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Harnessing Perplexity Deep Research: A Pragmatic Approach for Business Leaders
In today's fast-paced technological landscape, AI's ability to conduct deep research is an asset that businesses cannot afford to overlook. This overview explores the unique approach of leveraging perplexity in AI for deep research, its significance in the business world, and practical tips for implementation.
Understanding Perplexity in Deep Research
In the realm of artificial intelligence, perplexity serves as a metric of how well a probability model predicts a sample. However, when applied to deep research, perplexity refers to AI's ability to navigate vast data sources to autonomously deliver comprehensive reports. This approach combines automated searches, thorough document analysis, and reasoned summaries, providing businesses with concise, well-reasoned insights.
Unique Attributes of AI-Driven Deep Research
Using perplexity for deep research stands out due to its speed and cost efficiency. Unlike traditional research methods, AI can rapidly process data from multiple sources, offering a holistic view of a subject. Notably, perplexity-based research supports free usage for a limited number of queries, democratizing access to deep research capabilities, which would traditionally require costly tools or human resources.
Potential Business Applications
Business leaders can apply AI-driven deep research across various domains:
Competitive Analysis: By comparing financial performance, market strategies, and analyst sentiments, AI can aid in developing comprehensive competitor profiles.
Market Trends and Innovations: AI can identify emerging trends and provide market news, giving companies a strategic edge in adapting to market shifts quickly.
Due Diligence: As businesses expand into new ventures or alliances, AI's ability to conduct thorough background checks can inform strategic decision-making.
Personalized Insight Development: Organizations can use AI to create in-depth profiles on key personnel or companies, enhancing networking and partnership strategies.
Challenges and Considerations
While AI offers substantial benefits, business leaders need to remain vigilant about the accuracy of AI outputs. It's essential to employ AI insights as a starting point and conduct human-led evaluations to ensure the reliability of the data, acknowledging potential discrepancies or hallucinations in AI-generated information.
Conclusion
For any business owner or decision-maker, integrating AI-driven deep research can transform strategic planning and operational efficiency. By maximizing perplexity's capabilities, companies not only save time and resources but also gain valuable insights from diverse, up-to-date data. Whether exploring new markets or analyzing competitors, adopting AI for deep research signifies a leap forward in data-driven decision-making.
Topics Covered in This Episode
1. Breakdown of Perplexity Deep Research
2. Comparison with Other AI Deep Research Models
3. Live Demonstration and Deep Research Prompts
4. Differences and Mechanics of Deep Research Models
5. Results and Analysis of Perplexity's Deep Research Queries
Podcast Transcript
Jordan Wilson [00:00:16]:
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 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 gonna do a live run through. We're gonna put in, a couple of prompts for, deep research, the perplexity version, and then kind of see exactly what we get. Alright. I'm excited for today's episode.
Jordan Wilson [00:00:54]:
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 livestream 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. So if that sounds like you, welcome. You're in the right place. Maybe it's your first time listening on the podcast. Thank you. Please make sure to subscribe, follow the show.
Jordan Wilson [00:01:22]:
But after you're done or you can just check-in the show notes right now and go to youreverydayai.com. There, you're gonna wanna sign up first for our free daily newsletter. We're gonna be recapping today's show and we recap the show every single day bringing you additional info. So if this one hits you, then you're gonna wanna make sure to read today's newsletter. But also while you're there, there's more than 450, a free catalog of back episodes, videos, resources, literally everything that you need to become the smartest person in AI at your company. It's all on our website for free, unbiased, giving it to you straight, exactly how it is. So make sure you go check that out. While you're there, also make sure you go check out our twenty twenty five AI road map series, y'all.
Jordan Wilson [00:02:05]:
I don't care. 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 four forty three through four forty seven. They're quick, about twenty five minutes each, so make sure you go check that out. Alright. We're gonna have the AI news in the newsletter. You you know, there's too much to get to in this.
Jordan Wilson [00:02:24]:
I wanna 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 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.
Jordan Wilson [00:02:40]:
I 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. Alright. So if you see this symbol around for our our livestream 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.
Jordan Wilson [00:03:21]:
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 a hundred times a day.
Jordan Wilson [00:03:51]:
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. Alright. So here's here's the gist of 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.
Jordan Wilson [00:04:42]:
That could vary. Right? If this gets very popular, they might make that, you know, two. You 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 perplexity's 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.
Jordan Wilson [00:05:15]:
You know, perplexity has five free a day. So automatically, just for that, just for accessibility, I think a huge plus for perplexity. Alright. So, we're gonna get into a little bit more how this works, but we're just gonna 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 gonna go ahead. I have a couple of pre typed out, prompts that we're gonna put in to deep research. And then we're gonna go backwards from there.
Jordan Wilson [00:05:52]:
So we're gonna give it some time. We're gonna kind of launch all of these and go. So the first one that I'm doing, I'm saying provide an, and and this is the exact same thing I did for o 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 Underarmour. Include key financial metrics, recent earnings commentary, and relevant market news. Cite all sources and highlight any discrepancies in analyst opinions. Alright.
Jordan Wilson [00:06:25]:
So this one is it's pretty it's 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. Alright. So let's go ahead into our second one. And, again, these first two are the same things that we did, for our episode. If you wanna 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.
Jordan Wilson [00:06:54]:
Four fifty four. So if you want to go, listen to that, it was episode four fifty four. Alright. So the second one that we're gonna be doing in perplexity deep research, let me go ahead and share my screen there for our livestream audience. More open ended. Right? Just saying find the latest news and information about deep seek. Alright? There's another one that was, you know, controversial. We'll say that.
Jordan Wilson [00:07:16]:
But, yeah, if you care about deep seek, if you wanna know the truth, go listen to episode four sixty. Alright. So that is our second, deep research query that we put in, and then we're gonna 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. Alright. We'll see.
Jordan Wilson [00:07:45]:
Well, first, we'll even see if we can run three concurrently. You know, Google's Google's version of deep research as an example only allows you to run two. Alright. 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. Alright. 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.
Jordan Wilson [00:08:16]:
Right? Although I did spend better part of a decade partnering with Nike. I don't know all that all that information. DeepSeek, I know a ton because I just did a very in-depth report. I probably spent, I don't know, at least thirty 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.
Jordan Wilson [00:08:47]:
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 ten years. You know, so there's plenty. I've been on other people's podcast a lot.
Jordan Wilson [00:09:11]:
So, you know, there's plenty of information about me, on the Internet. Alright. So we have those three queries. So in about five or ten minutes, we're gonna go back and check on those. Alright. 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.
Jordan Wilson [00:09:32]:
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 copilot, kind of, mentality. Right? When everyone has a copilot, you know, I guess technically Microsoft GitHub Copilot was one of the first copilots to market.
Jordan Wilson [00:10:04]:
And then Microsoft obviously has, the Microsoft three sixty five Copilot, but there's so many other, like, quote, unquote, AI Copilots. And it's almost just become this general term. And it looks like that's what's gonna happen with deep research. Alright? It looks like that is just gonna become a term, and I'm guessing we're gonna 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 OpenAI, they all already could 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 Jenna AI.
Jordan Wilson [00:10:57]:
Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft, and NVIDIA 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 GenAI. 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 youreverydayai.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 GenAI. 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 a short response. Right? So the biggest differences with this deep research, if you haven't used them versus a traditional, right, like ChatGPT search or using Google Gemini, which is now finally better connected to Google.
Jordan Wilson [00:12:08]:
Right? You might be wondering like what's the difference. Right? Those queries that I put in, you could probably run those in ChatGPT search or a normal Google Gemini and get a pretty decent result. Yes. So the difference is, these deep researches, while 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.
Jordan Wilson [00:12:37]:
Well, maybe. Because actually before Google released deep research, about six months before that, There was some reporting, and we even covered it at everyday AI. There is 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 gonna do it. I'm gonna do a, we're gonna do a comparison of all three of these tomorrow, because I was gonna do that for today's show, and then I'm like, oh, man.
Jordan Wilson [00:13:19]:
That's gonna, accidentally turn into, like, one of those hour long shows, and I don't wanna 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 a a half marathon or a 10 k. Alright? So I'm gonna try to keep today's episode short, but, tune in tomorrow if you're interested. I'm gonna try to get a a a rubric of sorts, you know, outside. 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.
Jordan Wilson [00:14:01]:
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 gonna be rolling out, I think, two a month, because their deep research tool is utterly fantastic. Alright. But, hey. Perplexity already, has a a a nice, competitive advantage, but tune in tomorrow.
Jordan Wilson [00:14:44]:
We'll be doing a much deeper dive on them. Alright. 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, or 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, GPT four o latest, the one that just rolled out, like, a couple of days ago versus the GPT four o latest that was out, you know, a month ago or three months ago or, you know, the Gemini twelve o six versus the Gemini one twenty one. Right? It's it 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.
Jordan Wilson [00:15:32]:
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 models just, you know, measure their 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, humanity's last exam, it's pretty difficult. Right? And it's all this newer information that requires a lot of, you you know, reasoning, a lot of, logical thinking, and just a lot of, you 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 version, scored a 21% on humanities last exam, which is the second highest score out there next to OpenAI's, 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.
Jordan Wilson [00:16:42]:
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 probably gonna 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 GPT four o, your Gemini two, your Claude 35 sonnet, etcetera. Then you have your recenter models. You you have these models that think open AI o one zero3 mini o3 mini high. Right? Then you have, you you know, Gemini thinking.
Jordan Wilson [00:17:16]:
And now you have these deep research models, which are essentially fine tuned versions, that usually include a reasoning model as well. So I believe in 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 kinda 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 humanity's 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 they're like, alright. Well, hey.
Jordan Wilson [00:17:54]:
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 gotta start using this. Okay. Well, hey. Is it a transform 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.
Jordan Wilson [00:18:35]:
Alright. So how the heck do you use this thing? Well, it's simple. And then we're gonna 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 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.
Jordan Wilson [00:19:12]:
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.
Jordan Wilson [00:19:45]:
For the most part, it is an answers engine. So think of it more as a direct competitor to something like ChatGPT search, versus, you know, in OpenAI's, you know, GPT four o 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 is really good in making 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.
Jordan Wilson [00:20:33]:
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 wanna use, you know, GPT four o or Claude Sonnet three point five. 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.
Jordan Wilson [00:21:16]:
Right? So one of the things when you're working with a model, whether it's GPT four o, whether it's, you know, Google Gemini, you you know, Pro two, 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 cut off as an example is June 2024 like GPT four o, 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.
Jordan Wilson [00:21:55]:
That doesn't mean a % 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 datasets 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.
Jordan Wilson [00:22:36]:
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 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 kinda how perplexity 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 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 Pro plan.
Jordan Wilson [00:23:23]:
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 Mini from OpenAI or r one from DeepSeek.
Jordan Wilson [00:23:57]:
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? Alright. So but in this instance, when we went in, we went in, we chose deep research. Alright. 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.
Jordan Wilson [00:24:23]:
This is what perplexity says. So it says equipped with search encoding 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 is deep research. It is iterative. Right? So if it starts going down path a to, you know, answer your query, but, hey.
Jordan Wilson [00:25:00]:
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. Alright. So let's go ahead. Let's check-in.
Jordan Wilson [00:25:24]:
Let's look at some of these results. Alright. And then we'll, kind of give you my hot take. Alright. 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.
Jordan Wilson [00:25:48]:
Alright. So I'm gonna zoom out a little bit here. Alright. 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, OpenAI is is a a summarized chain of thought, but they just improved the summarized chain of thought about a week ago. Alright. So you can go through and see how deep research is doing this.
Jordan Wilson [00:26:15]:
So the first thing it says, I need to search for information on Nike's q four twenty twenty four earnings report to gather key financial metrics, recent commentary in 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 first step of its research, it went to let's let's count them. So we got four, eight. It went to 10 different searches. Alright.
Jordan Wilson [00:27:01]:
So some of these searches, interestingly enough, perplexity does have a new I'm just gonna 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. They 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.
Jordan Wilson [00:27:45]:
It went to investors.nike.com. It went to a couple, Yahoo finance articles. It went to, CNBC as well. Alright. 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 q four twenty twenty four results. Alright. 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.
Jordan Wilson [00:28:12]:
So it's saying, now I need to gather similar information for Adidas to compare its financial performance strategies and market challenges. Alright. So then, similarly, it looks like it goes into about let's see about eight or nine, additional searches looking at Adidas. Alright. 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 Armour q four earnings report. Same thing.
Jordan Wilson [00:28:45]:
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 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.
Jordan Wilson [00:29:47]:
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 instructions. 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 in 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.
Jordan Wilson [00:30:22]:
Don't do it. Same thing. Even with a reasoning query, never run it once. If you really wanna, you you know, grow your company, if you really wanna 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.
Jordan Wilson [00:30:53]:
Alright. 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 q four performance and market dynamics. Alright. So first, we have Nikes. We have a couple paragraphs here about Nikes, their financial port, performance, operational challenges, market reaction, and analyst sentiment.
Jordan Wilson [00:31:16]:
Same thing with Adidas, financial highlights, strategic drivers, analysts, and credit outlook. Under Armour, they said it's a lagging contender. Alright. 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 four revenue, full year revenue, gross margin in q four, 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.
Jordan Wilson [00:31:55]:
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, a %.
Jordan Wilson [00:32:31]:
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. 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 wanna share it with.
Jordan Wilson [00:33:08]:
Alright. So let's go ahead. We'll quickly tackle the other ones. Alright. So now this one was find the latest news and information about DeepSeek. So it ended up going to 55 sources. Alright. I'm I'm kind of looking through here, and seeing, kind of where it went and in what order.
Jordan Wilson [00:33:32]:
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 DeepSeek from February 2025. This is great. Love seeing this from perplexity. So then it's searching DeepSeek Feb twenty twenty five, 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.
Jordan Wilson [00:34:00]:
It's talking about the Texas attorney general launching an investigation. Alright. A lot of information on the Texas thing here, which is not terribly important, but, it's important. Alright. It's going through, some Wikipedia sources, a New York Times article, Economic Times, and then it says writing the research report. Alright. So let's see what we came up with here. I'm just gonna go ahead and read probably the first one or I'm just gonna read the first, couple of sentences here.
Jordan Wilson [00:34:31]:
So the rapid ascent of Chinese artificial intelligence startup DeepSeek has reshaped global technology markets, regulatory frameworks, and competitive dynamics in early twenty twenty five. 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 one tenth the training cost. While its open source DeepSeek r one model became the most downloaded free app in the US Apple Store, by Jan in January, the company faces mounting security concerns with nine governments banning its technology on official devices. This analysis examines DeepTeeks, DeepSeek's technical breakthroughs, market disruption, and the complex geopolitical tensions surrounding its rise. Alright. So the 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.
Jordan Wilson [00:35:32]:
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. Alright. So then it goes through looking at the architectural innovations driving cost efficiency, the market disruptions, and competitive response. Alright. Security concerns and regulatory backlash, strategic implications for AI developments, emerging frontiers, multimodal capabilities.
Jordan Wilson [00:36:13]:
Alright. That's good. It's it's talking about, like, Janus, Janus Pro, kind of, DeepSeek's version of DALL E, audio video, audio visual. Alright. So not not bad. Right? Again, not a ton of information. Right? So if you wanted to read something and, you 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.
Jordan Wilson [00:36:48]:
Right. Did it give you a 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. Alright. So overall, again, not bad.
Jordan Wilson [00:37:29]:
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. Alright. Let's try the last one. And this is one of those ones where it's like, I can't a % very quickly, look at accuracy. When I tell it to be creepy on myself, I can't. Alright.
Jordan Wilson [00:37:56]:
So this one I said, tell me everything about Jordan Wilson who does everyday AI from birth until today. Make it creepy in-depth. Alright. I I I all these prompts, FYI, I ran them multiple times. Alright. I ran them before because I also wanna 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 gonna update it.
Jordan Wilson [00:38:23]:
So, you know, 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 wanna see if any of these tools are 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 in 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 gonna 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 gonna be anything I didn't know, but might be something I forgot about. Alright. So let's quickly, check the accuracy here. Alright. So it says, Jordan Wilson, architect of accessible AI and pioneer of everyday generative intelligence. Alright.
Jordan Wilson [00:39:22]:
K. It's talking about a Midwestern youth. Alright. 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.
Jordan Wilson [00:39:38]:
Alright. Got it got in something. I used to AB test my lemonade stand as a kid. Alright. It looks like I talked about that on someone's podcast I was on. Alright. So that's correct. It says alright.
Jordan Wilson [00:39:49]:
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. Alright. So from newsrooms to boardrooms, let's see if we got this correct. Alright.
Jordan Wilson [00:40:06]:
So it's talking about my background in journalism in the early two thousands. That's correct. Strategic pivot to digital enablement. Alright. 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.
Jordan Wilson [00:40:28]:
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. Everything else on here looks pretty good.
Jordan Wilson [00:40:47]:
Looks pretty good. Alright. 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. Alright. It got the time of our livestream, 07:30AM Central Standard Time.
Jordan Wilson [00:41:10]:
Pretty good. It's bringing in some information from some of our, more recent deep research. You know, just kinda meta. Right? It just brought in a, OpenAI's deep research in the deep research from perplexity. Alright. So I'm looking through here. Nothing on this instance. That's really wrong so far.
Jordan Wilson [00:41:34]:
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 scale optimization. Alright. So, we have our we have our first, set of hallucinations. So that made it up.
Jordan Wilson [00:42:04]:
And also interestingly enough, there's no citations in that instance. So it made up a 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 inside it, which was incorrect.
Jordan Wilson [00:42:40]:
Alright. Here we go. More more, more hallucinations. So central to Wilson's consulting philosophy is what he terms the human amplification principle. Alright. Let's see. That's wrong. So it was some other website that pulling in information from other parts of the page, it looks like.
Jordan Wilson [00:43:01]:
So making up some stuff. So then it says media empire and thought leadership, the everyday AI podcast ecosystem. Alright. So those are all correct. Alright. So it says our, daily newsletter achieved 63% open rates through a structured content framework. We have very high open rates. It's not 63%.
Jordan Wilson [00:43:27]:
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.
Jordan Wilson [00:44:02]:
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 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.
Jordan Wilson [00:44:37]:
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 gonna do this. I was gonna do this at first, but I 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.
Jordan Wilson [00:45:11]:
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.
Jordan Wilson [00:45:39]:
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 may be 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.
Jordan Wilson [00:46:21]:
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 it 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.
Jordan Wilson [00:46:57]:
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 gonna 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.
Jordan Wilson [00:47:34]:
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 kinda 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 gonna be using it. I'm not. Right? There's a certain level of truthfulness you have to get.
Jordan Wilson [00:48:14]:
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.
Jordan Wilson [00:48:39]:
That's nothing to do with me. I think it's kinda lame when people criticize or talk trash. Right? That's not about me. Right? These these Reddit threads. Why do some software engineers say LeetCode 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 the 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.
Jordan Wilson [00:49:20]:
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.
Jordan Wilson [00:49:52]:
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, you know, twenty four, forty eight hours since I first ran this same search, but still, even in the the the version I just ran now, a lot of hallucinations, a lot of concerns about accuracy and truthfulness. Alright. So I hope this was helpful. Like I said, we're gonna do the exact same thing tomorrow. We're gonna take a look. We're probably gonna do those same three prompts.
Jordan Wilson [00:50:22]:
It's gonna be a faster one, but we're gonna look at, Google deep research, compare it to OpenAI deep research and perplexity, deep research, and we're gonna 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. Alright? So we're gonna 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.
Jordan Wilson [00:50:55]:
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 spend thirty 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.
Jordan Wilson [00:51:18]:
So you you really have to, be wise and intentional about how and if you even use some of these tools. Alright. So thank you for tuning in. Join us tomorrow. We're gonna do the big breakdown, all three of them. Please also go to your everyday a I Com. Sign up for the free daily newsletter. Thanks for tuning in.
Jordan Wilson [00:51:38]:
We'll see you back tomorrow and every day for more everyday AI. Thanks, y'all.