Ep 726: Perplexity Computer: What it is, How to use it and is it better than OpenClaw?

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Have you ever spent way too much time thinking about and testing what AI model is best for a certain job? Or maybe you've already spent like 6 hours trying to get OpenClaw set up, but the Telegram stream isn't Telegramming and the heartbeat thing isn't heartbeating? Yeah, me too. That's where I'm at right now. But I think Perplexity's new computer offering may have kind of solved that. Here's the reality: AI agents and agentic models are finally getting their shine this year and are already dominating the business landscape. But it's kind of overwhelming. I mean, there's so many agentic models. There's so many AI agents and different ways to connect them. The MCPs and the A2As and the ACPs. It's, it's AI agent soup and it still requires a ton of human oversight. That's why I've been pleasantly surprised so far with Perplexity's computer because it requires hardly no human duct tape and it has all of the best AI models. And agentic swarms that just work and get work done for you. Yet I still don't think I would ultimately recommend Perplexity Computer right now for most of our listeners, and it's not for the reason that you're probably thinking. All right, are you ready to put AI to work on Wednesdays? Let's get straight to it. So on today's show, here's what we're going to be going over. I'm going to show you what Perplexity Computer is and what it can unlock for even non-technical professionals. We're going to go hands-on, yeah, live, to showcase some of its advanced capabilities. We're going to compare it to one of its kind of closest competitors in OpenClaw, and I'm going to tell you if I think it's worth using or not. And yes, spoiler alert, I think for most people it's going to be a hard no, even though I am wildly impressed with its capabilities so far. All right, If you're new here, welcome. This is Everyday AI. My name is Jordan Wilson, and well, we do this every day. We do AI. It's a daily, unedited, unscripted livestream podcast and free daily newsletter helping everyday business leaders like you and me learn and leverage AI to grow our companies and our careers. Starts here with the unedited, unscripted podcast, but make sure to go to the next level. That's our website, youreverydayai.com. We're going to be recapping the highlights from today's show as well as giving you all of the other AI news you know to stay ahead and be the smartest person in AI. So let's talk about Perplexity Computer. All right. So on most Wednesdays, we do the Putting AI at Work on Wednesdays where we go quite literally hands-on and we do some demos and some, you know, really going behind the scenes on a new feature or a new AI model from one of usually the big four. You know, usually we just focus on Microsoft Copilot, ChatGPT, Google Gemini, and Anthropic's Claude. But I think this new offering from Perplexity is good enough that we had to get our hands dirty. And yes, for our newsletter audience that voted on our poll, everyone voted for Claude Cowork's scheduled tasks, which I was super stoked to do. Spent hours, you know, trying to plan the show and get everything set up. But Anthropic's been having some crazy outages right now. According to them, it's because they've had a surge of new users. So I was hoping to bring that show hopefully next week. I'll be able to use Anthropic Claude for more than 15 minutes without it breaking. But why not? Let's start live. What could go wrong? All right. So this is Perplexity's new computer. Unfortunately, right now, FYI, you do have to be a Max plan subscriber. That is $200 a month. Although Perplexity did say that this is going to be coming out to Pro subscribers, though, on those on the $20 a month plan. All right, livestream audience, help me out. Let me know if you can, you know, kind of see my screen here. Maybe zoom in a little bit. All right, podcast audience, if you ever want the video version of this, it's always available on our website at youreverydayai.com, but I'm going to do my best to describe this to you. So I've only been playing around with Computer for a couple hours here, you know, just really trying to get my hands dirty, you know, trying to really push it to its limits just in a, well, Very limited capacity though, and I'll tell you why here in a minute. So here's how it's going to work. You're going to go to your normal Perplexity plan and you're going to see a new option, right? Where you would normally just type in a prompt and, you know, choose your model or your mode inside of Perplexity. Now there's a new option that says computer. All right. So I have something I wanted to try. All right. So I'm going to click computer. And this part is a little different. And at first I wasn't a big fan of this interface, but after a while, well, I kind of like it. So I just said create a bar race chart over the last 5 years showing monthly prices for Nvidia, Microsoft, Tesla, Google, Apple, Amazon, and Meta. So the Mag 7 AI stocks. So if you've ever seen those kind of race. I think they're called bar race charts, right? But it's essentially where it plots different stocks or, well, anything that can be plotted on a spreadsheet, on a graph, on a bar chart, right? And it shows you kind of something over time. So what, I don't know if this is going to work. If it does work, we'll maybe post it on Twitter and post in the newsletter. I don't know if this will actually work because it's, it's a kind of complex task, right? So think of, you know, as we go into a little bit here on what Perplexity Computer is and some different ways to use it, I want you to think of some of those very difficult knowledge work tasks like you do, right? Or that you do on an ongoing basis, right? And that's one of the reasons why I thought of something like this. I've always wanted to, you know, create these and have them as, supplemental information or complimentary information that goes along in our newsletter. But things like this are hard, right? I used to edit videos, I don't know, 10 years ago, but I've spent thousands of hours doing it. There's some kind of AI tools that can help do these things, but as far as I know right now, there's no easy, simple way to make something like this. All right. So I'm going to show you right now kind of what's going on and walk you through it. So the way that I would normally do this right now, uh, I don't know, right? I would probably do a, um, a GPT-5.2 Pro query to figure out the best way. I would still try doing something like this, um, in Opus 4.6, uh, GPT, uh, 5.2 Pro, and, you know, you know, Gemini 3.1 Pro. I don't think any of them could necessarily do this because it's very complex. I don't even know if this is going to work. And if it does work, what the end goal is going to be. Is it going to be a GIF, an animation? Is it going to be a video? I don't even know, to tell you the truth, if Perplexity Computer can generate video or not. So we're going to find out as we go along. But you'll see right here on the right-hand side, there is this chain of thought. So kind of the new layout that it took me a little while to get used to is you now have a 3-pane layout when you're using Computer. All right. So if you use NotebookLM, you know, maybe this is a little easier on the eyes and you can toggle the right-hand column in the middle column to get a different size. So essentially what you have here is you have your— you can always go back to your normal Perplexity view. So if you are a Perplexity user, that didn't change. But then in the computer view, after you've entered into a, a certain task is what it's called. You're going to see all of your other tasks there at the same time. So as I was testing this, now I have 4 on the screen. I was running 3 concurrently. I was seeing, okay, is it going to break? Are we going to go off the rails if we're running too many tasks at once? And it actually did a pretty good job and it built these in a pretty, pretty quick amount of time. So let me just walk you through kind of what's going on here. So right now it says I'll load the relevant skills for this task and gather the stock price data. So now it's running this task task in parallel. It's loading different skills, right? So it has different skills that are already built in. I didn't go out and build these skills, right? So obviously skills popularized and brought to market by Anthropic, but they've been adopted by, you know, everyone from Microsoft and Google to OpenAI, right? Everyone's using skills. So it's using these skills to do some research. It looks like it said, let me get the historical price data. Okay. So as I go through here,, you know, I can click around and see what it's doing. Uh, it looks like it's running a little bit of code, running some JSON, uh, pulling some API responses. Um, and you'll see right now, wait, it, is it done? It might be done. Let's see. Uh, it said the bar. Yeah, it just finished. It says the bar race chart is live and looking clean. It shows all 7 top, uh, tech stocks with monthly closing prices from March 2021 to March 2026 using data from Perplexity Finance. Okay, that's one of the reasons why it was able to that was so fast I forgot. Perplexity has a really good and really popular finance tool. So it says hit play to watch the race unfold. So let's, let's go ahead and see this. I don't think this is going to generate exactly what I wanted, but not bad. Okay, so what we have here is we have actual bar charts. So I was, I should have done a little bit better job in the prompt of describing this that I actually wanted to see kind of more on the candlestick line, right? Uh, them go up and down. But instead, um, I mean, we'll see. So it has kind of the, um, standard bar chart. So right now Nvidia's at the bottom, right? Because this is 5 years ago. Uh, and this is— I'm trying to see what our axis is here. I'm guessing it's, uh, billions. Yeah, it looks like it's billions, uh, in market cap, I'm guessing, is what it is. Because it has Meta at the top at the time. Uh, Microsoft— this might be revenue, I'm not sure. So Oh no, it says monthly closing prices. So that's not great either. I'm probably going to actually play this for everyone. So at least our livestream audience can see. I'm going to actually go in and edit this prompt a little bit and then we're going to look at some of the other results. So let's go ahead and play. Okay. So to their credit, it is animating and it's doing fairly well. So when a new company kind of goes on top, it's switching their position. So as an example, Nvidia. Okay, I see what's kind of happening here. Nvidia is not going to climb all the way to the top because they did have their stock split, right? So I think it went from like, you know, $1,300 down to $130, and it looks like it's retroactively adjusting it. So this is just doing it by stock price, which isn't necessarily a good barometer, uh, to see which company is growing the most. But, uh, like I said, to their credit, I didn't really, um, denote that in my prompt. So I'm going to go in here and, well, we'll just go ahead and Uh, AI moves too fast to follow, but you're expected to keep up. Otherwise, your career or company might lag behind while AI-native competitors leap ahead. But you don't have 10 hours a day to understand it all. That's what I do for you. But after 700+ episodes of Everyday AI, the most common questions I get is, Where do I start? That's why we created the Start Here series, an ongoing podcast series of more than a dozen episodes you can listen to in order. It covers the AI basics for beginners and sharpens the skills of AI champions pushing their companies forward. In the ongoing series, we explain complex trends in simple language that you can turn into action. There's 3 ways to jump in. Number 1, go scroll back to the first one in episode 691. Number 2, tap the link in your show notes at any time for the Start Here series, or you can just go to starthereseries.com, which also gives you free access to our inner circle community where you can connect with other business leaders doing the same. The Start Here series will slow down the pace of AI so you can get ahead. See how to do it live. So I'm going to say, let's update this to market cap so we can see a truer version of company growth over the years. All right, we're gonna do nothing else. We're gonna come back and check on that. So let's go ahead and— well, this is going to be maybe a fun one, uh, because I asked Perplexity to do my work for me. All right. So in this prompt example here, I simple, I said, hey, I'm doing this livestream, go do my presentation. So I said, create a balanced, insightful, and factual presentation that I can show for my podcast, Everyday AI. Carefully find and emulate the exact style of my livestream presentations and create a presentation for this podcast. This very one I'm doing right now called Perplexity Computer. What it is, how to use it, and is it better than OpenClaw? I said, please do all the research, organization, and design. So you'll see here again, it chooses which model to use, which is great. So it's loading a different skill. So it loaded an Office PowerPoint skill, and then it said it's running these different tasks in parallel. So it's kind of putting out different subagents, and it's deciding what these subagents are going to do. One thing I wish, I wish that you could dive into a little bit more depth on how some of these skills are used. But it did. I can click and it's showing me, you know, what it's searching on the web. You know, what is Everyday AI podcast presentation style? It says, I don't have any previous records of your presentation style. Let me find examples of your Everyday AI livestream. So it went through. It said it created task lists here. So pretty cool. It's running these tasks in parallel. And then it said, okay, I think I have a solid understanding of this topic. Go through here. Let me just keep browsing a little bit. And then you'll see here we're starting to use different models here, right? So now it's using Gemini 3 Flash to analyze visuals, which is pretty cool because it used a, I think, a Python tool to take a screenshot. It found a YouTube presentation, took a screenshot, and then it said, okay, I need to use an AI model to understand this visual. Right. Again, think of all the human work that something like this would normally take. Right. So it went in, it used Gemini 3 Flash to kind of understand and ingest a screenshot of our visual. So then it went from there and it took more screenshots of the livestream presentation. All right. And then, yeah, so it literally says right here, I can see what video it used. It looks like it was a video from last week. It said it scrubbed to around 3 to 5 minutes where the video presentation slides our visible on the screen. It's taking these different screenshots, throwing them back into Gemini 3 Flash, right? So pretty cool just to look under the hood and see how Perplexity Computer works, right? And then it kind of started to pick up on some of the branding. So it's, you know, dark navy background, a turquoise accent color. I actually got the color code correct. White text for primary content, bold sans-serif for headlines, right? So it did a really good job. I didn't have to tell it, here's my brand guidelines, you know, et cetera. And then it obviously went out, it did the research as well, and then it created a presentation. So I did already look at the first version, and the first version wasn't the best. Okay, it was okay. There's nothing wrong with it per se. It just didn't really hit the exact branding that I would want, although it was fairly close. It looks like something that maybe I could— and don't get me wrong, it looks better than my normal livestream slides, right? I say they're the ugliest things ever, just because I put them together myself. Right. So these actually look a little bit better, but they don't really look fully on brand. All right. So I actually go and reprompt. So at any point, you don't have to wait for this to finish. At any point, you can actually send a prompt to Computer to have it update. So essentially, I said this isn't really anything like my livestream slides. Please update the visuals and try to create a 1-to-1 replica of my standard slide setup for livestreams. And then I also kind of wanted to adjust a little bit on the content, so I haven't seen the results yet. So I'm kind of scrolling through here on the chain of thought. Again, we have the 3 panes. The left pane is your normal navigation where you can go to the normal Perplexity search, computer, your tasks, your files, your connectors, etc. In the middle, that's your task list where you can see the different tasks if you have multiple going on. And then on the right-hand side, which is kind of what I'm reading through through and what I'm scrolling through now here on the livestream version. This is kind of your chain of thought. This is where you can see what these different AI models are doing. So let's just go ahead and go to the bottom here. Let's see if it did any better, you know, for our normal livestream audience. You'll have to let me know, you know, how close this is. So a ton, a ton of information. I'm seeing all the different tools that use you know, it switched over here to Claude Sonnet 4.6 for some visual QA, right? So it used some models to build it, different models to research, different models to do QA after. Very cool. What this also shows to me is just how steerable this can be if you do want to steer it right. The great thing so far that I like about Perplexity is it just uses the right AI model for the right job, even if you don't even know what the right model is or what the right job is, right? This is a lot of tasks. This is my example is extremely easy to follow, right? But that's why hopefully it'll encourage you or maybe inspire you to say, okay, maybe I could use a Perplexity computer for workflow A. All right. So let's go ahead and see. And then we're going to actually flip through these slides, right? Okay, here we go. All right, so actually much better. All right, so it actually did a much better job on the second iteration of sticking to our brand, right? It has the, the normal kind of turquoise heading that we use, the white text, the bottom bar with our website, the top bar with the episode name. So it did pretty good. Obviously it didn't pull the actual logo. That would be one of the main missing. Otherwise, if I'm looking at this quickly, it looks pretty much like exactly what we put out there, right? Aside from a title slide and putting our logo on there, it actually did a pretty, pretty impressive job. All right. But I'm going to— I'm going to actually go up and use this because I did look at the first one and I'm like, this is great. And for our podcast audience, right, I think some of this information in there is going to be really important as we learn a little bit more about Perplexity Computer. So one of the most important things to call out, I think, here is 19 models. Okay. That's how many models that Perplexity Computer right now can use in parallel. All right. And like I said, you all you need to do is just describe the project. You don't have to be super technical. You don't have to, you know, be super advanced in prompt engineering. And then computer breaks it down into smaller tasks. And then it routes to the best AI model. So tons of different models that it can use. Okay, so it does have video generation. I actually knew that. I just had temporarily forgotten it, right? I need longer context window. I need that 3 million token context window in my own brain. So, you know, for reasoning, as an example, it can use Claude Opus 6. For research, it can use Gemini 3.1 Pro. For image generation, it can use Nanobanana. For video generation, it can use V0.3.1. For long context, it can use GPT-5.2. For fast tasks, it can use Grok, right? So for like this, to me, this is so much of what I do on the day-to-day basis, even though I do think ChatGPT is probably my most used AI system. And then, you know, Claude and Gemini are kind of tied there for second most used. I think if I did more creative things, I would probably use Google Gemini way more just because Nanobanana Pro and BO3-1. But this is a lot of what I would be doing on a day-to-day basis, right? Going between these different AI tools, these different models. I have subscriptions to all of them and I use all of them. So this right here, this kind of engine that computer runs off of is extremely impressive, especially if you are— well, number one, I think what this might appeal to is people that know they want more out of AI models, but maybe they don't want to subscribe to all of them. This does kind of give you the best of all worlds without you having to even know, which I think is actually really nice to have because unless you're a dork like me and keep up with it, you probably don't know which AI model right now ranks best for grounded search because if you read the newsletter last week, you would know it was Gemini 3.1. But, oh, no longer. Now it's Opus 4.6 on WebGrounding, right? So it's, it's really difficult to know which AI model or which mode is the best for the right task. So that's why using something like Computer is extremely helpful. So some of the key capabilities, well, you can connect your data, your integrations, your Gmail, your Slack, your Notion, Google Drive, and more. And it does have persistent memory so it can remember past work preferences and context across sessions. Like I already talked about, you can spawn specialized agents without even knowing or without having to do anything, right? And then also it runs in an isolated environment. That is one of the big differentiators between something like Perplexity Computer and Open Claw. So what the heck can you do with Perplexity Computer? Well, a lot. It's almost like, well, what can't you do, right? So deep research with source synthesis, code projects from spec to, deployment, content pipelines, competitive analysis, uh, you know, really any type of knowledge work. Because like I said, video, photo, infographics, research, it's all in there. So one narrative that I saw when Perplexity Computer was announced and that I kind of agreed with, because I'm like, okay, what is the closest competitor here to Computer? At first I'm like, okay, maybe it's Manus, maybe it's Genspark. It's more of these, you know, it's a super agent. That's what it is. Right. But the more I thought about it and looked at it, I'm like, this seems just maybe more like a more enterprise-ready and secure version of OpenClaw. Granted, probably more expensive. Right. And I say probably because OpenClaw, if you don't know what it is, well, it's a free open-source project. It's an autonomous agent that can run on your local machine. If you give it access to it, you can, you can run it in a cloud environment as well. But what a lot of people are doing is they're giving OpenClaw its own, you know, Mac Mini, Mac Studio, whatever, sometimes downloading local models to save on cost. But a lot of people are still connecting it via the API to, you know, different GPT models, you know, different Claude models, you know, Mini Macs, etc. Even though OpenClaw is free and open source, if you really want frontier performance, unless you have a $30,000, you know, Mac Studio, you know, you're still going to have to pay for it on the API side. So that's when I thought about that, you know, maybe in 5 years when you have frontier-level models that can run locally, but at least right now, if I had to choose,, right? If I could have technically unlimited either Perplexity Computer or unlimited OpenClaw, I would probably have unlimited Perplexity Computer because it is more secure. It's done for you. I've already spent hours, you know, trying to set up my OpenClaw. That's why I haven't done a show on it yet because it's like you need so much time to get it set up, right? There's updates every day. I mean, the team is, amazing there in how quickly they ship things. But, you know, to go from zero to one, it actually takes a lot of time. Whereas with Perplexity Computer, it takes no time. You can put in the simplest prompt and it gets going. So, you know, the big difference with, you know, well, there's a lot of differences between Perplexity Computer and OpenClaw. But even though at first the comparison, I was like, I don't know if that's comparing apples and oranges or if it's comparing apples and banana chips, but I think it is pretty similar. But you know, OpenClaw is more of a self-hosted AI agent that can run on your machine and you can talk to it via WhatsApp, Telegram, Discord, Slack, iMessage, etc. Right. The only difference, I think, ultimately, because, you know, with all of those connectors, you can put just about any of that information into a Perplexity computer and its ability to have memory, consistent memory across sessions. So, yeah, I can't, you know, necessarily text your, your, your wife, or it can't, you know, necessarily, you know, drag and drop files onto your computer. But it can do just about anything if you are using all of those connectors. So here's a little bit of head-to-head. So the architecture, Perplexity Computer, cloud-based sandbox, OpenClaw, self-hosted setup. Like I said, Perplexity Computer, nothing. OpenClaw, decent amount, although you can, you know, get OpenClaw up and running in, you know, 10, 15 minutes. But if you want to do it the right way, it can take many hours. The cost, that's where it gets, you know, some differences. So OpenClaw technically is free. If you do have a more powerful, you know, Mac Studio or, you know, NVIDIA DGX Spark, something like that, you can run some of these, you know, uh, like a Qwen 3-5, uh, you know, their new small models, right? You, but you, you still can't run, uh, with OpenClaw. You can't run anything at the level of an Opus 4-6, a GPT-5-2, right? You can't. Um, you know, and, and if you chain 5 of them together, you know, you can do it very slowly, but at that point you're spending $25,000. Um, you know, but really where I think I think the difference comes is just the ability to set up, right? And even if you look at AI models, the fact that 19 models inside a Perplexity Computer are just auto-routed and you don't have to do anything, that's a huge advantage. All right. So if at least if you're deciding between Perplexity Computer and Open Claw, who is it best for? I think right now Perplexity Computer probably best for business professionals and executives, non-technical people, people who need a little bit more security and people who just want to see and experience or actually use AgentX Forms without having to spend a lot of time learning, tweaking, improving. OpenClaw, probably a little bit more for developers and technical power users, or if you are ultimately more budget conscious. Because like I said, right now, Perplexity Computer, you do have to be on that $200 a month. But if I'm being honest, if you really want to get the most out of OpenClaw, you're probably going to be spending at least a couple hundred dollars a month anyway on API costs. So, you know, tomato, tomato. All right. So that's enough on the comparison. I do want to show you a couple other results of what we did inside of Perplexity Computer. I did actually see while we were doing that, it did say that the updated version, was done of the bar chart. So let's see. Okay, this, this makes a lot more sense now because now to start with, Apple is at the top and we do have, uh, market cap there. So, you know, here we are, uh, starting in, uh, 2021 and, you know, it's nice and it's animated there in the upper right-hand corner. It says 2021. We have Apple, uh, with the top market cap at, uh, $2 trillion and then Nvidia at the very bottom with a market cap of $332 billion. So I'll play this. This is fun to watch and it's kind of interesting for me, right? As Google, Microsoft, and Apple in 2022 are kind of tussling for the top spot. Nvidia still kind of near the bottom passing Meta. You know, Amazon's just kind of been hanging out in the middle there through 2023, right? Obviously things with with, you know, COVID coming out of the severe COVID period. And now you just have NVIDIA by 2025 all the way at the top, right? With a, you know, $4 or $5 trillion market cap and huge, right? So that was kind of fun to watch. You know, livestream audience, let me know, should I post this in the newsletter? I don't know if anyone cares or not, but at least for me, I always wanted to see seeing, you know, this exact thing. I'm like, this is fascinating and I've never been able to see it. And Perplexity Computer just did it for me with two prompts and probably could have been one. I didn't do a good enough job describing it. So I do want to show you one or two other things because yeah, what you can build in here, literally anything. So in simple prompts too. So in this example, I just said create a gamified task tracker for Mac. All right. So it went through. I can quickly kind of show it, use different skills. It said, okay, for this Claude Opus 4.6 is going to be the best. And it was done. And it told me that I can download this and install it. But it also said, here's a web version as well. So I'm opening it here on my screen and it's a fully working gamified task thing, right? So I can say, you know, publish The Perplexity Computer Podcast. All right, and then there's different toggles here for a 1, a 2, or 3. I'm guessing that's, um, you know, how much time— yeah, how much time you need. Uh, and then a personal or work toggle. I click enter, so it's a working task list, right? I can go check it off. There you go. And it looks like there's some badges, uh, you know, uh, it's a gamified thing called Quest Log. So again, working very nice, very slick. I can go, you know, update it. I can create a database. I could tell it, go create a database, you know, sign me up for whatever I need, or, you know, I'll take over the computer and sign up for whatever I need. All right. And then one last one. And this is one I always do a version of this when I'm testing out agentic models because the agentic models themselves actually do a pretty good job of this. So I essentially have a saved prompt that I use, and I have a— in ChatGPT agent mode, I have it do this every single day. And I usually start my day by reading something like this so I can keep up on the latest in a way that helps me. So I essentially say, here's a bunch of information about Everyday AI. Here's the things that we care about. Here's the things that our audience cares about. I give it some URLs, some things to search. Some reasoning, right? Here's, here's reasons why certain stories matter to our audience more than ever. Right. And then I have it build an interactive HTML website, you know, so nothing crazy here. Right. So simple. Well, not as simple. It's a long prompt, but a simple prompt. And at the end, this is pretty impressive, right? It put together a sortable Daily briefing. So this is the March 3rd top 20 AI stories you need to know. All right. I can sort by all and put them in order. What's crazy is I'm saying like, yeah, this is going to be the order of our newsletter. I can sort by Google, by Anthropic, by NVIDIA, by Amazon. Right. So pretty cool. There's a search bar. Let's see if it works. I'm going to type in 5/3. There you go. OpenAI released GPT-5.3 Instant. So it all works, right? I can click on it. It gave me bullet points and I'm reading these. These are factual. So I'm not seeing any hallucinations. There's an update to the story. That's pretty cool. So again, extremely, extremely impressive with this one shot. I mean, look at this. There's even a light mode and a dark mode. I didn't even ask for that. This is really good. When you talk about the future of personalized knowledge consumption, if you're not doing something like this almost every day, you are getting behind. I like, I'm tired of saying that, but I always show something like this and people are like, oh my gosh, I'm losing my mind. It's like, You need to be doing this, right? Everyone should be doing something like this. Uh, just a way to personalize and consume, uh, information in a way that is going to resonate more with you, but also think about how you can use it for your company, your department, your customers, uh, et cetera, right? So that's it. That's a wrap. Let me give you some of my final thoughts here. All right. So my big takeaways, the capabilities themselves. Extremely impressive. Technically, it is. When I was just watching it work and looking at some of the first outputs without really learning it, right, I was very impressed, right? I will say probably, and this might not seem like a lot, but I've used, you know, I've been doing this everyday AI thing for 3 years. I've used probably at least, I don't know, 500 100+ AI tools, this was a top 10, like, wow moment for me, right? Which is hard, right? When you think about things like, like VO3 and, you know, Sora and, you know, NotebookLM and Nano Banana, right? To say like this is a top 10 thing, that's pretty big, right? Seeing how seamlessly it worked and how you didn't even need to have those technical skills. And I intentionally gave it very vague prompts. In two of the four cases just to see how good it could do. It is extremely impressive. Its ability to spin up sub-agents, assign the correct model to the right task, right? Even in there, I'm pretty picky about like using the right model. And it did really, really good on using the right model for the right task, right? Even the example, right? It used Opus 4.6 for the coding task, right? It could have in theory used Opus,, you know, uh, 4 or 5. Could have used Sonnet 4 or 6, but it said, okay, no, for this, Opus 4 or 6 is the right tool for the job, right? Uh, so that's one thing. Very little human skill needed. The thing I talk about human duct tape, uh, and, and why the, uh, the future of multi-agent swarms is so important. Well, you just kind of saw or heard examples of why that is, right? Especially when those swarms can pull in models, uh, from different models and modes from different frontier AI labs. Connections. You got to talk about connections. So it just has— it integrates with platforms like Gmail, Slack, Notion, GitHub, Salesforce, Notion, right? So it can monitor and schedule as well, right? I should have mentioned that earlier because you can run these things every day, every hour, every week, every month, right? So If you need, you know, the inbox calendar triage, right, you can run that each and every morning, you know, grabbing information from your Google Drive and, you know, pairing it up with something from Slack or Notion. It can do it all, right? It can build you— literally, I've seen examples of this— it can build you an update, keep an updated version of a personal CRM, right? It can use the browser. So, so many capabilities just because of the connections are huge. And I think that's where like a lot of the narrative early on was like, oh no, well, I need OpenClaw, right? Because I need to be able to give it all of this important data. Well, when an agent can, number one, browse the web willingly, but also if you can connect your Gmail, Slack, Notion, all those things where all of your data lives, right? Aside from it being able to to, you know, use iMessage on its own or, you know, things like that. I at least think that Perplexity Computer can do two-thirds of the things that Open Claw can do with zero setup, right? Or one-click setup, bringing in your connectors. So what's this best for now? I think right now it's best for tasks that would normally require multiple models, multiple modes, or separate AI tools that would require that human duct tape. Between systems, right? So as an example, right, if I needed to do some personalized research and create a spreadsheet and a PowerPoint, I wouldn't use Perplexity Computer because I know Claude and ChatGPT can do that in one shot and it already has my memory. And then this takes me to, well, the big thing though with Perplexity Computer is just works. It's very impressive. But here's why I ultimately probably won't personally use this a ton and why I don't think, at least now, most of you should either. All right, I'm going to hit refresh on my, on my page here. Okay, so I just did those 4 little tests, right? Nothing crazy. With a $200 a month plan, you get 10,000 credits. Okay, so this is a credit-based system. In my little test there, I went through about 4,000 credits. So I went through 40% in an hour or so of— and I think a total— I hit the Enter key, I think, a total of 7 times. So it wasn't like I was iterating a ton. All right, that's probably not good. That's probably not good, right? Because the reality You could say, okay, well, yeah, Jordan, it's worth it. It uses all these models, right? I do think especially between OpenAI, Anthropic, and Google, I think that they're going to have something comparable to this fairly soon. And from a value perspective, I'm just not seeing it because the credits burn so quickly. And to pay $200 a month, even that example, Uh, right. And at least the nice thing is, is I can go in and see, you know, how much each of these things, uh, use. So that little newsletter example that I showed you, right, it went through, did some research, put together a little HTML page, right? That took 800 credits. So if I'm doing the math, I can do that what, 12, 12 days a month? I can't even do that every day. So I can't even use that as my daily digest planner, right? So I think the capabilities are extremely impressive. The value, I think for the most part, just isn't there. Granted, you know, Perplexity does have a little promotion going on where you get some one-time bonus credits, but outside of that, I don't see this, at least right now, being worth that $200 a month, right? Because at least for me still, I'd rather spend that $200 with ChatGPT. I'd rather spend that $200, you know, with with Claude, I'd rather spend the $200 on, you know, Gemini Ultra because at least with Google Gemini and OpenAI's, the limits are almost limitless, right? So I almost felt very limited after I ran my first prompt and I'm like, oh my gosh, this ate through 1,000 credits, right? So I'm like, oh, I can only use this 40 times a month. I'm using AI all day. So at least for me, and I think for a lot of our audience, Although I really want to love Perplexity Computer, and I do think it is better than OpenClaw in so many ways, the value just not there for me right now. All right. I hope this was helpful putting AI to work on Wednesday. So now you know Perplexity Computer, what it is, how to use it, and if it's better than OpenClaw, at least for your use case. So if this was helpful, tell someone about it. But if this was helpful, you're really going to find our 2026 AI prediction and roadmap series. Very helpful. So make sure if you haven't already listened to that, episodes 712 and 713. Thank you for tuning in. Make sure to go to youreverydayai.com, sign up for the free daily newsletter. See you back tomorrow and every day for more Everyday AI.

Thanks, y'all. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going! For a little more AI magic, visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers, and we'll see you next time!