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Perplexity Computer vs. OpenClaw: A Precision Look at Practical Value for Business Leaders
The AI landscape is dense with tools claiming to automate knowledge work and streamline workflows. Perplexity Computer, a new premium AI platform, enters the scene promising to minimize human “duct tape” and empower executives and professionals to deploy multi-agent AI models with minimal setup. This article unpacks specific, actionable insights drawn directly from a hands-on analysis, focusing on business value and side-by-side differentiation with OpenClaw—an open-source alternative under active scrutiny from technical users.
AI Agentic Models: From Overwhelm to Orchestration
For many business environments, the influx of AI agentic models (MCPs, ACPs, A2Ws, etc.) creates a complexity burden and operational fragmentation. Traditionally, these models require extensive manual direction—a costly and frustrating obstacle, especially for non-technical teams ([00:00:47–00:01:13]). Perplexity Computer targets this pain point by significantly reducing human oversight, claiming “hardly any human duct tape” is necessary to move from prompt to outcome.
The platform’s standout feature is its built-in orchestration of 19 distinct frontier AI models, dynamically routed according to task, without users needing to select or configure underlying AI engines ([00:19:16–00:21:17]). This “auto-routing” is particularly valuable where knowledge work involves diverse formats—research, infographics, videos, code, and presentations.
Setup and User Experience: Cost, Credits, and Barriers
A critical business consideration—setup time and ongoing friction—differentiates Perplexity Computer from competitors, especially OpenClaw. OpenClaw, while robust and open-source, typically requires hours of setup, frequent manual updates, and a fair level of technical proficiency ([00:24:35–00:24:48]). In contrast, Perplexity Computer offers a near-instant, cloud-based experience: users simply subscribe, describe their tasks, and receive outputs with no local dependencies ([00:24:59–00:25:51]).
However, a major caveat for decision makers is cost transparency. Perplexity Computer’s current Max Plan is priced at $200/month and meters usage via a credit system—delivering 10,000 credits monthly, but even minimal testing can deplete 40% of those credits in under an hour ([00:38:27–00:39:02]). Executives evaluating usage for frequent, business-critical workflows need to weigh the risk of midmonth credit exhaustion against planned ROI.
Output Quality and Use Cases: From Market Cap Visualizations to Personalized Briefings
What is the actual business output? In testing, Perplexity Computer handled multi-step requests without additional programming or prompt engineering. One example: generating an animated, multi-year bar race chart of top AI stock market caps—a process that traditionally requires scripting and data pipeline integration—was initiated by a simple prompt and rerun with improved parameters in minutes ([00:05:33–00:28:46]).
The platform’s value increases in complex tasks such as:
Automated research, customized presentations, and creation of branded documents without explicit input of brand guidelines
Concurrent project execution, where tasks are auto-assigned to specialized AI models for data gathering, analysis, and visual synthesis
Integration with Slack, Gmail, Notion, Salesforce, and other enterprise connectors for seamless workflow automation and persistent cross-session memory ([00:21:47–00:22:12], [00:36:09–00:37:02])
Critically, Perplexity Computer can build interactive dashboards, daily AI news briefings, and task trackers, with built-in QA and output validation using multiple models—features tested and realized with minimal user involvement ([00:30:10–00:33:22]).
Security, Hosting, and Operational Focus: Differentiating Against OpenClaw
Perplexity Computer operates in a secure, cloud-based sandbox. Unlike OpenClaw—which is self-hosted and can be distributed across local or cloud hardware (including Mac Studios or NVIDIA DGX systems)—Perplexity eliminates local infrastructure risks and update/maintenance cycles ([00:25:02–00:26:26]). For enterprises and regulated industries, cloud control, data isolation, and connector flexibility stand out as decisive advantages.
Self-hosted solutions like OpenClaw still appeal to technical teams willing to invest in granular control and lower marginal software costs, but the tradeoff is a notable increase in technical overhead and slower onboarding for non-engineering teams.
Suitability for Business Leaders and Decision Makers
Business owners, executives, and department leads should view Perplexity Computer as a premium, ready-to-use platform for “multi-modal” AI workflows—particularly valuable to non-technical organizations prioritizing speed, data connections, and minimal maintenance. The most direct wins include:
Automated research with up-to-the-minute context and visual presentation
Seamless integration of complex tasks (news synthesis, code generation, workflow monitoring)
Security and data integration, essential for persistent, cross-tool business operations
However, credit constraints and higher monthly costs make it less suitable for companies needing daily high-volume automation. Organizations with existing technical resources or those seeking to run local models may still prefer OpenClaw for its customization and potentially lower variable costs—balanced by far greater complexity and slower time-to-value.
Summary: Key Takeaways for Business Adoption
Perplexity Computer makes multi-agent AI accessible, auto-routes tasks to optimal models, and supports diverse business outputs with little manual intervention.
Setup and operation are instant and cloud-based, minimizing technical hurdles, but heavy business use may encounter credit and cost limitations.
Security and integrations provide critical enterprise value, with connector support for core business tools.
OpenClaw remains attractive for technical power users seeking deep customization and local execution, at the expense of ramp-up time and ongoing support.
When assessing AI agentic platforms, the choice hinges not on technical potential alone but on operational friction, real business integration, and true cost of workflow automation. These hands-on insights help inform that decision with actionable, business-focused clarity.
For a deeper breakdown of how agentic AI platforms align with organizational priorities, visit industry-specific evaluations and user communities for continuing updates on daily deployment and value benchmarks.
Topics Covered in This Episode:
- Perplexity Computer Hands-On Demo & Review
- Perplexity Computer vs. OpenClaw Comparison
- Perplexity Computer's Multi-Model Agentic Swarms
- Advanced Workflow Automation With Perplexity Computer
- AI Model Auto-Routing and Task Chaining
- Data Integration: Gmail, Slack, Notion, Google Drive
- Perplexity Computer’s Price, Credits, and Value Limitations
- Best Use Cases: Perplexity Computer vs. OpenClaw
- Creating Animated Data Visualizations with Perplexity
Episode Transcript
Jordan Wilson [00:00:16]:
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, six hours trying to get OpenClaw set up, but the Telegram stream isn't telegraming and the heartbeat thing isn't heart beating. 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.
Jordan Wilson [00:01:03]:
The MCPs and the a two ways 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'd 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 gonna be going over. I'm gonna show you what perplexity computer is and what it can unlock for even nontechnical professionals.
Jordan Wilson [00:01:56]:
We're gonna go hands on, yeah, live to showcase some of its advanced capabilities. We're gonna compare it to, one of its kind of closest competitors in OpenClaw, and I'm gonna 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. Hi. If you're new here, welcome. This is Everyday AI. My name is Jordan Wilson, and, well, we do this every day.
Jordan Wilson [00:02:29]:
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 gonna 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. Alright. So on most Wednesdays, we do the putting AI at work on Wednesdays where we go quite literally hands on.
Jordan Wilson [00:03:08]:
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, Chad GPT, Google Gemini, and Anthropics 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 task, which I was super stoked to do. Spent hours, you know, trying to plan the show and get everything set up, but, INTROPICS 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 fifteen minutes without it breaking.
Jordan Wilson [00:04:02]:
But why not? Let's start live. What could go wrong? Alright. 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. Alright. Live stream audience, help me out. Let me know if you can, you know, kinda see my screen here.
Jordan Wilson [00:04:29]:
Maybe, zoom in a little bit. Alright. Podcast audience. If you ever want the video version of this, it's always available on our website at youreverydayai.com, but I'm gonna do my best to describe this to you. So I've only been playing around, with computer for couple hours here. You know, just really trying to get my my hands dirty, you 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 gonna 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.
Jordan Wilson [00:05:13]:
Now there's a new option that says computer. Alright. So I have something I wanted to try. Alright. So I'm gonna 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 five years showing monthly prices for NVIDIA, Microsoft, Tesla, Google, Apple, Amazon, and Meta.
Jordan Wilson [00:05:44]:
So the Mag seven AI stocks. So if you've ever seen those, kind of, race I 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 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 gonna work. If it does work, we'll, you know, 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 you know, and as we go into a little bit here on what's a 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.
Jordan Wilson [00:06:33]:
Right? Or 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, you know, 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, ten 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. Alright. So I'm gonna 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, I don't know.
Jordan Wilson [00:07:19]:
Right? I would probably do a, a g p t five two pro query to figure out the best way. I would still try doing something like this, in Opus four six, GPT, five two pro and, you know, you know, Gemini three one 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 gonna work, and if it does work, what the end goal is gonna be. Is it gonna be a a GIF and animation? Is it gonna be a a video? I don't even know, to tell you the truth, if perplexity computer can, generate video or not. So we're gonna 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 three pane layout when you're using computer.
Jordan Wilson [00:08:08]:
Alright. So, if you use notebook l m, you know, maybe this is a little easier on the eyes, and you can, toggle, the right hand column and 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 gonna see all of your other tasks there at the same time. So as I was testing this, now I have four on the screen. I was running three concurrently. I was seeing, okay, is it gonna break? Are we gonna 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.
Jordan Wilson [00:08:57]:
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 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.
Jordan Wilson [00:09:27]:
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. It's looks like it's running a little bit of code, running some JSON, pulling some API responses. And you'll see right now wait. It is it done? It might be done. Let's see.
Jordan Wilson [00:09:48]:
It said the bar yeah. It just finished. It says the bar race chart is live and looking clean. It shows all seven talk, 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 go so fast. I forgot, Perplexity has a really, good and really popular, finance tool. So it says hit play to watch the race unfold.
Jordan Wilson [00:10:15]:
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? Them go up and down, but instead, I mean, we'll see. So it has kind of the, standard bar chart. So right now, NVIDIA's at the bottom.
Jordan Wilson [00:10:46]:
Right? Because this is five years ago. And this is, I'm trying to see what our access is here. I'm guessing it's, billions. Yeah. It looks like it's billions, in market cap. I'm guessing is what it is because it has Meta at the top at the time. Microsoft, this might be revenue. I'm not sure.
Jordan Wilson [00:11:02]:
So, oh, no. It says monthly closing prices. So that's not great either. I'm probably gonna, actually play this for everyone. So at least our livestream audience can see. I'm gonna actually go in and edit this prompt a little bit, and then we're gonna look at some of the other results. So let's go ahead and play. Okay.
Jordan Wilson [00:11:21]:
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 gonna 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 a 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, to see which company is growing the most. But, like I said, to their credit, I didn't really denote that in my prompt.
Jordan Wilson [00:11:58]:
So I'm gonna go in here and we'll we'll just go ahead and see how to do it live. So I'm gonna 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 gonna be maybe a fun one, because I asked perplexity to do my work for me. Alright? So, in this, prompt example here, I simple. I said, hey.
Jordan Wilson [00:12:38]:
I'm doing this live stream. 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 live stream 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 Open Claw? 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 in Office PowerPoint skill, and then it said it's running these different tasks in parallel. So it's kind of, putting out different sub agents, and it's deciding, what these sub agents are going to do.
Jordan Wilson [00:13:25]:
One thing I wish I I 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 live stream. So it went through. It said create it created task list 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.
Jordan Wilson [00:13:59]:
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 three 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 and it used Gemini three 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 live stream presentation.
Jordan Wilson [00:14:46]:
Alright? And then yeah. So it 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 three to five minutes where the video presentation slides are visible on the screen. It's taking these different screenshots, throwing them back into Gemini three 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.
Jordan Wilson [00:15:17]:
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 you know, etcetera. 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.
Jordan Wilson [00:15:40]:
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'd say they're the ugliest things ever, just because I put them together myself. Right.
Jordan Wilson [00:16:04]:
So these actually look a little bit better, but they don't really look fully on brand. Alright. 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 live stream slides. Please update the visuals and try to create a one to one replica of my standard slide setup for live streams. And then I also kind of, wanted to adjust a little bit on the content.
Jordan Wilson [00:16:34]:
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 three panes. The left pane is your normal navigation where you can go to the normal perplexity search, computer, your tasks, your files, your connectors, etcetera. 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 and what I'm scrolling through now here on the live stream 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.
Jordan Wilson [00:17:11]:
You know? For for our normal live stream on Antilda, you also let me know, you know, how close this is. So a ton a ton of information. I'm I'm I'm seeing all the different tools that use, you you know, it switched over here to Claude Sonnet four six, 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.
Jordan Wilson [00:17:55]:
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 eight. Alright. So let's go ahead and see, and then we're gonna actually flip through these slides. Right? Okay. Here we go. Alright.
Jordan Wilson [00:18:15]:
So actually much better. Alright. 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 things missing. Otherwise, if I'm looking at this quickly, it looks pretty much like exactly what we put out there.
Jordan Wilson [00:18:52]:
Right? Aside from a title slide in putting our logo on there, it actually did pretty pretty impressive job. Alright. But I'm gonna I'm gonna 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 gonna 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. Alright. And like I said, you all you need to do is just describe the project.
Jordan Wilson [00:19:33]:
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've actually knew that. I just had temporarily forgotten it. Right? I need longer, context window.
Jordan Wilson [00:19:56]:
I need that 3,000,000 token context window in my own brain. So, you know, for reasoning, as an example, it can use cloud over six. For research, it can use Gemini three one pro. For image generation, it can use nano banana. For video generation, it can use v o three one. For long context, it can use g b t five two. For fast task, 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.
Jordan Wilson [00:20:20]:
Even though I do think chat g b t is probably my most used, AI system, and then, you know, Claude and, Gemini are kind of tied there for second most use. I think if I did more creative things, I would probably use Google Gemini way more just because Nano Banana Pro, and, b o three one. 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 wanna subscribe to all of them. And this does kinda 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 can 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 three one, but, oh, no longer.
Jordan Wilson [00:21:33]:
Now it's Opus four six on web grounding. 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 OpenClaw.
Jordan Wilson [00:22:19]:
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, you 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 it's a super agent.
Jordan Wilson [00:23:02]:
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 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 GBT models, you know, different Claude models, you you know, MiniMax, etcetera.
Jordan Wilson [00:23:54]:
So 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 gonna have to pay for it on the API side. So that's when I thought about that. You know, maybe in five 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 and how quickly they ship things.
Jordan Wilson [00:24:51]:
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, etcetera. 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 your wife or it can't, you know, necessarily, you know, drag and drop files onto your computer.
Jordan Wilson [00:25:52]:
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, ten, fifteen minutes, but if you wanna do it the right way, it can take many hours. The cost, that's where it gets, you know, some differences. So OpenCloud technically is free.
Jordan Wilson [00:26:26]:
If you do have a more powerful, you know, Mac Studio or, you you know, NVIDIA, DGX Spark, something like that, you can run some of these, you know, like a Quen three five, you know, their new small models. Right? You but you you still can't run, with OpenClaw. You can't run anything at the level of an Opus four six, a GPT five two. Right? You can't. You you know, and and if you chain five of them together, you know, you can do it very slowly, but at that point, you're spending $25,000. You know, but really where 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. Alright.
Jordan Wilson [00:27:18]:
So if at least if you're deciding between perplexity and computer and OpenClaw, who is it best for? I think right now, perplexity computer, probably best for business professionals and executives, nontechnical people, people who need a little bit more security, and people who just want to, see and experience or actually use agentic swarms without having to spend a lot of time learning, tweaking, improving. Open claw, probably a little bit more for developers and technical power users, in 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 wanna get the most out of OpenClaw, you're probably gonna be spending at least a couple $100 a month anyway, on API costs. So, you know, tomato, tomato. Alright. 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.
Jordan Wilson [00:28:20]:
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, market cap there. So, you know, here we are, starting in, 2021 and, you know, it's nice and in it's animated there in the upper right hand corner. It says 2021. We have Apple, with the top market cap at, 2,000,000,000,000, and then Nvidia, at the very bottom, with a market cap of 332,000,000,000. So I'll play this.
Jordan Wilson [00:28:57]:
This is fun to watch, and it's kind of interesting for me. Right? As, Google, Microsoft, and Apple, in 2022 are are kind of, tussling for the, 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, you 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, $4.05, trillion dollar market cap and huge. Right? So that was kind of fun to watch. You you know, livestream audience, let me know.
Jordan Wilson [00:29:38]:
Should I, post this in the in the newsletter? I don't know if anyone cares or not. But, at least for me, I always wanted to see, 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 props and probably could have been one. I didn't do a good enough job describing it. So I do wanna 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.
Jordan Wilson [00:30:10]:
Alright. So it went through. I can quickly time to show it, use different skills. It said, okay. For this, Claude Opus four six is gonna 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.
Jordan Wilson [00:30:43]:
Alright? And then there's, different toggles here for a one, a two, or three. I'm guessing that's, you know, how much time, yeah, how much time you need. 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. You you know, it's a little gamified thing called quest log.
Jordan Wilson [00:31:06]:
So again, working. Very nice. Very slick. I can go, you know, update it. I could 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. Alright. 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.
Jordan Wilson [00:31:34]:
So I essentially have a saved prompt, that I use, and I have a, in chat gbt 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.
Jordan Wilson [00:32:13]:
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 3 top 20 AI stories you need to know. Alright? I can sort by all and put them in order.
Jordan Wilson [00:32:37]:
What's crazy is I'm saying like, yeah, this is gonna 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 gonna type in five three. There you go. Because it's OpenAI released GBT five three instant.
Jordan Wilson [00:32:56]:
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.
Jordan Wilson [00:33:19]:
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 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.
Jordan Wilson [00:33:48]:
Just a way to personalize and consume, information in a way that is gonna resonate more with you, but also think about how you can use it for your company, your department, your customers, etcetera. 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.
Jordan Wilson [00:34:21]:
Right. I was very impressed. Right. I will say probably, and and this might not seem like a lot, but I've used, you know, I've been doing this everyday AI thing for three years. I've used probably at least, I don't know, 500 plus AI tools. This was a top 10, like, wow moment for me. Right? Which is hard. Right? When you think about things like like v o three and, you you know, Sora and, you you know, notebook LM and and nano banana.
Jordan Wilson [00:34:54]:
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 four six for the coding task.
Jordan Wilson [00:35:30]:
Right? It could have, in theory, used Opus, you know, four five. Could he use SONNET four six, but it said, okay. No. For this, Opus four six is the right tool for the job. Right? So that's one thing. Very little human skill needed. The thing I talk about human duct tape, and and why the, the future of multi agent swarms is so in important. Well, you just kind of saw or heard examples of why that is.
Jordan Wilson [00:35:59]:
Right? Especially when those swarms can pull in models, from different models and modes, from different frontier AI labs. Connections, you gotta 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 you know, grabbing information from your Google Drive and, you know, pairing it up with something from Slack or Notion. It it can do it all.
Jordan Wilson [00:36:45]:
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, the 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.
Jordan Wilson [00:37:20]:
Right? Aside from it being able to, you know, use iMessage on its own or, you know, things like that. I I at least think that perplexity computer can do two thirds of the things that OpenClaw can do with zero setup. Right? Or, one click setup bringing in your connectors. So what's this best for now? I 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 chat gbt 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 computers 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.
Jordan Wilson [00:38:27]:
All right. I'm gonna hit refresh, on my, on my page here. Okay. So I just did those four little tests, right? Nothing crazy. With the $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.
Jordan Wilson [00:38:53]:
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 seven times. So it wasn't like I was iterating a ton. All right. That's probably not good. It'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 gonna have something comparable, to this fairly soon.
Jordan Wilson [00:39:28]:
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, right, and at at least the nice thing is is I can go in and see, you know, how much each of these things, 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 a month. I can't even do that every day. So I can't even use that as my daily digest planner.
Jordan Wilson [00:40:02]:
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 Chad GPT. I'd rather spend that $200, you you know, with with Claude. I'd rather spend the $200 on, you you know, Gemini Ultra because, well, at least with Google Gemini and OpenAI, 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 a thousand credits.
Jordan Wilson [00:40:53]:
Right? So I'm 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 wanna 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. Alright. 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, yeah, if it's better than OpenClaw at least for your use case. So if this was helpful, tell someone about it.
Jordan Wilson [00:41:26]:
But if this was helpful, you're really gonna find our 2026 AI prediction and roadmap series very helpful. So make sure if you haven't already listened to that, episode seven twelve and seven thirteen. Thank you for tuning in. Make sure to go to your everydayai.com. Sign up for the free daily newsletter. See you back tomorrow and everyday for more everyday AI. Thanks y'all.