EP 464: Perplexity Deep Research - What it is and if you should use it

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Perplexity is getting into the Deep Research game. Yeah, there's a Deep Research game. Now all the big AI and tech companies are trying to play there. So it's in today's show. We're going to do a quick overview of Perplexity's version of Deep Research, talk about what makes it kind of unique and maybe some potential use cases that you could use it for your business. And we're going to do a live run through, we're going to put in a couple of prompts for Deep Research, the Perplexity version, and then kind of see exactly what we get. All right, I'm excited for today's episode. I hope you are too. And welcome to Everyday AI. What's going on, y'? All? My name is Jordan Wilson and I'm the host of Everyday AI and this thing's for you. It's your daily live stream, podcast and free daily newsletter helping everyday people like you and me not just learn AI, but how we can actually use it to grow our companies and to grow our careers. 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It's, you know, whatever month, February, doesn't matter. This is still required listening. So make sure if you haven't already, go listen to episodes 4:43 through 4:47. They're quick about 25 minutes each, so make sure you go check that out. All right, we're gonna have the AI news in the newsletter. You know, there's too much to get to in this. I want to keep it tight. So let's talk about it. And this happened over the weekend. Y', all, like, what's up with all the AI companies now coming up with, you know, new, new releases and new features over the weekend? Don't they know people, you know, need to rest, Right? There's too much going on in AI. We need to rest. Well, I didn't. I played with Deep Research and have been using it quite a bit since it came out. So let's go ahead and dive straight into it and talk about Perplexity Deep Research, what it is and if you should use it or when or how. All right, so if you see this symbol around for our, our live stream audience, right, this futuristic new little Deep Research, that's Perplexities. So yeah, they just dropped this via a blog post and a Twitter announcement over the weekend. It's gotten some mixed reviews so far, to be honest. And you know, I've even had some mixed reviews of it myself. But I said, hey, let's do this live. And again, you know, I appreciate, and I'm glad when people reach out and they're like, hey, Jordan, thanks. We trust everyday AI for, you know, making all of our decisions. Great. Right. But you need to be testing these things out yourself. Let me just say this right now. A couple of use cases, a handful of use cases is never enough to go on if something's good or bad, if there's problematic outcomes or outputs. Absolutely right. Because if that happens once, it doesn't matter if you use it 10 times a month or 100 times a day. If you get one pretty bad output, you know that you have to keep that in mind if, when and if you're evaluating if some of these options are good fit for your company or your department. All right, so here's, here's the gist of what Deep Research is. And this is from Perplexity's blog post. So they said Perplexity Deep Research performs dozens of search reads, hundreds of sources and reasons through the material to autonomously deliver a comprehensive report. Okay, they said right now Deep Research is free for all pro subscribers get unlimited Deep Research queries, while non subscribers will have access to a limited number of answers per day. So yeah, the last I read, if you are, if you have a free Perplexity account, you will get five Deep Research queries a day. That could vary, right? If this gets very popular, they might make that, you know, two. You know, that's the latest number that we heard from OpenAI's version of deep Research. It's not, it's only for the pro subscribers on the $200 a month plan and they might end up giving two a day to free users. But for Perplexities, Deep Research, I mean this is pretty good, right? Because Google's variation is paid only. Right now, OpenAI's is paid only, although they will be rolling this out very limited free. But out of the gate you gotta like this. You know, Perplexity has five free a day. So automatically, just for that, just for accessibility, I think a huge plus for Perplexity. All right, so we're going to get into a little bit more how this works, but we're just going to go straight in and do it live because sometimes these take a while. I actually think that Perplexity's version of Deep Research is going to be much faster than others. So I'm going to go ahead, I have a couple of pre typed out prompts that we're going to put in to Deep Research and then we're going to go backwards from there. So we're going to give it some time, we're going to kind of launch all of these and go. So the first one that I'm doing, I'm saying provide in. And this is the exact same thing I did for oh one's Deep Research. I'm doing two of the same. And then I have another one that I added. So I'm saying provide an analysis of Nike's latest quarterly performance compared to Adidas and Under Armours. Include key financial metrics, recent earnings commentary and relevant market news. Cite all sources and highlight any discrepancies in analyst opinions. All right, so this one is, it's pretty, it's pretty, pretty detailed, right? We're not giving Deep Research a lot of room to operate and make its own decisions. Right. So in this example we are being a little more open ended. All right, so let's go ahead and do our second one. And again, these first two are the same things that we did for our episode. If you want to go in and listen to that one or watch that one. Let me see, what, what number was that? That was Deep research. There we go, 454. So if you want to go listen to that, it was episode 454. All right, so the second one that we're going to be doing in Perplexity, Deep Research, let me go Ahead and share my screen there for our live stream audience. More open ended, right, just saying, find the latest news and information about Deep Seek. All right, there's another one that was, you know, controversial, we'll say that. But yeah, if you care about Deep Seek, if you want to know the truth, go listen to episode 460. All right, so that is our second Deep Research query that we put in and then we're going to come back and check on them in a couple of minutes. So then the, so first one was an analysis of Nike's latest quarterly performance compared to Adidas and Under Armour. The second one, more open ended, find the latest news and information about Deep Seek and then we're gonna go a third one here. All right, we'll see. Well, first we'll even see if we can run three concurrently. You know, Google, Google's version of Deep Research as an example only allows you to run two. All right, so now my next or my last one here. And just FYI, I am on the $20 a month paid plan for perplexity. So in this instance I have unlimited usage of Deep Research. All right, so now the last one, this one, like I care about fact checking, right? And for some of these things, I don't know everything about Under Armour and Nike, right? Although I did spend better part of a decade partnering with Nike. I don't know all the underground, all that information. Deep Seek, I know a ton because I just did a very in depth report. I probably spent, I don't know, at least 30 hours, I kid you not, over the past couple of weeks just reading, researching about Deep Seek. But at least with this one about myself, I know this. So I'm saying tell me everything about Jordan Wilson who does everyday AI from birth until today. Make it creepy in depth. Yeah, sometimes. I have tried a lot of these before with other Deep Research type tools and this is a good one for me at least, right? I know myself. I, I give enough information to the, to the tool and there's plenty about myself on the Internet, right? I've, I've written stuff on some of my old company websites. You know, I was a journalist for 10 years, you know, so there's plenty, I've been on other people's podcasts a lot. So you know, there's plenty of information about me on the Internet. All right, so we have those three queries. So in about five or 10 minutes we're going to go back and check on those. All right, but let's go ahead and jump back in to a little Bit more about Perplexity's Deep Research. And you already just heard me say this, right? The exact same name, everyone's got the same name. Deep Research, right? So Perplexity now has Deep Research. Google has Deep Research, they were actually the first to market with it. And then OpenAI has deep research as well. So a little confusing, right, when people are just talking about Deep Research, but it almost seems, it's, it's, it's taking on this kind of co pilot kind of mentality, right, when everyone has a co pilot. You know, I guess technically Microsoft GitHub co pilot was one of the first co pilots to market. And then Microsoft obviously has the Microsoft 365 copilot, but there's so many other like quote unquote AI co pilots and it's almost just become this general term. And it looks like that's what's going to happen with Deep Research, right? It looks like that is just going to become a term. And I'm guessing we're going to see this from a lot of other companies as well. It's starting with the big companies that already had the data, they already had the architecture and systems in place, right? Because all three of these, right? So Google with their Gemini, Perplexity and Open AI, they all already could browse the Internet, look at a lot of web pages and essentially try to decipher what's good and what's not and put together a short response, right? So the biggest differences with this Deep Research, if you haven't used them versus a traditional, right, like Chat GPT search or using Google Gemini, which is now finally better connected to Google, right? You might be wondering like, what's the difference? Right? Those queries that I put in, you could probably run those in Chat GPT search or a normal Google Gemini and get a pretty decent result. Yeah. Yes. So the difference is these deep researches, well, they do that, they take it much, much deeper and the three of these work in a very different way. There's some similarities between the three. There's a lot of differences. But you know, also it's worth noting, right, because everyone's like, oh, everyone's copying Google. Well, maybe because actually before Google released Deep Research, about six months before that, there was some reporting and we even covered it@everyday AI. There was reporting that OpenAI was working on a product that did quote, unquote deep research. So even though, yes, Google was the first to market with it, the kind of deep research kind of feature had been tied to OpenAI many months before Google's version came out. Hey, live stream audience. I'm thinking about just, well, no, I am going to do it. I'm going to do a, we're going to do a comparison of all three of these tomorrow because I was going to do that for today's show and then I'm like, oh man, that's going to accidentally turn into like one of those hour long shows and I don't want to do that. Right? I get, I get tons of emails from people. They're always like, oh, I listen to you on, on my runs, right? And I, I just run until the podcast is over. And I'm like, that's dangerous because sometimes I accidentally do like an hour plus podcast and maybe you weren't prepared to do a half marathon or a 10k. All right, so I'm going to try to keep today's episode short, but tune in tomorrow if you're interested. I'm going to try to get a, a rubric of sorts. You know, aside, I'll probably do the same three questions I just did here and a couple of others and kind of go over some of the pros and cons, comparison, similars, different, you know, the differences in these tools. But for the most part, at least as it pertains to Perplexity, I think there's some, some big advantages. I think speed, I think it is a little faster and cost. Right. So I mean, those are two of the biggest factors when people are looking at, right. When choosing which AI tool do I want to use or should my company be looking at? So right away, at least as of today, Perplexity is the only deep research tool that you can use for free. Right. Like I said, OpenAI said that they're going to be rolling out, I think to a month because their deep research tool is utterly fantastic. All right. But hey, Perplexity already has a, a nice competitive advantage. But tune in tomorrow. We'll be doing a much deeper dive on them. All right, a couple other things, and this is from Perplexity. So they said that they're doing great in terms of benchmarks on this. So there is a new kind of a newer benchmark out there called Humanity's Last Exam. So a lot of times it's like you're trying to figure out like, hey, are these models good, right? Or what's, what's the difference between, you know, GPT4O latest, the one that just rolled out like a couple of days ago, versus the GPT4 latest that was out, you know, a month ago or three months ago or you know, the Gemini 1206 versus the Gemini 121. Right. It's, it can be hard, especially when these big AI tech companies, you know, aren't the best at naming their models and you always don't know which version of a model you're using. Benchmarks are extremely important, right? But there's been a lot of, you know, talk over the last couple of years that benchmarks aren't as important, you know, in 2025 maybe as they were in 2023. Mainly because, you know, the argument is a lot of these benchmarks kind of get thrown into the training data. So you know, the argument is well, well the model is just, you know, know, measure or they essentially memorize. You know, a lot of this, you know, a lot of these questions that are on these benchmarks, right? So this new one, Humanities last exam, it's pretty difficult, right? And it's all this newer information that requires a lot of, you know, reasoning, a lot of logical thinking and just a lot of, you know, math, coding, stem, all these different aspects are required to get a good score on this Humanities last exam. So Perplexity did share that their Deep Research research version scored a 21 on humanity's last exam, which is the second highest score out there next to open AIs which I believe they got a 26%. I don't understand why Perplexity did this if I'm being honest. Because it's a different type of tool, right? So we, we have these, you know, kind of the, the transformer tools or the transformer models, right. Then you have now your reasoner models and those are primary, probably going to get merged anyways. So we'll probably have a show on that and what that means. So you have your, you know, your quote unquote traditional transformer models, right? So that's your, your GPT4O, your Gemini 2, your Claude 35 sonnet, etc then you have your reasoner models. You, you have these models that think OpenAI 0103 mini 03 mini high. Right? Then you have, you know, Gemini thinking and now you have these deep research models which are essentially fine tuned versions of that usually include a reasoning model as well. So I believe, and hey, I guess I'll find out when I do a little more research. But I do believe that OpenAI's and perplexities uses a reasoning model where right now Google's does not. And you'll kind of see that hopefully in the results and in our show tomorrow when we compare them all. But I didn't understand why Perplexity included this. Because essentially there's only two deep research models that have done this humanities last exam benchmark. And Perplexity isn't second. But, you know, I don't know, it is what it is, I guess, you know, they're like, all right, well hey, we can say we're second on the list and put up a bunch of, you know, other models that it doesn't necessarily make sense because they're different classes. Right, But I get it, right? There's not, you know, 50 different, you know, deep research models. There's not 50 different Reasoner models. But that's important when you look at benchmarks and you hear people talking about this, right? So let's say you're the CTO at a medium sized company and someone's coming out and you know, spitting these facts out at you and being like, oh, we got to start using it. Okay, well, hey, is it a transformer model, Is it a reasoner model or is it a deep research mode? Right. That you're looking at these, these metrics. It's important to understand the difference. All right, so how the heck do you use this thing? Well, it's simple. And then we're going to check in here on some of our results. And you know what, I might actually, I wasn't planning on this. I might run some of these same queries in the reasoning mode. So Perplexity, it is a very unique product. Right. And I've been a paying subscriber to Perplexity. Probably not the day it came out, but the week or the month pretty early on. And if I'm being honest, I think very early on I was extremely, extremely bullish. Right. Loved it. Been a little bearish over the last, you know, year or two or maybe year. I feel in my experience, and again, this is limited, but I've read plenty. I feel Perplexities hallucination rate has not kept up with the rest of the industry that has Internet connected models. And we'll see, I mean, we'll see if I get, you know, in, in our little results here if that happens. Anyways, there's a lot of different things to keep in mind about Perplexity. It is a little different. For the most part it is an answers engine. So think of it more as a direct competitor to something like chat GPT search versus, you know, in OpenAI's, you know, GPT4O or something like that. So you can choose a different large language model that it runs off of. Right. So Perplexity does have their own model called Sonar, but I don't think anyone that uses Perplexity uses Sonar for the most part, although they did just release an updated version that I think is really good in making it available via the API. But for the most part, when you're using Perplexity, even if you have a free plan, you can use their Pro Search. So for their Pro Search, again, this is what Perplexity's kind of bread and butter is. It's going through a lot of websites. It was, you know, one of the first players in the AI space that did exactly this. That did. It was the precursor for Deep research, right. So one of the first iterations of Perplexity, and why I think it was pretty amazing, is you would ask it a query, you would choose a model, right? So if you had the paid version, you could choose, hey, I want to use, you know, GPT4O or Cloud Sonnet 3.5. So you can choose a model that actually powers the Answers engine. And then the, even the first version of Perplexity, what it would do is it would generally go to 10 to 20 different websites and it would try to get a better idea of what you were asking. Right? So it's. It was almost like unsteerable rag, right? So one of the things, when you're working with a model, whether it's GPT4O, whether it's, you know, Google Gemini, you know, Pro 2, whatever the model is, you always have to keep in mind there's probably a lot of old data in that model. Right. Even if the knowledge cutoff, as an example, is June 2024, like GPT4O, a lot of that information that is in the data training is probably very old, Right. Just because. Right. There's people get this false sense of security when they see a knowledge cutoff date, right? And they're like, oh, June 2024. Okay, that's not bad, right? No, that just means that's. That's when the knowledge cutoff was. That doesn't mean 100% of what goes into these models that have trillions of parameters. That doesn't mean that it's all current and all correct and all accurate through June 2024. Absolutely not. Right. A lot of these data sets are probably offline and have a lot of older data. You hope, through the reinforcement learning with human feedback or, you know, the RL stages where humans are going in and training the model, that they're taking out some of that old data. But it's not always the case. Right. So Large language models when they're using their own kind of internal data, it can be old, it can be outdated, which is why even the original Perplexity, I think, was a very innovative product, right? Because what it would do, essentially it was this version of Rag, right? Almost uncontrollable, though, right? So before it would kind of rely on whatever base model you chose, it would first usually go to 10 to 20 websites and, you know, verify that information, see if it's up to date. Right? But then use also the training data in whatever model you chose. So that's kind of how Perplexity has always been ahead of the game when it came to kind of this concept of deep research, or pairing Internet research, multiple pages with a large language model. But it's, it's actually kind of confusing, right? And I think Perplexity is now suffering from kind of what OpenAI and Google are as well. There's just too many models, too many features, right? And if, if you're an average user, it can be a little difficult because if you're looking at my screen right now, right, I'm on a Perplexity Pro plan. So I have Auto, which it says best for daily searches. I have Pro Search, which essentially does three times more sources and detailed answers. Then I have what we're going over today, which is Deep Research. And then we also have reasoning, right? So this is the Reasoning search. Think of it like the normal Pro search, but the Pro search uses a transformer model. And then the Reasoning search is like a Pro search, but it uses a reasoning model. You can choose either O3 many from OpenAI or R1 from Deep Seq. So it's a little confusing, right? You know, in terms of like, hey, how should we be even using something like Perplexity if you have a paid plan? All right, so, but in this instance, when we went in, we went in, we chose Deep Research, all right? And then here's what Perplexity said. So they said it takes about two to four minutes. And they said it's reasoning, it's research with reasoning. This is what Perplexity says. So it says, equipped with search and coding capabilities, Perplexity's Deep Research mode iteratively searches, reads documents and reasons about what to do next, refining its research plan as it learns more about the subject areas. That is big, and that is one of the differentiators, at least from Google. Again, tune into tomorrow's episode. We'll tackle that a little bit more in depth. But that's important to know because Perplexity's Deep Research. It is iterative, right? So if it starts going down path A to, you know, answer your query. But hey, oh, something's changed. Maybe it needs to go down path B or path C. That is something that Google's Deep Research does not necessarily do by default in OpenAI's does. So that's important. It does use a reasoning model to steer itself to give you the best answer using the Deep research. All right, so let's go ahead, let's check in, let's look at some of these results. All right. And then we'll kind of give you my hot take. All right, so here is the first one we did. Provide an analysis of Nike's quarterly performance compared to Adidas and Under Armour. Include key financial metrics, recent earnings commentary and relevant market news. Cite all sources and highlight any discrepancy in analyst opinion. All right, so I'm going to zoom out a little bit here. All right, so you'll see what happened in Deep Research. I always encourage people, right, especially with these reasoning models where you can kind of see their chain of thought or you can see their rationale. You know, open AIs is a summarized chain of thought, but they just improved the summarized chain of thought about a week ago. All right, so you can go through and see how Deep Research is doing this. So the first thing it says, I need to search for information on Nike's Q4 2024 earnings report to gather key financial metrics, recent commentary and market news. This will help establish a baseline for comparison with Adidas and Under Armour. I am looking for specific data such as revenue, net income growth rates and notable trends or challenges highlighted in the earnings commentary. So then essentially what it did, it went to a search engine and all it typed in was Nike latest quarterly performance earnings report 2024. And you'll see for this, its research it went to. Let's, let's count them. So we got 4, 8. It went to 10 different searches. All right, so some of these searches, interestingly enough, Perplexity does have a new. I'm just going to go ahead and open this. So Perplexity does have a new kind of finance mode. So it essentially launched three different queries for Nike, Under Armour and Adidas. So I should probably bring up the correct, the correct slide there for our, our live stream audience. Right. So it launched into this kind of financial view of Nike, which is super helpful. Right. And then we go back and we look, it did the same thing for Under Armour and Adidas. So it ran some of it ran Additional queries on Perplexity, which I think is really cool. It went to investors.nike.com went to a couple Yahoo Finance articles. It went to CNBC as well. All right, so pretty good. So then you can see it says from the most recent search, I found detailed information about Nike's latest quarterly performance, including its Q4 2024 results. All right, so then it's, it's starting to report back, right, what those results were and then it's going on to its next version. So it's saying now I need to gather similar information for Adidas to compare its financial performance strategies and market challenges. All right, so then similarly it looks like it goes into about, let's see, about eight or nine additional searches looking at Adidas. All right, it replies back, then it says, I need to do the same for Under Armour. So it's doing, you know, both traditional open ended searches, right? Under armor Q4 earnings report, same thing. It went to about nine different sources. And then from here it's, it's, it's pretty impressive, right? And this is why I always encourage people, if you want to become better at using large language models, especially ones that reason and are connected to the Internet, you're always going to get better results the second time you do this, right? Because what you can do is you can go and learn from, from exactly what this, what this model did, right? So this is mimicking human behavior. When you give it the ability to use a reasoning model and it can change its original course of research. This is huge. So in the same way this is like if you could go give an assignment to an analyst on your team and you were, and you also paid someone to audit them, right? And write down every single step and then they gave this to you, right? If you are smart, let's just say you're, you're a senior analyst, right? This is maybe what a junior analyst might do. But you could go through and see exactly step by step what they did correct and what they did incorrect. And then you can go through, in theory you could run this again, right? And then have a little bit more pointed or a little bit more detailed instruction. So in this prompt it wasn't super restrictive, it wasn't, you know, pre steered too much. It was kind of open ended, right? So that's where I think it's extremely important. Don't just use these to save time, right? Use these to improve your next round of outputs. I always tell people, never run something like this, a deep research query once, don't do it. Same thing. Even with a reasoning query, never run it once. If you really want to, you know, grow your company, if you really want to become the best person in AI, study the chain of thought. Study what these models are doing under the hood. That's how you get better. You don't get better by just blindly, you know, trying to hand off as many tasks as possible. Right, because these models still aren't that great. Right. They're the worst today they ever will be. But you can make them better if you understand how they work. All right, so let's just go ahead, let's skip to the end here. We have an answer from Perplexity Deep Research. It says comparative analysis of Nikes, Adidas and Under Armour and Q4 Performance and Market dynamics. All right, so first we have Nike's, we have a couple paragraphs here about Nike, their financial performance, operational challenges, market reaction and analyst sentiment. Same thing with Adidas, Financial highlights, strategic drivers, analysts and credit outlook. Under Armour, they said it's a lagging contender. All right, so financial and market position, strategic missteps, analyst perspectives. Then we have a nice chart here. Did a really good job. So we have the quarter 4 revenue, full year revenue, gross margin in Q4 operating profit, market cap. Great, great charts here from Perplexity. Unfortunately these, these metrics are not cited in the chart, which I wish they were. Right. I could go through in the, I can go through in the rest of this answer and probably find those citations. Right? And this is one of those things I would say in my follow up, I would say, hey, make sure if when creating a chart to cite each individual statistic in the chart because again, human in the loop. This changes this probably. You know, it's not the most impressive deep research I've ever seen, but it was fast. You know, I don't see anything off the top of my head that looks inaccurate. But again, I don't know this information 100%. So the, it wasn't actually a very in depth report. It was extremely detailed, extremely specific. It looked like only a couple, couple hundred words. So then what you can do at the end is you can a couple of things. Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more but can't really get traction to find ROI on Genai. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have, have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GEni. So you can click View Sources, you can rewrite it if you want. You can export it as PDF markdown or create a perplexity page that then you could share with someone or you can click share and then this will send a copy of this chat to whoever you want to share it with. All right, so let's go ahead. We'll quickly tackle the other ones. All right, so now this one was find the latest news and information about Deep Seek. So it ended up going to 55 sources. All right, I'm kind of looking through here and seeing kind of where it went and in what order. So first it was just trying to get some general information. Then this is good. It's saying I need to search the web specifically for news and updates about deep seek from February 2025. This is great. Love seeing this from Perplexity. So then it's searching deep seek Feb 2025 to make sure it has the most up to date and accurate information. That's huge. Especially in a developing story, freshness is extremely important. It's talking about the Texas Attorney general launching an investigation. All right, a lot of information on the Texas thing here, which is not terribly important, but it's important. All right. It's going through some Wikipedia sources, a New York Times article, Economic Times, and then it says writing the research report. All right, so let's see what we came up with here. I'm just going to go ahead and read probably the first one or I'm just going to read the first couple of sentences here. So the rapid ascent of Chinese artificial intelligence startup Deep Seek has reshaped global technology markets, regulatory frameworks and competitive Dynamics in early 2025. Founded in 2023 and backed by hedge fund High Flyer, the company achieved unprecedented cost efficiency through architectural innovations like mixture of experts and multi had latent attention enabling its models to rival industry leaders at 1/10 the training cost, while its open source deep seq R1 model became the most downloaded free app in the US Apple Store by January. In January, the company faces mounting security concerns, with nine governments banning its technology on official devices. This analysis examines deep teaks. Deep seeks technical breakthroughs, market disruption, and the complex geopolitical tension surrounding its rise. All right, so the, the recap was okay, you know, I didn't see anything. I mean, I saw things I could, you know, pick, pick apart a little bit, but for the most part, it did a pretty good job. It was pretty fresh, pretty relevant, no hallucinations. There were probably some. Some gray area there and some. Just some overly general responses, but nothing that was factually inaccurate. Right. There's things that were up for discussion, but, you know, nothing. Nothing terrible here. All right, so then it goes through looking at the architectural innovations driving cost efficiency, the market disruptions and competitive response. All right, Security concerns and regulatory backlash, strategic implications for AI developments, emerging frontiers, multimodal capabilities. All right, that's good. It's. It's talking about, like, Janus, Janus Pro, kind of. Deep seeks version of Dall E Audio Visual. All right, so not, not bad, right? Again, not a ton of information. Right. So if you wanted to read something and, you know, digest a couple dozen pages and really get a lot of information, at least at first, I'm not seeing this from Perplexity's version of Open or. Sorry, of Deep Research. So did it research deeply? Sure. Right. Did it give you a very long and in depth report? No. Essentially it gave a bullet point of facts. The good thing, the good thing, which is normal with Perplexity is, you know, after some of these facts in the body of this response, you can hover over and look at the different sources. Right? So when it says, you know, like as an example, when it says it became the most downloaded free app in the US Apple App Store by late January, I can hover over and see. That was from the BBC and we forum. Okay, so you can go through and check some of the sources. All right, so overall, again, not bad. Not the best version I've seen, but fast free. So hey, if those are two things you care about, if you care about speed, if you care about price. Perplexity. Not hating it. Not hating it. All right, let's try the last one. And this is one of those ones where it's like, I can't 100 very quickly look at accuracy when I tell it to be creepy on myself. I can't. All right, so this one, I said tell Me, everything about Jordan Wilson, who does everyday AI from birth until today, make it creepy in depth. All right, I. All these prompts, FYI, I ran them multiple times. All right, I ran them before because I also want to see how or if the product is getting better. Right. Because the Perplexity CEO there is some terrible responses that came out immediately. They said they were going to update it, so I'm actually hoping that this version did a little better because it's easier to fact check something on yourself, and I encourage you to do that. Right. If you want to see if any of these tools are at least worth considering for your company, ask it to do something incredibly in depth, almost at a creepy level on either yourself, your company, your department, if you work at a big enterprise, you know, your competitors, whatever it may be, first test it on something that you know like the back of your hand and see if, Number one, is there going to be anything incorrect? Number two, is there anything maybe you didn't know or forgot about? Right. At least for me, I don't think it's going to be anything I didn't know, but might be something I forgot about. All right, so let's quickly check the accuracy here. All right, so it says Jordan Wilson, architect of accessible AI and pioneer of everyday generative intelligence. All right. Okay. It's talking about a Midwestern youth. All right, sure, that's correct. Early formation in academic foundation. So it says childhood precursors to technical orientation or technological orientation mainly. I'm just looking. I got it. Got in something I used to a B test my lemonade stand as a kid. All right, it looks like I talked about that on someone's podcast I was on. All right, so that's correct. It says. All right, it says I went to siu. That's correct. Looks like it pulled that in from Coursera. I did a course there a couple of years ago. Looks like it looked at my LinkedIn profile as well. All right, so from newsrooms to boardrooms, let's see if I got this correct. All right, so it's talking about my background in journalism in the early 2000s. That's correct. Strategic pivot to digital enablement. All right, so media production to digital strategy through leadership roles, triple threat mentoring, working with Nike and Jordan Brand. Pretty good genesis of Accelerant Agency. So, yeah, one of my two companies. So it says in 2015, I founded Accelerant Agency. That's not correct. It wasn't 2015, it was 2017. But not terribly right. One small detail, but incorrect there. Let's See at everything else on here, looks pretty good. Looks pretty good. All right, it's talking about a little bit of the, the PPP courses. It's talking about everyday AI revolution, launching everyday AI in April 2023. Yep, that's pretty good. All right, it got the time of our live stream, 7:30am Central Standard Time. Pretty good. Bringing in some information from some of our more recent deep research. You know, just kind of meta. Right. It just brought in A open AI's deep research in the deep research from Perplexity. All right, so I'm looking through here. Nothing on this instance that's really wrong so far. Okay, here we go. This, this doesn't look. Client engagement models. This doesn't look like anything I've done. So it says Wilson's consulting arm with Everyday AI employs a phased implementation strategy, AI readiness assessment, ethical framework development, pilot program design and skill optimization. All right, so we have our, we have our first set of hallucinations. So that made it up. And also interestingly enough, there's no citations in that instance. So it made up a client engagement model that we do at Everyday AI, which we don't do. That says notable client successes include a Midwest manufacturing firm achieving a 40% reduction. Yep, that's not true. We'll see where it sourced that from. So it looks like it brought in some, maybe a random transcript from our website talking about. Yep. So I'm looking at the website, just hallucinated that and it tried to source it and cite it, which was incorrect. All right, here we go. More, more, more hallucinations. So central to Wilson's consulting philosophy is what he terms the human amplification principle. All right, let's see. That's wrong. So it, with some other website that, pulling in information from other parts of the page, it looks like, so making up some stuff. So then it says media empire and thought leadership, the Everyday AI podcast ecosystem. All right, so those are all correct. All right, so it says our daily newsletter achieves 63% open rates through a structured content framework. We have very high open rates. It's not 63%. So yeah, toward the end we started to get a lot of hallucinations. Right. But I will say this, at least these hallucinations were on brand. Right? It's, it says, it says I have collaborated to some of these research partnerships and white papers, which I did not do. It says I have 37 peer reviewed papers again, so it's, it's pulling things from, you know, the Chicago AI week. So let's even go look at that, so a lot of these things that were hallucinated was from this page. I did a panel at Chicago AI Week and none of this information is even on there. There's like no information on this. There's a little very short bio in the session that I led. And that's it. Right. So it's, it's interesting that perplexity is making these hallucinations and citing them to certain pages that have like no information on them anyways, Right. So, yeah, I will say the bottom half of this. I'd have to look. It looks at least about 40% hallucinated. Not good. Right. So the good thing is, is I can go up here, I can look at these sources, right? So it looks like it went to 23 sources. So I could see like, oh, if it's pulling in information that seems to be hallucinating, I could click on the source and I could click, you know, remove source. And then it would rerun that whole, that whole kind of deep research. Right. So you know what? I'm going to do this. I was going to do this at first, but I think it's probably important to talk about. So let me do. I ran this exact same deep research query. Like I said, I ran all these twice. So here's the first one. The first one that I ran, I think it was over the weekend. So I said, same thing. Tell me everything about Jordan Wilson, who does everyday AI from birth until today make it creepy in depth. You'll see. In this instance, it went to 243 sources, right? But if I click the source list here, I saw this and I'm like automatically, I don't know what the heck is going on here, right? It's, it's doing all these things from, from Reddit. I looked at about half of these. None of these are about myself. They're not about everyday AI. A lot of these sources are. But I, I, I, I noticed that it pulled in a bunch of sources that were extremely irrelevant. Right. At least on this second variation where it looks like it maybe hallucinated about 30 to 40% of the time, at least it brought in sources that looked somewhat relevant. Whereas the first time I ran this exact same query, I mean, it just brought in it mainly Reddit. I don't know why. It was bringing in dozens of irrelevant Reddit threads. And then in the actual deep research report, I would say it was about 60% hallucination. So it did get some things correct. Right? It got the, you know, my undergrad and grad school didn't get the years correct, but I got the, the schools correct. It got, for the most part, some of my original places of employment correct. But it started hallucinating very, very early on, making a lot of things up. You know, in, in, in the first, the first variation, it was bad. Very, very bad, right? So imagine if you don't know, right? This is why it's important. Imagine if you don't know about the topic that you are deep researching. I haven't seen this level of hallucinations. I mean, we'll, we'll see. We're going to do the exact same thing. But I haven't seen this same level of hallucinations with Google Gemini's deep research. I haven't seen it with OpenAI's deep research. But again, on something that I know a lot about myself everyday AI, I was a little concerned with what happened with Perplexity's version of Deep research. So is it free? Yes. Is it fast? Yes. But at what cost, right? If you're trying to learn something, if you're trying to research a potential client, right? For a huge sales meeting, if you're trying to put together some information for a big presentation for your board, you really got to know your stuff, right? I would not at this point feel safe in copying. Not, not just copying pasting, but taking the ideas from Perplexity. Deep research, right? So the way, Let me say this, I'm not going to be using it. I'm not. Right? There's a certain level of truthfulness you have to get. I understand. Humans hallucinate, right? There's inaccurate information on the Internet. The Internet hallucinates, right? But when you're using a reasoning model and you're going to these dozens or hundreds of websites, right? This should have known that all these random Reddit threads, right, where it's like, I'm just reading some of the names. Please help. I am a new dad. It's been nine weeks. That's nothing to do with me. I think it's kind of lame when people criticize or talk trash, right? That's not about me, right? These, these Reddit threads. Why do some software engineers say leap code isn't worth it? Suggest me a book that speaks to what the next few years. You know, Wilson released a statement that's an NFL thing about, you know, the old quarterback for the Seattle Seahawks, right? So the fact that Perplexities, the first version of this deep research brought in just any, any model, any person, any, anything with a brain should know if it can't match these Sources up with me. My name is Jordan Wilson. I run everyday AI. You got my background correct. So why are you bringing in dozens or more than a hundred of sources that are irrelevant and then seemingly pulling from that? Right. Not good. So could this be okay? Ish maybe. Right. But I would be very, very careful. Don't just jump on and use Perplexity Deep Research because it's free, because it's fast. You really have to know your stuff and you really have to pay attention. And I think the role of the human in the loop, if you are using Perplexity Deep Research, the onus is on you. Right. So I don't know from a time savings perspective if I would ever use this. At least as it is now. Hopefully it improves. It looks like it has improved in the last 24:48 hours since I first ran this same search. But still, even in the version, I just ran out a lot of hallucinations, a lot of concerns about accuracy and truthfulness. All right, so I hope this was helpful. Like I said, we're going to do the exact same thing tomorrow. We're going to take a look. We're probably going to do those same three prompts. It's going to be a faster one, but we're going to look at Google Deep Research, Compare it to OpenAI deep research and Perplexity Deep Research and we're going to see which one is best. Is it better to use the free version and maybe just have a little bit more control? Right. And only use it for something that you are innately aware of? All right, so we're going to be doing that tomorrow. So I hope this was helpful. If so, please share this with your network. Yeah, you can use everyday AI as your personal cheat code, but I'd really appreciate it if you'd share it, help other people out. Learning AI is scary. It is time consuming. You really have to know a lot. That's why I do this for you. Right? And I do this live. This isn't some, you know, polish. And I spent 30 hours editing to get it just right and to show a certain message. This is all live, y'. All. This is live, unedited, for the most part, unscripted, showing you the real part of AI, which, as you saw there, there's promise and there's peril. So you, you really have to be wise and intentional about how and if you even use some of these tools. All right, so thank you for tuning in. Join us tomorrow, we're going to do the big breakdown, all three of them. Please also go to your everyday AI.com sign up for the free daily newsletter. Thank you for tuning in. We'll 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.