out and it's slightly better than the competitor model. And everyone switches for six months and the next new model comes out and everyone switches back. Is there some thought that these agent platforms create lock-in, create actual loyalty on top of just the models being better?
Yes, they're basically hoping that they can build a moat with their users for these things. So, you know, if Meta and Google already have super easy integrations with all of each company's existing tools and services, they can easily keep you there. For example, I have an Apple iPhone, but I use almost exclusively Google apps. So if Google had an AI agent that I was using every day, it would be a little tough for me to switch. Meta is hoping that they can get you so into the Muse ecosystem that it would be tough for you to switch once maybe one day you do have to pay. With OpenAI, they're trying to get you locked into their ecosystem right away. Even though you have to pay upfront, they're thinking, okay, well, we have integrations with almost every tool and service. Maybe it's not as good as the company that owns that tool and service, but we have the variety and the breadth. They're kind of going for quantity here. One of their employees told me that. They said, 'Look, yeah, maybe our Gmail integration, for example, isn't going to be as good as Google's, but the fact that we have a Gmail integration and an Outlook integration and X, Y, and Z,' they're going for just the power of choice and the fact that power users will hopefully stay in this moat.
It's funny, you mentioned Google. Google announced an agent, Gemini Spark. We should mention that SpaceXAI, the very coherent company that everyone understands, SpaceXAI, has an agent called Grok Bots, which it is promoting heavily. People seem to like it. That was built by the Cursor team, which they acquired. There's a lot of this going around, but it seems like Meta really got there first with a consumer product that people could understand. Obviously, Grok Bot and Dots are much more enterprise-focused, coding focused. Why was Meta able to get there first?
I think it's because from all my reporting on Meta, they have been behind in AI for a while and Mark Zuckerberg was like, 'Look, we've got to lock in and do a product that people are going to use on our platforms.' Alex Wang apparently was more looking to create a frontier model that it was going to compete with the other leading frontier models. He was like, 'We got to think long. We got to think super intelligence.' Zuckerberg was like, 'No, we got to think cold, hard, cash,' A.K.A., getting people to use stuff on our own platform. And so I think that where they met in the middle was this, an AI agent that people would use on the Meta platforms, but that still represented a huge step forward, they hoped, for AI systems and what they could do in the everyday world.
This is a fascinating dynamic in that Meta still doesn't have a frontier model. I don't think anyone at Meta's claiming that the Muse Spark model competes at the frontier of GPT-6, but their product is better. And certainly they have a massive distribution advantage in that they don't have to pay for advertising on Meta platforms and OpenAI does. And certainly they have a consumer design advantage. They're just better at consumer products. Is this the future of competition in AI at all? For so long, it's just been about the capabilities at the frontier, but now it's a little bit about marketing. It's a little bit about product design. It's, you don't have the best model, but you made a consumer agent product and you connected it to giving everybody a computer in the cloud so now it can actually do things for you. And its frontier capability is actually not the important thing, it's its capability capability.
I think the more money and power you have at one of these companies, you get a little bit mentally farther away from what the average consumer wants. So let's say the average consumer does want an AI agent, which many of them don't as we know, but let's say they do. If they do, they're going to want it to be free or cheap, and they are going to want it to be safe to use without creeping them out too much. They're willing to deal with a little bit of creepiness in order for it to be free, but not too much. Meta's Muse, the headlines about some of the privacy issues with it have gotten a little bit above that comfort level, I think, for some users. That's probably why OpenAI at Dev Day made a lot of remarks about privacy and the importance of that, especially if they're trying to court enterprise users with their AI agents. Right now, these companies are trying to, especially Meta, just trying to flood the market and create a mass market appeal for this stuff. Similar to how ChatGPT became the default AI chatbot, Meta wants to be that for AI agents and they feel very hungry about it. They want to create a ChatGPT moment for this technology by making it the default use for the general public, Muse.
I just think there's something really interesting about the idea that a less than frontier model wrapped in a better product is actually going to be the success here versus a bleeding edge frontier model that is a little bit harder to use. And there's some dynamic in there that is the history of all consumer tech products, right? The worst product that's easier to use often wins, but we haven't seen that yet in AI.
Right, I think that's really important to note here because not everyone needs the bleeding edge frontier model. Software engineers do. They see a huge difference with coding prowess from one model to another, and that's why they're always switching. But the general public, if they're using an AI agent, as long as it's useful, they're good. However, not many of them are useful yet. We're still testing Muse and Dots ourselves. Muse, apparently, I saw a couple anecdotes yesterday that it had a big flop for a day or so. People felt like the capabilities got a lot worse. Dots, the jury's still out, we're still testing it, but it depends on what industry you're in and what you're really using this stuff for. That's why OpenAI is going for enterprise because it makes money and they're confident that their agent is going to be more and more helpful in these niche industries. That's also why they launched specialist Dots, because as we know, the more specialist niche info you feed into an agent or model, the better it's going to do obviously. And they don't want to have that imbued into every single agent or general purpose agents, just too pricey and too compute heavy. So they're like, let's launch these specialists.
I feel like you and I talk about consumer versus enterprise adoption of AI every time you're on this show. And here it seems very important to note that OpenAI asking people to pay a lot of money and asking people to use it primarily in enterprise context is going to overcome what I think of as the brittleness problem just because enterprise customers, business customers have a lot of economic incentives to figure out how to make something work. So if I'm paying OpenAI 100 bucks a month and I'm using the marketing Dot to solve some marketing automation and it doesn't work the first time, it is very likely I will try again because if I get it right, then maybe I've saved some money or some time in my business, that is really good and I'll do it again. And eventually that will lead to more revenue for me, the business owner. What I know about consumers is that the first time it doesn't work, they just walk away. They have no incentive to try again to overcome frustration or brokenness. And we've seen this happen with Muse now a few times. I've seen it myself with Muse where it pops open a browser and tries to do something and it runs into a brick wall or a Captcha or Amazon blocking it and it says, 'Hey, I can't do it that way. Do you want me to try another way?' And I'm like, 'Ah, whatever. I was just screwing around and I'm never going to try it again.' These are very different markets. OpenAI has vacillated between wanting to be the big consumer company and knowing that the revenue is in enterprise. Do you see any focus in Dots or do you see that personality shift back and forth in the product itself?
