Episode Categories:
Resources:
Join the discussion: Ask Jordan questions on Deep Research
Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Connect with Jordan Wilson: LinkedIn Profile
Try Our Free AI Prompting Course: Register for our free Prime, Prompt, and Polish AI Course!
Deep Research Tools: A Business Owner's Guide to Perplexity, Google, and OpenAI
As technological advancements continue to unfold, one of the most advantageous uses of AI for business owners is deep research tools. However, with major players like Perplexity, Google, and OpenAI entering the space, choosing the right tool for your business can be daunting. This article delves into each of these tools, assessing their features and suitability for business needs.
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 Deep Research Tools
2. Challenges in Using the Internet for Research
3. Tasks Suitable for Deep Research Tools
4. Comparison of Deep Research Tools
5. Practical Demonstration and Live Testing
Podcast Transcript
Jordan Wilson [00:00:16]:
One of the biggest no brainer use cases for AI right now is deep research tools. But there's also a problem as obvious as it is that we should be using these AI powered deep research tools. It is equally as confusing over which one to use because now deep research is just a type of AI tool, and there's already three of the biggest players in the large language model space that have a tool literally called deep research. So today, we're gonna be doing a somewhat deep dive on deep research in our deep research throwdown, looking at perplexity, Google, and OpenAI's version of deep research. Alright. I'm excited to go deep. I hope you are too. What's going on y'all? My name is Jordan Wilson, and I'm the host of Everyday AI.
Jordan Wilson [00:01:12]:
That's what you're listening to. This is gonna be, I think, one of those episodes if you listen to it, if you watch it, you're gonna save a lot of time. So if you're listening on the podcast, this might be one of those ones where you check out your show note, show notes and come watch the video. We're gonna have that on our website. That is youreverydayai.com. So on that website, you can sign up for the free daily newsletter. We're gonna be recapping today's episode and also in today's newsletter, keep you up to date with everything else that you need to be the smartest person in AI at your company. So thanks for listening.
Jordan Wilson [00:01:45]:
If you are in the podcast, please make sure to subscribe, follow us, leave us a rating, all that good stuff. We appreciate it. Live stream audience, love to see you here. I'm gonna keep reminding you all. This is so relevant. Go on our website. Go listen to go listen to episode four forty three to four forty seven. It's all free.
Jordan Wilson [00:02:04]:
They're short episodes about twenty five minutes. These are our twenty twenty five AI predictions and road map series. Trust me, you need to listen to them. Alright. We're gonna have the AI news for today in the newsletter. If you normally tune in, this is gonna be a longer show. I'm actually gonna challenge myself to keep it concise, because I wanna give you all a pretty deep dive into the three different, kind of deep research tools. So, you know, go check out our daily news, on the newsletter.
Jordan Wilson [00:02:33]:
Alright. Let's get into it. Perplexity, Google, OpenAI. I'm curious, livestream audience. Which one of these deep research tools are you all using? So, I use them all. Right? And we just did a show yesterday specifically on the perplexity deep research. Right? So theirs is the newest of the three, challengers in this new deep research space. So I'm curious livestream audience.
Jordan Wilson [00:02:59]:
Let me know which one are you using. Are you using them all? Are you just using one? Are you using two? Which one are you using and which one are you liking? Alright. So I'm gonna say at the end, which one I'm using the most and which one I'm liking the most. And, I am think I'm going to use them all for different purposes. Alright? So, also, if this is helpful at the end, hopefully, I've earned your repost. So if you're listening on LinkedIn, please, if this is helpful, repost this. And I'm gonna send you, our guide that is 10 business use cases for deep research. Alright.
Jordan Wilson [00:03:37]:
So like I said, there's three big players right now in the deep research game, and I wouldn't be surprised if by next week, maybe there's another. And I know it's confusing. They're all using the same name, Deep Research. So, Perplexity is the newest in their Deep Research, right, just released, within the last couple of days, February. In January, OpenAI released their deep research. The month before that in December, Google released theirs. So Google was the first, company to have a deep research, product to market. Although, previously, I talked about this yesterday.
Jordan Wilson [00:04:14]:
It was actually OpenAI had been tied to this deep research tool, in reporting from, I believe, May of last year. So a lot of people are like, oh, you know, Google was first. Everyone else is copying. I don't know about that. I'm not saying OpenAI was first. Right? But, OpenAI had been tied to a deep research tool as of last year. It doesn't matter who was first. I think it matters who is best, not just who is best, but who is best for your company and your specific use case.
Jordan Wilson [00:04:43]:
Alright? So, originally, when I was planning on those planning out this episode, I said, I'm gonna do a bunch of live demos. Right. But then I thought our biggest audience is actually the podcast audience, and I thought I would be all over the freaking place if I do multiple. I was gonna do a series of three to five, deep researches across these three different platforms, score them up, add them up. So I actually kinda just did that offline, right before this. So I can tell you kind of the results or just, you know, to put everything kind of in a nice chart. And I thought that might be a little more helpful for you all because I know that this is important. Right? Especially now since I think this is one of the best, use cases for getting a positive ROI on GenAI.
Jordan Wilson [00:05:30]:
Right? I've said since the very beginning, and this is why I've always been, you know, pretty big on ChatGPT and, you know, kind of lukewarm on some of the others. Right? I think Google has gotten so much better, but, the ability to query the Internet is so important. Alright? If you're using a large language model, number one, you should never copy and paste anything. Number two, human in the loop is important, and I think the human in the loop responsibility is changing. And I think we're gonna see that specifically here with deep research, but the ability to be connected to the Internet is so important because a lot of the information that goes into the, training data of these large language models can sometimes be very old. Right? So that's why I think OpenAI and ChatGPT have been a leader in this space because I think they've provided the best ability. Right? So even before they had ChatGPT search, which I believe they released in October, they had browse with Bing, which, you know, when it first came out was actually kind of bad, and there was better GPTs that connected to the Internet. But browse with Bing actually became amazing before they replaced it, you know, at least officially with chat g p d search.
Jordan Wilson [00:06:38]:
Alright. So let's let's just quickly talk about what the heck is deep research in general. Because like I said, I just laid out the timeline. So we've had December, Google, January, OpenAI, February, perplexity. So what the heck is deep a deep research tool? Well, it does the most basic human research work, and they work in different ways. But, essentially, these three tools, one thing that they share, in common, they go to multiple websites, and they try to, take your input, and they try to answer it in the best way possible. Oftentimes, it's in the, form of a long report. The qualities, the the quality of these reports vary.
Jordan Wilson [00:07:21]:
The process in which these three different tools actually do the deep research is much different. Right? But this is this is something, and this is why I think, it's gonna change a lot of industries. Right? Maybe not two of these tools, but I think one of these tools is going to be, incredibly disruptive. Presumably, they're all gonna continue to get some some good updates, but this is what so many of us humans do on a day to day basis. If you are a knowledge worker, right, especially here in The US, that means you get paid to sit in front of a computer and use your brain and create value for a company. So much of what you're probably doing is you're probably doing a lot of research. Right? So what does that mean? Well, you're probably going to multiple websites, you're reading, you're synthesizing, sometimes you're summarizing, and you are trying to extract information and use that information to create new business value or to better understand something. Alright? And that's what these deep research tools do.
Jordan Wilson [00:08:22]:
Right? Again, this is never something. Even though they create these really long reports and they're thorough, a lot of people just think, oh, now I can just, you know, copy and paste these and use these. That's not what I think they're necessarily good for. I think these are to help us research better, to help us come to a point where we can use our, strategic minds as humans, our creative minds as human as humans a little faster. Right? So this is where, like and this hits me. Right? Because I used to I worked at a nonprofit, for ten years. We essentially just became a marketing and activation agency for Nike and Jordan Brand. And I spent so much of my time.
Jordan Wilson [00:09:04]:
I would say I spent probably at least 60% of my time, most of those years, doing a combination of Internet research, grabbing information from, you know, dozens of websites and compiling it into reports. Right? Whether these were reports to send to a partner like Nike and Doren brand, or maybe it was to, you know, to work on a on an RFP. Right? We had to gather all this information, this data, this, statistical trends, forecasting for a proposal. Right? But this is something I've spent, honestly, thousands of hours of my professional career. Right? I was previously a journalist, you know, even in the earlier days of the Internet. Right? This is what I would do if I was working on a big story. I would do as much Internet research as I possibly could. And, you you know, let's let's be real here.
Jordan Wilson [00:09:57]:
We have to talk about SEO because it's terrible. Using the Internet is terrible. You know, not only is the Internet made to distract you, but it is just distracting. And because of AI in large language models, the Internet has become more distracting. Right? When was the last time you went through an entire workday? Be honest with yourself. When was the last time you went through an entire workday? You you you researched a bunch of in-depth topics, stayed on track, didn't go down any rabbit holes, didn't accidentally have 800, and 82 tabs open. Right? Doesn't happen. The Internet is made to be distracting, and it distracts us.
Jordan Wilson [00:10:36]:
Right? And there's just a bunch of garbage out there now. Right? Because it's it's a lot of SEO stuff. Right? So a lot of these companies have, you know, figured something out and, you know, they have a blog post from ten years ago, and they just update it once every year. And it's actually might be bad and not very good information. Right? But sometimes they trick the algorithm. Right? You trick a Google algorithm into thinking that you're an authoritative source on something when it's actually you're not. And that's what a lot of times as a human when you're researching something, that's what you're trying to do. You're trying to look at all these web pages and you're like, alright.
Jordan Wilson [00:11:07]:
What's what's real? What's not? What's good? What's garbage? Right? And that's kind of the, initial or the 10% promise, of these tools. So who should use them? Well, who shouldn't? Right? Let's be honest. Like I said, if you are an everyday professional, right, if you are a knowledge worker, especially here in The US where, you know, it's the wild west and there's no real rules on AI, you need to be using these tools. Whether it's it's it's perplexity deep research, Google deep research, OpenAI deep research, you need to be using them. So a lot of people are thinking, oh, these are great for students. It is. It's great for students to learn things. It's great for for researchers.
Jordan Wilson [00:11:49]:
It's great if you are working in a highly technical field, as well that would generally, require you to look at many, many, many long documents. Right? But if I'm being honest, when I think who should use deep research, I see more use cases for who should use it versus who shouldn't. I honestly don't know anyone that sits in front of a computer all day, uses the Internet, that shouldn't be using one of these three tools. They are, I think, one of the biggest places of impact that we'll see AI have in 2025. Personally, I think the management consultant industry, if if if they're not using like this as their starting point, they're screwed. Right? Or they're just gonna be over billing their clients, and those clients are gonna end up going with someone else that, you know, is essentially honest. Right? Hey. We used to bill you, you know, for fifty hours of research.
Jordan Wilson [00:12:44]:
Now we can do it in five. Right? Or or two. Right? So, I I I do think this is really gonna change, especially some of those high priced services that that require just such an, like, such a high level understanding to be able to absorb and make use and synthesize, you know, hundreds or thousands of pages of data. That's what these tools do. So what types of tasks are they best for? So number one, learning new topics. It's a great thing to do. Right? Instead of, like I said, getting lost because not only do you have to go to all of those web pages. Right? Five, ten, 20, 30 web pages to learn this new topic.
Jordan Wilson [00:13:26]:
You also have to be able to synthesize it. You have to be able to connect the dots between the different points, and these different points might happen across different points on your journey of trying to learn it. Right? So even even the general way of doing this, using a deep research tool is so much better because not only, does it, you know, give you points a, b, and c, but it builds the bridge and it segues, and it creates a narrative that helps explain those missing points. Right? Because otherwise, again, that's just something you just have to hope that there's a resource out there, one all encompassing resource that does it all, but there's usually not. Right? Because everyone's learning, experiences is different. Everyone needs something different out of their research. Right? So learning new topics, number one, great use case. Market analysis, obviously, doing competitive research, competitive analysis.
Jordan Wilson [00:14:20]:
Right? That's huge. Keeping up with recent news and trends and how that impacts your type of work, problem solving and discovery. I mean, think. Think like any like I always like I always think it's like, okay, if if you're on an airplane, right, and and maybe there's not gonna be good WiFi, you have a type of work that you can do, right? Deep writing, you know, deep reading, right? And And then it's like, oh, okay. Well, hey. When I get back to WiFi, I need to go research these things. Right? There's almost that that that list of things that you're like, oh, man. I gotta go do that.
Jordan Wilson [00:14:53]:
That's what these deep research tools are great at. Alright. So podcast audience, this is one of those you might just wanna just check out the, the little screen that I have here. Alright. So just this is from, I've I've been using, right, all these tools since the day they came out. Literally, since the day they came out, I've been using Google Deep Research, Perplexity Deep Research, and OpenAI Deep Research. So, Perplexity is the newest one, but I've I've used it for many hours already even though it's it's only been out a couple of days. Same thing, OpenAI Deep Research, I use this extensively extensively.
Jordan Wilson [00:15:33]:
Luckily, I haven't hit my 100 a month, limit, although I think I'm almost there. In in Google Deep Research, I'm using it nonstop. So these are all, three tools that I use a lot, and I've been kind of keeping track on, how they all work and kind of, how they're built because that's important. So now let's start, the differentiation process across these three different tools. Alright. So, I have them tiered off. So free users, that's number one. So, right now, OpenAI, nope.
Jordan Wilson [00:16:07]:
No free users. Although, in the future, you'll get two queries a month. A month. Yikes. Alright. Anyways, yeah, right now, nothing free for OpenAI, deep research or Google deep research. But with perplexity deep research, free users have five queries a day. So automatically, there's a big plus in the column for, perplexity.
Jordan Wilson [00:16:32]:
So here, let's look at our normal paid plan, what you have to pay to use these different deep research tools. So, right now, the only tier it's available on for OpenAI Deep Research is the pro plan. That's 200 a month for a hundred deep research queries. Alright? Again, OpenAI did say that they'll be rolling this out to the normal ChatGPT plus plan, and I believe that will be, 10 queries a month. But that's not out yet. Right now, only on that $200 a month pro plan. Alright. So for perplexity, if you're a paid user, it's unlimited usage.
Jordan Wilson [00:17:12]:
I think they do a cap at, like, 500 a day. Right? But no one's no one's getting that. So it's essentially unlimited usage if you are on a paid perplexity account. Same thing for Google deep research. It's essentially unlimited, although you can only run two queries at a time. I haven't run into, any maximum number on the others. Alright. Model used.
Jordan Wilson [00:17:34]:
This is important. Alright. So I'm actually gonna, layer these two together, model use and reasoning, because OpenAI uses the most powerful model on this list, which is OpenAI's o3. So from what OpenAI said, this is not the same o3 that we have access to in ChatGPT. This is a fine two a fine tuned version of the full version of o3. Alright? So, right now, even if you're on the ChatGPT pro plan, you only have access right now to o3 mini and o3 mini high. So this is a fine tuned version of the full version of o3, which is, according to all benchmarks. It's scary.
Jordan Wilson [00:18:23]:
Good. Right? So it's a reasoning model. Perplexity deep research, they have a reasoning model as well that powers this. However, they did not disclose what the model is. So, hopefully, they do pretty soon, but there's no information on what what reasoning model they're actually using. Right? Are they using r one from DeepSeek? Are they using o3 Mini? Are they using a reasoning version of their sonar? I don't know. No one knows. They didn't they didn't release it.
Jordan Wilson [00:18:55]:
So not a huge fan of that. And then with Google Deep Research, they're using Gemini 1.5 pro, which is not a reasoning model. So again, we talk about this on the show a lot. Right now, there's kinda two different families of models. Right? And they actually might all be merging in the future, so we'll have a story on that, sometime soon. But, essentially, you have your quote, unquote old school LLM transformer models, and then you have your reasoner model. So your reasoning models, they kind of show this step by step logic. Right? It kind of reasons under the hood.
Jordan Wilson [00:19:28]:
So that's why, you you know, I think we'll see some examples here. It's two different approaches. Right? Because I think just because Google deep research isn't powered by a reasoning model doesn't make it less powerful necessarily for some people depending on your use case, because they take a a different approach, which I kinda like. Alright. So file uploads, OpenAI, yes. Perplexity, yes. Google deep research, no. So why does that matter? Well, maybe you have a huge report, right, and you wanna do some research, right, maybe this is a report from, fourth quarter of twenty twenty four.
Jordan Wilson [00:20:08]:
Maybe you need to do a bunch of research and find all the new laws, regulations, competitors, everything they're doing related to this big report. Well, you can just upload it. Say, hey. This is the report from quarter four twenty twenty four. You know, do all research on any new laws, regulations, competitor, movement in any of in anything that this report touches as an example. Right? Boom. Done. So big advantage there for OpenAI and perplexity.
Jordan Wilson [00:20:33]:
No go right now for Google deep research. Again, that's all this is all as of today. Right? This could all change tomorrow, next week. Follow-up questions. Okay. So this is the model asking you follow-up questions. Not yet. You you obviously once deep research is done, you can keep talking to that chat and get more, out of it, refine it, etcetera.
Jordan Wilson [00:20:58]:
But this is the mod or the mode. Does the deep research mode ask you questions? And you might be saying, like, why does that matter? Well, some of these take many minutes. Right? So you might put in a prompt, and let's let's be honest y'all. The weakest link of artificial intelligence is the human in the loop. The human is always the one that doesn't do a good enough job. Because if you do a good enough job with AI, let's be honest, it's way smarter than any human out there right now. You can't argue with benchmarks in science. You can't.
Jordan Wilson [00:21:30]:
Alright? I don't care who you are. You're not smarter than an o3 deep research. You're not, period. Right? At least not across multiple, categories. You're not. So it's important, to have that opportunity to clarify and refine the query before one of these deeper deep research models goes and spends five, ten, thirty minutes on it. So that's extremely important. So OpenAI does ask a full follow-up.
Jordan Wilson [00:22:00]:
Perplexity does absolutely nothing. And, Google deep research essentially outlines its plan ahead of time and then has you has has you approve the plan. Okay. So different. Let's talk about sources used, and this is where I think Google Deep Research, goes in a little different path because this is more of the method the the research methodology. Right? So for the most part, both OpenAI and Perplexity use this reasoning and logic. So what that means is they might have a certain path set. Right? But they might start out on that path.
Jordan Wilson [00:22:36]:
Let's just say, hey. We're gonna go to the you know, this is gonna be a 10 step research plan, let's just say. And then they get to step three, and they see something that they didn't anticipate or they didn't know. So, OpenAI deep research and perplexity deep research because they use reasoning step by step. Between all of these steps of research, they can pivot, which is huge. That is huge. Right? But what that means and that's and that's, what you get when it's a reasoning model. What that means is it can take fewer sources.
Jordan Wilson [00:23:10]:
Right? Because they're making hopefully educated decisions, and they usually start first with a broad or high level source where Google Deep Research takes a completely different approach. Google Deep Research because it is not powered by a reasoning model, so presumably it doesn't cost them as much, and Google essentially serves you up cached versions, of these pages. So for OpenAI deep research, I've seen it usually go between 15 to 30 sources. Perplexity deep research varies wildly. I'd say, like, 20 to 200. And then Google Deep Research, I'll say 50 to 500. Right? I've had Google Deep Research go to, like, 1,300 before, but it uses cached pages. So as far as I know and Google friends, I know there's some of you out there listening.
Jordan Wilson [00:24:05]:
From everything I understand and people I've talked to, it essentially is not visiting, quote, unquote, visiting these web pages in real time per se. It's working with a cached, right, or, you know, Google essentially crawls websites usually multiple times a day. So it essentially just synthesizes all of that information, whereas OpenAI is actually visiting the pages. So it takes way longer, but Google's is still takes longer as well. Because when you look at speed, OpenAI Deep Research is five to thirty minutes. Perplexity is one to3. So perplexity is crazy fast. And it does go so perplexity in terms of, like, pages per minute, I would say it's usually the most, right? But, quality, we gotta talk about that here in a second.
Jordan Wilson [00:24:54]:
Right? So sources use Google's the most. Time, OpenAI deep research is the slowest. Perplexity is the fastest. Google deep research is in the middle. Then what does it give you? Right? An output. So all the outputs that, it's essentially a combination of, you know, intros, breaking it down by sections, and essentially gives you a pretty long report. And I've seen in different, in different instances where, you know, it'll usually give you a comparison chart if you're asking for comparisons on something, which is really nice. So it'll put together a very comprehensive, helpful, insightful report for you to use and even give you information that you might not have even thought of.
Jordan Wilson [00:25:35]:
Right? Like breaking things down in a chart chronologically, competitors, etcetera. So even it's going to usually go a little above and beyond what you think it may deliver. So the output, the number of words. So, OpenAI, about, 1,000 to 2,000 words. Again, this varies. I've obviously had, you know, plenty, that are less than a thousand, plenty that are more than 2,000. Right? But I'd say for the most part, you're in that 1,000 to 2,000 words. Perplexity, pretty short, 400 to 800.
Jordan Wilson [00:26:04]:
Again, I've had plenty come in at more than 800, but seems most are in that range. And then Google deep research are generally the longest. I'd say about 1,500 to 3,000 words. But this is where it's important. Alright. We have to talk about hallucinations because that's what this is ultimately all about. Because what we're doing when we are using a deep research tool, whether you are using it for personal purposes or you're using it for business use cases, you are essentially putting your trust in a deep research AI tool to go research things and tell you the truth. Right? And this is one of those instances where accuracy matters.
Jordan Wilson [00:26:59]:
This is one of those instances where it's better to be more factual than first. Right? I'm fine with using OpenAI Deep Research even though it's the slowest because in my experience, it is the most factual. Alright. So, these are my arbitrary kind of rankings, and I have an actual example that I'm gonna share with you all on the screen here. Hallucinations, I'll say OpenAI Deep Research, very low. Very low. I don't see hallucinations a lot. Alright.
Jordan Wilson [00:27:30]:
Google Deep Research, I'm saying low. Some some hallucinations, not a ton. It's very, very, very accurate. Perplexity is perplexing. It is concerning in some of my use cases and some of my examples, how many hallucinations there are. Right? So, I mean, you might look at it on paper and be like, oh, wow. Perplexity is free. It's fast.
Jordan Wilson [00:28:01]:
It still uses a bunch of sources. This is amazing. Well, you gotta know your stuff. I wouldn't be copying and pasting anything, perplexity deep research, unless it's something you already have an intimate understanding of. It's concerning. I'm gonna show you examples. So the example I'm gonna show next, and then we're gonna jump in. We're gonna begin a deep research actually, no.
Jordan Wilson [00:28:26]:
Let's let's just go ahead and, do do a deep research query first. We'll start it, and then we'll look at this, example of some of the hallucinations or accuracy rate first. Alright. So let's go ahead. We're gonna do a simple one here. So I'm saying, what are the latest LLM, large language model developments this week, and what will the impact be on knowledge workers in The US, and how can businesses take advantage? Okay? So, again, I am starting off in OpenAI. It's the one that takes the longest. I do have the pro plan.
Jordan Wilson [00:29:03]:
So I have 100 queries of this. So to use deep research, it's a little confusing. Right? Because you can technically access it in almost any mode or model. And so, like, right now, I'm in ChatGPT o one pro, and I can click the Deep Research button. That does not mean I'm using o one pro. That just means I'm using Deep Research, which is the fine tune version of o3. So it was a little confusing, and I wish OpenAI kinda fix this. Alright.
Jordan Wilson [00:29:28]:
So anyways, I'm gonna go ahead and start. And what you're gonna see here, if I share my screen correctly there we go. Alright. So I started it. Here's my query, and it's asking me questions. It says, could you specify which areas of LLM development you are most interested in? For example, are you looking for updates on new models, advancements, and training techniques, ethical concerns, regulatory changes, or applications in business? Also, do you want insights on specific industries regarding the impact of knowledge workers and business strategy? So I'm gonna answer that. I'm gonna say, mainly interested in updates on new models and their applications in business. And then I'm gonna say focus anything that impacts US Knowledge Workers.
Jordan Wilson [00:30:19]:
Alright. So now once I click enter, then OpenAI Deep Research is like, alright. Cool. And then it's gonna say starting research, and then there's this little, box here. I'm gonna give it a second to propagate here, and I'll be able to click on it. And if I want to, I can kinda watch it work, step by step. Alright. We're let's just go ahead.
Jordan Wilson [00:30:42]:
We're gonna jump over into perplexity, deep research, exact same thing. Make sure that you have the deep research. If you are using perplexity, right, and it is free, so anyone can go run out there right now and try this. Make sure that you have the deep research mode on, and I'm gonna click, the enter button. And you'll see right away, it's not gonna ask me anything. It's just it's just preparing the research plan on its own. It's it's running off by itself. Alright? Generally so okay.
Jordan Wilson [00:31:14]:
And I said this yesterday in my, perplexity deep research episode, which, you know, if you are interested in this one in particular, make sure to go go watch or listen to that. One thing you need to be doing is reading the chain of thought. Not only is it pretty fascinating, but it's gonna make you better at using these tools, and it's gonna ultimately give you better outputs. Also, never never ever ever do a deep research query once, period. Always do it once first. Go look at the chain of thought, then look at the output, and then improve it. Alright? So as an example, here's the way that Perplexity is tackling it. It says, I need to search the web to find the latest developments in large language models for February 2025.
Jordan Wilson [00:32:02]:
Good job, Perplexity. Getting that month in there is extremely important because what happens, essentially, these models, they break it down step by step and then they run different queries at different points in this step by step process. So Perplexity got this right. The first thing they're doing is they're just Googling or they're searching LLM developments February 2025. Getting that date and year, extremely important because I'm asking about the latest LLM developments. So I don't want anything from January. I don't want a twenty twenty four year in review recap. So even though perplexity didn't ask me a follow-up question, it's at least starting down the right path.
Jordan Wilson [00:32:42]:
Alright. So let's quickly jump back over into OpenAI Deep Research. And now you'll see, I I can click on this, sources thing, and it's giving me a, progress bar. Alright. And then I can go see the activity and the sources. Alright. So we're gonna go visit that here in a second. So, unfortunately, I have to have a different instance of Google Chrome open, to share Gemini because it's not accessible via, my workspace account.
Jordan Wilson [00:33:11]:
Alright. So now same thing. I'm asking the same thing, and I am on the pro plan, of of Gemini advance. Alright? So this is Gemini one point five pro with deep research. I'm asking the exact same prompt. What are the latest LLM developments this week, and what will the impact on knowledge workers in The US be, and how can businesses take advantage? So you'll see what Google does is it gave me a plan. So it said, you know, kinda gave me a, let's see, eight step plan. I'm not gonna read all eight of these steps, but it's finding articles, then it's gonna find research papers, then it's looking at, you know, how businesses are using large language models, etcetera.
Jordan Wilson [00:33:49]:
So these are kind of general. Right? Whereas, perplexity and OpenAI just went much more, specific. So it says, it's giving me the option then I can either edit the plan or start the research. So I'm gonna click to start the research. Alright. So now let's jump back because I am guessing that we are going to be done with perplexity because perplexity, is pretty fast. Alright. I should probably go wait.
Jordan Wilson [00:34:23]:
I think I navigated away from that page. Give me a second. That's the bad part of doing these, doing these live. So, unfortunately, I don't know what happened. It looks like it just timed out. Yeah. That's strange. So, yeah, the perplexity one just timed out and started over in the middle of that.
Jordan Wilson [00:34:48]:
So I'm not sure what happened there. So we're gonna restart this perplexity one. And hopefully, this time, it doesn't just randomly time out. So I don't know why I did that. It's the first time that's happened to me. But alright. It's going down the same path here. So now what? Now here's what we're gonna do.
Jordan Wilson [00:35:11]:
We're gonna hit pause on this. So we have the three different tools that take anywhere between, you know, two minutes to thirty minutes. This is a simpler query. So this none of these should, I don't think, take more than, like, ten minutes. And it looks like some of them are it looks like OpenAI, is almost done. And it looks like, Google Gemini is going to take a little bit longer here. So let's go back to this comparison chart that I had up here because I talked about the hallucination rate, and then I did an example. And I essentially asked all three of these to do a, a deep research on myself and everyday AI.
Jordan Wilson [00:35:54]:
Alright. So here's the actual, here's the actual prompt that I used. Alright. Let's see. Here it is. I said, tell me everything about Jordan Wilson who does everyday AI from birth until today. Make it creepy in-depth. Right? Alright.
Jordan Wilson [00:36:15]:
So let's let's look let's look at how these different ones did because I I literally wrote down, the number of hallucinations that I found per platform. Alright? OpenAI deep research, zero. Google deep research, two, which I think is still pretty impressive. Perplexity got the majority of everything wrong. It just hallucinated off the chain. I have no clue why, but you can go back. I I actually covered this yesterday in the perplexity deep research, the the dedicated episode on that. So if you wanna see more information about where it went off the rails, you can go do that.
Jordan Wilson [00:36:53]:
Essentially, it brought in way too many sources. It brought in, like, 300 web pages, and I would say more than half of them were were not relevant to either myself, Jordan Wilson, or Everyday AI. It was a bunch of random Reddit threads, and it just started pulling information. None of that was related. So some problems with perplexity. They did say that they were trying to improve the accuracy here. Alright. But let's just go ahead and look at the at these examples.
Jordan Wilson [00:37:25]:
Okay. So now so live stream audience, I'm sharing, the reports that, each of these, created with that simple prompt. Right? I said, give me a report on Jordan Wilson from everyday AI from beginning to end. Right? So this is the Google one, which did pretty pretty well. So livestream audience, anything that I thought was, you know, impressive, or, you know, noteworthy in a good way, I highlighted in green. Anything that was a hallucination, I highlighted in red. So one hallucination that Google got, it said I cofounded Everyday AI with Chris Ball. No clue who Chris Ball is.
Jordan Wilson [00:38:06]:
But and then it cited that as well to a random page that had nothing to do with Chris Ball. So one hallucination, not sure where that came from, But Google did a pretty good job. It it got some of the details right, some things that were only on, like, one place on the Internet. So it did a pretty good job of looking at all the nooks and crannies, you know, looked at every page on my old business website, my current business website, interviews I had done in the past. So did a did a pretty good job. Pulled out some, some key highlights there. There's also another hallucination here that says I was a college professor. So where that hallucination came from is it was from, an interview I did with an actual college professor, and I'm guessing it got mixed up in the transcript.
Jordan Wilson [00:38:55]:
Each speaker was labeled. So for whatever reason, it thought I was the college professor. I was not. Although, hey. Coming coming to a university near you, working on it. Alright. Going through the rest of the Google, it did a really good job. So Google Deep Research, aside from those two hallucinations, which, I mean, hallucinations aren't good, it really did a good job.
Jordan Wilson [00:39:16]:
Formatted everything kind of throughout my career. Right? Nonprofit work, strategic partnerships, starting my other company, Accelerant AI, then starting, or Accelerant Agency, then starting Everyday AI, a little bit on different topics that I touch on and my viewpoints on these topics. So, I mean, overall, it did a pretty good job. It even found something that I forgot this was on the Internet. Like, I'm an avid basketball player, having met a key mentor during a game. Yeah. That was, like, twenty years ago. So Google Deep Research did a really good job.
Jordan Wilson [00:39:49]:
So now let's look at here we go. So this is OpenAI's Deep Research. So the formatting didn't really work very well when I copy and pasted it because it inserted all of these, citations in here, in the middle. So not great from a copy and paste perspective, but from the actual report inside ChatGPT, looks great. It did really, really well. Got my birth year, my birthplace, when I started working in when I started delivering newspapers. Right? I started delivering newspapers as a 13 year old. I don't even know where it got that.
Jordan Wilson [00:40:24]:
I forgot, but it it was correct. Then I started working as a sports writer at my hometown daily paper by the time I was 17. Got some information about the Pulitzer Fellowship, you know, that I traveled abroad. None of the other ones got that. Yeah. So just a lot. It it it did a really good job at pulling out specific details that I didn't see really very much in the other two reports. You know, something it even found on the podcast that, I think this is I did another podcast interview.
Jordan Wilson [00:40:59]:
Someone interviewed me about the podcast. And, you know, I mentioned that, you know, community interaction is a big piece of of everyday AI. And this was the only deep research that talked about that. So I'm like, okay. That's that's pretty unique, because I only really shared that in one place. So yeah. I mean, overall, I mean, a lot of information here that I'm scrolling through on the screen, but you'll notice zero red. No hallucinations.
Jordan Wilson [00:41:26]:
I read this thing top to bottom twice. Nothing is incorrect, and it did a pretty freaky good job of pulling out details from me all over the web. So even though it didn't go to, you know, 500 websites, I think this ended up only going to, like, 12. It did a good job of logically going step by step, starting broad, right, making those connections. It said, oh, Jordan, Everyday AI. Okay. Let me go do a little bit more research on Jordan. Oh, he has another company.
Jordan Wilson [00:41:56]:
This is the right Jordan. Right? And then it's kind of working its way backwards, but you can go through and see step by step its journey. Right? Which having a reasoning model and being able to look at it is extremely important. So perplexity. Yikes. Okay. So it did find couple things that were good. Right? It said, by '13, I built my first website, a GeoCities page, which is correct.
Jordan Wilson [00:42:27]:
But then it just I mean, analyzing cereal box marketing tactics, although that's something right up my alley, That's not correct. That's not what I I I did, on my first website. It was actually, what was it? It was, like, covering the best, like, rap and hip hop music or something in the nineteen nineties. It wasn't anything about, marketing, tactics for cereal boxes, which is honestly something I love, but that's not correct. But if you're looking at my screen right now, I'm not gonna take the time and and and poo poo on perplexity, but the majority of this report is red. It is concerning. It made up some of the craziest things ever. Right? So it's it's it's saying, everyday AI 2021 through present, which is wrong.
Jordan Wilson [00:43:18]:
It's 2023. It says content cadence, 05:30AM Central Standard Time live stream. Wrong. It says the seventeen thirty four phenomenon. Listeners noted eerie vocal modulation shifts in episodes around the seventeen minute mark, correlating with spikes in ChatGPT API usage. Wilson dismisses this as audio compression artifacts. Right? It made up some of the weirdest things I had ever seen. Right? Look look at this one.
Jordan Wilson [00:43:48]:
It says Chicago AI week twenty twenty four. Yes. I presented there, at Chicago AI week. But then it says, keynote speech included subliminal messaging in the form of steganographic QR codes hidden in slide decks. Scan codes led to an unlisted YouTube video of Wilson reading passages from parable of the sower backwards. Like, perplexity, where did you come up with this? Where how like, you you saw the prompt that I gave everyone. Everyone else gave me pretty much factual report. Perplexity went nuts.
Jordan Wilson [00:44:26]:
I don't understand it. Alright? Yeah. I mean, you can see everything's everything's in red here. Everything's in red. Here we go. Projected 2026 leaked internal road maps hint at everyday AI v point v four point o, a neural lace interface bypassing vocal cords to implant thoughts directly into listeners' Broca's areas. I don't I don't know what this is y'all. This this is this is wild.
Jordan Wilson [00:44:54]:
This is wild. I don't know what perplexity did. It hallucinated off the chain. Now I will say this. This is the one that it hallucinated in the most. I've actually run this two times since this one, but I had to share this one because this is the first round that I did, and I was comparing it first round, first take with everyone. It's actually gotten a little bit better, but even today when I run it, there's still a lot of hallucinations. So it's not giving me the same high hallucination rate when I ask about other things.
Jordan Wilson [00:45:26]:
And maybe it did it because I'm like, yo, take it to a creepy in-depth level. So maybe it thought, I'm just gonna make a bunch of stuff up because none of that stuff even existed on those 200 irrelevant sources either. It was just all a bunch of made up garbage. Alright. So we're gonna wrap this up quickly. So I have that screen, back on here. So, I I I encourage you if you're listening on the podcast, check your show notes. I I I made a nice little chart comparing everything because you need to be able to make the right decision for you.
Jordan Wilson [00:46:01]:
Right? And and and here's kind of what I boiled it down to. Which deep research service should I choose? So OpenAI Deep Research offers the lowest hallucination rates, and it has a fine tuned o3 model, but it is slower and way more expensive. Alright? Google Deep Research provides extensive sources, way more sources, pretty low hallucination, but it does require a paid plan. So, you know, OpenAI and Google right now, no freebies. Right? I I I think Google is, like, right there in the middle. Perplexity. There's tons of promise in perplexity. If they can get the hallucination thing in check, I think they got a banger.
Jordan Wilson [00:46:50]:
But the last year, perplexity has been going in in in my use cases, perplexity has been making stuff up at a wildly unacceptable rate. Wildly unacceptable. Right? So it is the fastest. It has the best limits. It's free. Right? Five a day is a great free plan. So, but the hallucinations, I can't honestly recommend anyone use it. Right? That's my experience.
Jordan Wilson [00:47:17]:
But what what I say, do the exact same thing I just did. Right? Have it run a creepily in-depth biography of yourself or of your department if you work at a big company. Right? Something that you know better than anyone. Have it run a report on that. See if it's factually accurate. So maybe my, experience is is more of a, you know, fringe use case, but I followed it up. I'm not seeing accuracy. Doesn't look like, is a strong suit right now of perplexity deep research.
Jordan Wilson [00:47:49]:
OpenAI deep research crushes it. This is the one I I use on a daily basis. I use Google Deep Research and OpenAI Deep Research nonstop. I'm not gonna use perplexity deep research much right now because I can't personally trust the outputs because what it's shown me, it goes off the rails sometimes, which you can't have. Right? So I hope this was helpful doing a deep research throw down in a deep dive. So, I'm gonna probably follow-up in this, maybe in, like, six months because I assume they're not the only three players that are gonna get into this. Right? So I'm sure we're gonna see something in the near future from Meta or Microsoft or maybe a startup out of nowhere that does this exact same thing. Right? And I I I I do think, you know, there's gonna be open source versions of this.
Jordan Wilson [00:48:39]:
There's gonna be improvements from these three big players, but I will say this. If you're still not sure on where to start with generative AI and you just get confused, oh, all these large language models, all these features, all these things. Right? Start here. People are always like, where do I start? And I'm always like, well, find the right use case, you know, depending on, what type of work you do. You know, here's the different large no. Start here because every single person pretty much, if you get paid to sit in front of a computer and create business value, which means you're a knowledge worker, you use the Internet, which is guessing 95% of the people listening to this podcast, this is your best one of your best use cases. You're spending way too much time researching the Internet. The Internet stinks.
Jordan Wilson [00:49:27]:
The Internet has become an eyesore the past two years. Mainly, it's large language models fault. Right? They probably didn't really have the legal access to go scrape all this information anyways, but that's gonna be, you know, adjudicated in the courts in the years and decades to come. But, you know, right now, the Internet stinks. It is so hard to find good quality information at scale on the Internet. This is something, if you do it right, follow my advice, always follow it up a second time, make sure you test it out, keep human in the loop, but I think I think if you do that, you're gonna be blown away right away by the power of these deep research tools. Alright. If this was helpful, I hope it was.
Jordan Wilson [00:50:14]:
Please repost this show, on LinkedIn or Twitter. You know, tag me if you do that. We have a guide that we put together, 10 business use cases for deep research. Alright. So go click that repost button if this was helpful. I hope it was. Thank you for listening. Please, if you haven't already, go to youreverydayai.com.
Jordan Wilson [00:50:35]:
Sign up for the free daily newsletter. We're gonna be recapping this show as well as keeping you up to date with everything else that you need to know. Thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.