ChatGPT’s new Deep Research Update: 5 Ways You Can Use it Today

Imagine having complete control over your company's information and the web. Let's be honest, whether you're an AI native worker or not, when it comes to retrieving the correct information, whether it's from your company's files and folders or cloud or the internet, it takes a long time. But what if you had like a Jarvisesque enabled uh tool that could find highv value insights from AI models without all of that nonsensical back and forth or those pesky hallucinations that come with using AI models.

So, while deep research tools have given us glimpses of this type offormational control that you really only see in superhero movies, I think even the most powerful options have left a bit to be desired. Well, that may have changed this past week with Open AI's latest update to its deep research platform. And if I'm being honest, I think a lot of people missed it because of everything that was going on in the AI world. I mean, Microsoft was like, "Ah, we're not going to use OpenAI's models anymore." And OpenAI and Anthropic are kind of fighting in this Open Claw thing. It's been crazy. So, I think the majority of people missed this huge announcement from Open AI because they just kind of rolled it out as a little tweet, but it is a new deep research update and we're going to be going over it today and the five ways that you can use it starting now. So, I hope you're excited. I am too.

This is Everyday AI, but this is our putting AI to work on Wednesdays series. So, for the past year or so, we've been doing this every single week. A practical and actionable hands-on walkthrough for a new AI tool or model. So, let's get into it. And if you are brand new here, well, welcome to Everyday AI. My name is Jordan Wilson and we do this every day, not just Wednesdays, Monday through Friday with the unedited, unscripted daily live stream podcast and a free daily newsletter helping business leaders like you, yeah, sift through all of that information better, save time to grow your companies and your careers. So, if that's what you're trying to do, starts here. But make sure you take it to the next level by going to our website atyou everyday.com. Sign up for the free daily newsletter. We're going to be recapping the highlights from today's show as well as all of the important AI news today that is going to impact your company tomorrow and in the future.

All right, speaking of impacting you and your company now and in the future, if you haven't already, I cannot tell you enough times, make sure you go listen to episode 712 and 713. That is our 2026 AI predictions and roadmap series. Trust me, it is literally thousands of hours of conversations uh over the past year all into two episodes that you can't miss.

All right, but let's talk about what we're here for. The new Chad GBT updated deep research. And you know, apologies to our live stream audience. If you're listening on the podcast, you didn't know this. Sometimes when I say unedited, unscripted, yeah, tried to do this earlier today. Uh audio went haywire. So, sorry about that. This is take two. Uh but regardless, let's get in and learn about the new deep research. So, what we're going to be going over on today's show, the single upgrade, one small little thing that I think turns uh now CHP's deep research from chatbot into a true research platform. Uh why your company data just became deep research's most powerful source. And last but not least, I'm going to be going over five practical use cases that can replace hours of manual research that you can do today.

All right, so here's what's new. Uh, OpenAI announced this last week with a tweet. Nothing else, no live stream, no big hoopla, and I think it's pretty big. So, it is available right now for plus and pro users, and I think it's going to be rolling out here soon to free users on Chad GBT. So, here's the biggest updates. Visually, you'll notice it right away if you're using the new version of Deep Research. And I'll show you how to choose between the two. But reports now appear in a full screen viewer with a split screen citation checking as well as a nice little table of contents on the left side. So, visually it looks better, it's easier to work with, and it's just a better experience reading these research reports. Uh so there's also you can upload files both in the beginning and during the course of a deep research without interrupting it which is huge and then using that as your primary context for research. Uh the other thing kind of related to that is live steering lets you pause or redirect the deep research agent logic in real time without having to start over. And then the one thing, this one little feature that I think is actually turning uh deep research from a nice little, you know, tool to a complete research platform is the ability to choose which websites it does go to, which is big. Uh and then the most obvious update, but probably the biggest one there is the updated model. Uh so now this is running GPT 5.2. So this is OpenAI's uh technically their latest series of models available in chat GPT. Uh there is a GPT53 codeex but that's only available in their codeex uh platform which by the way codeex is absolutely insanely good even for uh you know non-technical non-coding work. Anyways uh this new deep research is powered by their latest model available in chat GBT which is the GPT52 family of models. So, unfortunately, we don't know exactly what flavor or variation that they're actually using for this. We just know it's GBT52. Uh, I assume it's probably GPT52 thinking. I don't know if it's the GPT52 Pro model. Um, hopefully OpenAI will release a little bit more information, but for my use case, my testing, obviously using Chad GPT way too much every single day, my thought is it's a it's an extended thinking version and not the pro version.

Speaking of that limits, who gets it? How much? So, if you are a pro user on that $200 a month plan like I am, you get a 125 full model and then 125 lightweight queries. So, again, uh you know, like the last version of Deep Research was technically powered by a dual model approach, 03 and 04. So, those models aren't really used anymore. Uh so, I believe it was the 03 full and then the O4 mini. Uh so presumably there's two different versions of 52. So once you hit your queries on the full version, then you get the lightweight. So uh yeah, hopefully OpenAI uh releases a little bit more information on that because I think it's super important. Uh if you are on the normal $20 a month or a team's plan, you get 10 full model um runs and then 15 lightweights for every 30 days. So, you know, essentially you get one every work day of the month uh between the heavy and the light. And then free users are expected to get limit limited queries in late February 2026. Uh so I've checked my different free accounts. Don't have access to it yet. Obviously, I have access on my pro account, my plus account, my team's accounts, all my other accounts.

All right, here's why I think it's no longer just a mode and now this is a fully deep research platform. Uh aside from the end goal is much better, the model is exponentially better. All right. And not just that, but the ability to pause, redirect, and interrupt uh this model in the in the middle is such a huge um game-changing option or feature, right? So if you are a power chat GPT user, you'll know that actually OpenAI rolled out this feature I think a couple of months ago to pro users, right? So if you're using GPT52 Pro, which is probably my favorite model, uh I do have to give Gemini uh 3 Deep Think a little more uh a little more time to see if that uh you know can kind of take the crown in at least in my personal usage. Uh right but with GBD52 Pro it can often take 20 30 minutes an hour longer uh right so deep research queries if you haven't used it uh the new model is actually a little slower which I think is not a bad thing uh than the previous deep research but uh deep research query might run anywhere from you know eight minutes to 45 minutes right it really just depends on uh number one what you're asking it uh number two um the data that you're giving it, the complexity of your query, um, you know, any steering that you do in the middle of the query. So, it really depends. And, you know, I'll say this, if you're not running, whether it's on Chad GPT, uh, Google, Gemini, their new deep research powered by Gemini 3 Pro is absolutely bonkers good. Uh, right, Claude's research tool, not deep research, their research tool, Perplexity, whatever. If you're not using a deep research tool daily and connecting your data on the front end, I'm telling you, you are absolutely missing out. It is the best way to consume uh synthesized wellsourced information at scale that is personalized for you, your use case, your business's viewpoint, etc. It is literally sometimes, right, I've worked with consultancies in the past, right? some deep research queries if you give them enough information enough uh context in that context window I mean it is like you hired a consulting company if you do it right right it is an amazing output uh so let's talk a little bit uh about why the 52 model matters well it's enhanced reasoning capabilities for complex multi-source research tasks just smarter research planning and improved synthesis across multiple sources and you can still use the old model if you want to but for the most part there is actually one caveat that I'll share here in a little bit. But for the most part, I do think that users are going to get a much better experience from the new 52 model.

The document viewer is also great. So, I am going to show that, right? It's our putting AI to work at Wednesday. So, I will uh grab the screen here in a little bit and do some live walkthroughs. What could go wrong, right? Aside from uh my audio not working like the first attempt today. Uh but research reports are now open in a dedicated full screen interface. So, it's just a a nicer uh way to consume the information. It's less cluttered. Uh the table of contents on the left is really cool. I like that. It's great to have. And then you also have the dedicated source panel on the right for easier and faster fact checking. And then the great thing is is that real time control. So, you can obviously like when you're using a thinking model, you can kind of monitor the chain of thought, but it's kind of a two-pronged approach. And I can show you. So, it's going to both uh show you what sources it's looking at. It's going to technically there's three different things that you can see by kind of watching this in real time. There's sources it looks at, which is usually hundreds. Uh there's sources that it will use, which is usually dozens. Um and then you can also see kind of the steps or the thinking that the model takes. So, it's great to be able to see that a little more granular control with this new version of deep research versus the last version. Uh, and then being able to adjust uh, deep research in the middle with new sources or follow-up instructions midway like you could with GPT52 Pro. It's just such um, it is it is huge, right? Even if you don't have the 30 minutes to sit around and watch the computer screen, if I'm being honest, schedule some time that you can with a meaningful deep research query, right? Especially if you're, you know, um, you know, on the the normal paid plan where it's somewhat limited. If you do that earlier on in the process, it's going to pay its dividends later. Uh I kid you not, one of the easiest things to do to get better results out of any large language model is to watch the chain of thought. You know, write down notes as it goes along. Um look at the output, compare uh your input, your notes as you go along and the output and then run it again, right? Uh such an easy shortcut to get much better. And I think with the new version of Deep Research, aside from the fact that you can interrupt it midway, always running a second or third time is going to give you better results.

All right, so now let's uh do this live because I do want to uh show everyone here a little bit on uh kind of the new setup, the new layout. So I'm going to share uh my screen here. Live stream audience, thank you for uh letting me know earlier that my that my audio wasn't working. So let's let's try this again. What could go wrong, right? All right. So, let's bring up my uh my chat GPT window. All right, there we go. And uh you know, podcast audience, I'm going to do my best to describe this, but if you want to see the video version, uh you can always do that on our website atyou everyday.com. You can always listen to the podcast there on as well each episode page, listen to the the video version, etc. Okay, so let's go ahead. Uh I'm going to clear my my little computer interface out a little bit here. Okay. So, uh I just started a deep research query, but I'm going to walk you through uh how this new version works. Uh so, right now I am on my looks like I'm on my team's plan because I have this company knowledge button, which is a little different than if you're on a normal plan. It's not super important, but uh so now uh to start a deep research query, uh you'll see kind of what I'm doing here. You're going to look in the uh the prompt bar, click the plus button. Okay. Uh you're going to see the deep research option in the menu that pops up. So, here's the thing. A lot of people are overlooking this. Now, there's a drop-down menu after you select deep research. And from there, uh you can select the version. The new updated version is just called deep research. Uh the older version is called legacy. So, why would you want to use that legacy version, right? It's an old model. It's 03 and 04 mini. Uh right, you want to use 52 for the most part. there's all these new features that you just told me about Jordan, why would you ever use legacy? I'm being honest, there is one little thing I actually like better. Uh, so in the old model, so in legacy model, um, right before it got started, before the deep research got started, it would usually ask you three to five clarifying questions. Okay, which is always nice because what if you, you know, just type something that's nonsensical if you make a mistake, right? You don't want to have to wait in the old version a long time for it to be done. Uh, so I like that it would ask follow-up questions. So in the new version, there is a similar um feature. Essentially, it puts the plan together and you just have to approve it, right? Technically, I like the old version better because those questions that it would ask you really, I think lead to a better first version. So what I would uh recommend um and if you've taken our free prime prompt polish course uh you know which is just updated it's free inside of our uh inner circle community uh you know this concept of context stacking I would context stack first uh before starting a deep research query FYI two to three times better results easily.

All right. So now let's go ahead and uh jump in here. So uh I have a prompt up on my screen, a deep research query, and it's already working. So I'm going to show you what's happening. So I essentially said, um I use Canva every single day for building slides for the everyday AI show. Please do not look at these titles, um as sometimes I do not always update the titles. So essentially in my deep research query, uh you can choose the different apps that it has access to. All right, last time I checked, there's 60some uh different apps that you can connect your data to. So, I use Canva every single day for my, you know, ugly slides that I put up on the screen here on the live stream. So, I have 720ome, uh, Canva decks that have a wealth of information. Uh, right. And then I'm also giving deep research access to my website. So, this new feature, you can click on manage sites. All right. And you can choose a specific site, which is great. Um, and then there's also a new toggle option. So, I have it on my screen here, but it's very easy to see this. Uh, and then it says, "Prioritize these sites, but allow full web search." So, essentially, you can require deep research to go to, you know, your site first or, you know, a series of sites that you trust, your competitor sites, etc. But you don't have to limit it. But you can if you want. All right. So, in this use case, I'm actually going to do that. All right. And then it gave me a kind of a research plan here. Um, so it says I'm going to extract live stream decks from the user's Canva account using the Canva connector. Uh, it's funny that OpenAI is still calling it a connector. Uh, even though it's not what it's called anymore. It's called an app. Uh, anyways, let me get back to my original prompt. Sorry, got sidetracked here. Uh, so I said, "Please look through the last six months of Canva documents that appear to be live stream presentations for the Everyday AI show." This is important. All right? Because I obviously use Canva for dozens of other projects. So, I'm saying ignore those, right? The names are all over the place. So, go in, use your best judgment, smart model with computer vision, and find those that look like that they're live stream documents. And then I'm saying cross reference those Canva decks with the web pages on my website, your everyday.com. Then create an easy to digest report that goes over the 50 most popular trends, categories, stories, news, happenings, events, LLM updates, new AI models, etc. This should be angled as a starting guide for someone who is newer to AI but who wants to double down on their knowledge. Right? So even the last, right? Uh I said six months, it's like 150 episodes. That's a ton. Even myself, I've probably forgot 80% of what was covered here. Uh, so this is hopefully going to be a good guide. But now you see, uh, Deep Research kind of creates this plan. So, it's a couple steps. Uh, it gives me, um, the different steps that it's going to do. It's going to extract information. Then it's going to cross reference it with the website. It's going to survey additional high-quality web sources. It's going to identify and rank the top 50 trends. And then, it's going to draft an easy to to digest starter guide. So, if I want to update that at any time, well, there is an update button, then essentially what's going to happen here. I can click uh to add uh files uh uh or I can just essentially send a follow-up prompt. All right, so I'm not going to do that because we're going to give it some time uh to cook. But now you see how this works in real time. you see some of the new features already and we're going to check in this uh at the end um and kind of see some of the other features that are on the back end once a report has been produced. Uh, but you can see um it's going I can also click um I wish OpenAI made this a little more prominent. Um people don't know but there's essentially this small little gray text at the bottom. So if you click on that, that's how you watch um that's how you watch it work live, right? So now I can see its research activity. I can see the steps that it's going through. This is important because sometimes maybe one of your apps that you have connected, maybe the connection is stale and you need to go reconnect it and you think it's connected but it's not, right? So essenti especially if you're uh using a lot of apps, you always want to keep an eye early on, right? because you don't want to come back 45 minutes later and like oh frick right my oh I changed my password two weeks ago and forgot to update the app and you know uh a lot of times deep research and AI models when that does happen they'll try you know crazy things to try to make up for not having access to certain information that you told it it had access to and it'll try for sometimes way too long. All right uh we'll check back in on that later. So, uh, let's go ahead and get back to learning.

All right. So, like I said, you can connect the apps and target specific websites. And this is huge. All right. So, um, I'm going to give you my use cases here in a minute. Uh, and I've already started to, but this is where I need you to think. What is it you do? Um, I think so many knowledge workers out there, that's probably most of people listening uh to this podcast, if you're sitting in front of a computer all day, you know, you're probably using different apps, different pieces of software, right? As an example, maybe you're using Salesforce as your CRM. Maybe you're using HubSpot for email marketing. Maybe you're using uh, I don't know, ClickUp for project management, right? All of those that I just mentioned, they all have connectors, right? Maybe you use, you know, shareepoint and one drive or, I don't know, Gmail, Google calendar. All of those things have connectors. So, what do we as knowledge workers do? Well, we go visit Salesforce. We go look at Slack. We go look inside HubSpot at our last campaigns, right? We go check the project in ClickUp. we go, you know, log into our um uh you know, our our our one drive, our sharepoint to check these files and folders. All of those things that we do, large language models, especially ones that are extremely powerful like this new deep research from OpenAI, they do it better, they do it faster, they do it at scale. I don't care. Better than me, better than anyone. This is what, right? This is what I've been doing for 20 plus years of my professional career. Use different software. Go find the information, right? Essentially, you are synthesizing, personalizing, and carrying context over from app to app. Maybe you're taking notes. Maybe you're working on a document as you go along. But that's what we do, right? That's all we do. But now, this is what deep research does, right? And it's not just OpenAI's version, right? Uh Andropic's version, very similar, Google's version, very similar. uh perplexities version very similar you know they all have different you know apps or connectors um but one thing to keep in mind which is very different than an agent right uh because technically at least when openi announced this they said it's a deep research agent uh deep research this only has read access okay so it's not going to perform actions for you obviously with their agent mode you can do that if you want to uh and still using a lot of those apps it's much slower uh so deep research is not going to you know delete those files right in your CRM or it's not going to change the status of an important project in your project management tool. It is read only never write actions. That's important. All right.

So, let's go over now now that you know how it works and we'll check in on uh our little project to see if it worked. Um [snorts] now let's go over the use cases. So, I have five that I think are great and I already gave you kind of my uh you know, an example of my use case. But as I go over these five use cases, I want you to think about your work. Where do you spend your time? Even as you are using AI tools, right? Where are those inefficiencies still? I think with deep research, a lot of those are going to go away. I think it's an underused aside from canvas modes uh right uh in open AAI's Chad GBT canvas mode in Google Gemini uh or the artifacts mode in claude right I think canvas modes or artifacts is underused and deep research is underused uh and the biggest thing right is when you select those apps that you want to use or the websites you can select multiple you can select 10 these are the 10 apps that I use every day again assuming you have permission to connect all these apps to your chat GPT Right? Always do that first. But that's what we do all day. We carry information from app to app, website to website, and well, we create something in the end. Uh so this is where it's huge.

So use case number one is a memory powered planning for your next steps. Sometimes I like being very open-ended um with chatgbt in terms of what you're working on. And this becomes especially powerful as you give access to chat GBT to more information about you, about your goals, about your team, what you're working on, your company, etc. So, this is if you have the personalization turned on, the memory turned on, right? Uh this is great and you'd be surprised. So, my example, and I invite you to try something like this, just say based on everything you know about me, including memory, chat history, etc., Please ch out my please plan out my next six months of what I should be focusing on in my case to grow everyday AI. Right? Start open-ended. Don't give it access to everything else. Right? Start open-ended and then you can do a follow-up prompt if you want to based on what it suggests and then give it access to certain information. I think one of the biggest mistakes people make when working with extremely powerful large language models is we think that we know the right answer. I always stay say start wide, work your way to narrow. Okay? uh it's you're going to find out some great insights because one thing that I always say especially if you're a power user this is why I think advertising on the chatbt platform is going to be uh bananas good they find gaps that you don't even know about right if you're asking about a b cde e f right a large language model is going to be able to connect patterns uh across things that you may not even know about yourself personally professionally career-wise your team etc right because it understands the intent of what you're asking over and over it's going to be able spot patterns that you used to ask about but no longer do. Uh, you know, it's going to connect these dots that you may not necessarily even know are there to connect. So start wide.

Use case two essentially rag company search right this is not again this is not as good as a complete fullyfledged vector database but this is huge and I think that you know using the deep research mode is going to give you a much more accurate and better cited report than using a normal thinking model and then using apps that way. So in this case I say restrict deep research to search only your whatever it is you know your Google drive and your company website as an example. Uh, and then the connected document stores uh just become these searchable sources for focused internal research and then you can get a synthesized report built entirely from your organization's own files and your files only. So, my example of this, well, that's what I just showed you, right? Uh, I can't tell you, if I'm being honest, how valuable I think my Canva account is and our website, [snorts] right? There's a lot of inaccurate information out there when it comes to covering AI, right? And that's why I love this new sites feature as well. And you can include sites because I know as an example, you know, there's a lot of sites out there, web publications that you think, oh, by looking at their name, but then there's different versions of those, right? Different countries, uh, different publications under that umbrella. I know at least a dozen that you would think would be very reputable. I know that they just turn out AI slop. It's full of hallucinations. Anytime I'm doing a normal search, I say, "Oh, can't include that." Right? I do hope that OpenAI uh allows you to instead of include websites one by one, I hope it allows you to exclude or blacklist websites one by one. That would make it a lot better. Uh I did suggest that uh you know on Twitter and they actually liked the reply which they normally uh you know don't go through and like replies. So maybe that means it's coming, maybe it means nothing. Uh, but uh that's a great example is you know give it access to all of your company's data only your company's website and that's it go to town. Uh, right this is a very quick um version not as good again as a full retrieval I'm going to generation uh setup right uh but it's 80% of the way there and 1% of the time. Um, the other thing that's great uh or something that's important to understand and know where does data come from? There's three sources. Training data, right? And we don't necessarily have control over that. We can kind of prompt our way around it. So large language models have training data. Then there's number two, the data that we connect, whether that's through, you know, apps, formerly known as connectors, uh it's it's something we upload in a chat window, a project file, etc. And then there's three websites. So training data data we connect and websites. So this is a great way to control the latter two by just restricting them and then you have a version of rag company search. All right.

So, let's go ahead. Uh, let's see how uh ours did. So yeah, unfortunately uh it's still it's still working. All right. I wanted [snorts] to be able to see if we could see some results here. Uh luckily like any good um you know person trying to cook in the kitchen with some AI stuff. I do have a version of this done. So yeah uh the example I gave that was kind of the you know everyday AI retrieval augmented generation right just pay only look at our wealth of Canva and our website. It's still going. Uh right still going. It's been going for uh quite a bit here and I can kind of check on it. Still cooking. Uh, but let's go ahead. Uh, let's look at a finished version here because we do have a finished version, right? I put one cake in before we started. So, this did take 35 minutes. Uh, which is probably one reason why we couldn't get it done in the live stream. Sometimes I've had ones that take, you know, 40 minutes and then the second time it takes 20 minutes. So, I was giving it a try. But anyways, let's look now uh live at some of the new features and options. So, there is this new kind of full screen uh view here. Um, and then on the right upper right hand side of the screen, right? So, hopefully podcast audience simple enough to follow along. So, you uh when the deep research is done, you're going to click on it. It's going to put it into live uh sorry, full screen mode. Uh in the upper right hand corner, you can download it. You can copy the contents. There's this little squiggly line. If you click that, that's how you get your sources. So, these are the sources used. Okay? And then you can click the activity, which is it's kind of summarized chain of thought. This is how it thought about things, went through the sources, and this to me is fascinating. I spend way too much time reading summarized chain of thought more than probably most humans. Um, and then on the left hand side, this is the new table of contents, which is really cool. So, especially if you have longer reports, you can just hover over these uh little toggles here on the screen and then you can scroll down and then if you want to click something, it's like a jump link. So, I can click that and it goes straight there. So, actually, let me see. Let's see how this turned out. Okay, so we have an executive summary. Let's see if it did everything we asked. Okay, so this is the great thing. It's giving sources. So, uh, this is meta. All right, there's something about deep research um that just came up from my website in the deep research report. So, I can click it and then on the right hand side, it has all the sources that are used. So if there's ever anything that you want to verify, you can always click that, look at the sources and verify it. All right? Uh this is not your initial chat GBT hallucination, right? 2020. This is not it. The always site cited and sourced very well. All right. So let's go down. Let's see how it did uh according to the directions I gave it. All right. So scope sources and cross referencing. So it's telling me what it did, how it worked. All right. Uh, it kind of mapped out all the different Canva decks. That's really cool, too. So, I can go check those out if I ever have any questions. Okay, this is nice. It gave me uh a visual synthesis of the sixmonth landscape. I didn't even ask about this, but this is pretty cool. It made me kind of a visual of how things have changed over the last six months based on all of the data, which is very helpful. Um, all right. It gave me a uh kind of what themes were talked about the most. Unfortunately, the x-axis is a little messed up and it's overlapping. Uh, but if I wanted to, I could look at the code and kind of figure out what it's doing. So, you know, I didn't even ask it to create these visuals and it actually did a really good job of making this document more interactive, right? It made some charts. Here's the ranked top 50 items with side ready briefs. Um, [snorts] sorry, slide ready briefs. Okay, this is this is very cool. Uh so found the 50 trends that have you know the 50 biggest trends over the last um six months at least according to things that we've been talking about here on the website. This to me is such a valuable resource, right? Imagine doing this for your company's website, for a competitor's website, for you know, 10 industry websites that you follow all the time, right? Maybe you've been out of the loop, been busy on a couple projects, been on vacation. Uh, right? This is such a good way uh to do some simple sentiment analysis uh and catch up quickly. Uh so it gave me kind of the recurrence signal strength, you know, things that were very high. So AI as an operating system, that's a trend, the number one trend. AI agents becoming the default workflow units. Very high trend. Uh so this is really cool. Uh so this is really good. Okay. And then we have slide ready briefs uh for ranks one through 50. So it gave me a nice write up on all 50 of those. Sheesh. This is good. This is nice. My gosh. Okay. Um I'm gonna read this. Right. As crazy as it sounds, like I told you, I don't remember everything I've talked about over the past six months, you know, sometimes I don't know, I feel humans, maybe it's because of AI or just attention spans in the internet, but sometimes I feel like, you know, we have like a memory of a goldfish. This this report to me is pretty freaking amazing. Um, so yeah, I don't know if if you want access uh to the report, just go ahead repost uh today's show uh on LinkedIn and I'll send you this because uh as I'm scrolling through, this is actually very impressive and a very good resource uh to go through and read. Uh so yeah, if you're listening on the podcast, we always have uh the LinkedIn link that you can just go and then repost this and I'll send it to you. Really cool.

All right, let's wrap up. Let's go over our next use cases. So use case three, a competitor deep dive using your company context. So similarly how I started the first use case by saying hey use everything you know about me. Same thing use everything you know about my company but then also combine your personal context what your role is uh and then your synced company data for competitor analysis. Then you can add your competitors websites uh industry websites etc. And then you can get a report that's mapping competitors against what matters to you and your specific company. Again, this is like as if you were hiring a consultant, but for you, right? Not for your company or for your department. And if you do take the time and even if you go through maybe two or three iterations of providing feedback or uh steering it midway through, I think you will literally be shocked at the amount of information that you get out. Have you guys never done this? Sometimes I feel like I'm a crazy man because I'm like, why is no one talking about this and doing it nonstop? It is so good, right? I can't [snorts] lie. I think a good chunk of whatever uh success we've been able to have as a you know as a top AI podcast and I know that's kind of cringe to say I think a lot of it is from doing things like this things that I'm telling you to right when I'm when I talk about these use cases these aren't just like random things I read about on the internet I'm like oh this is cool go try it these are things that I do all the time and then like when I do them I'm like holy freak that's amazing I need to tell people right so you should be doing this.

Use case Four, industry SWAT built uh from your data and news. So kind of similar to three but more on the industry versus just competitors and just a SWAT, right? Strength, weakness, opportunity, threat report. So this can use your chat history and company data to identify sector trends that are impacting you. Uh deep research can scour uh trending industry news filtered through your business context and then produce a SWAT analysis grounded in both your data and current market signals. Uh the cool thing and I do have to do a little bit more uh research on this uh but I'm pretty sure Deep Research does a great job with Boolean URLs um which is amazing because without being too dorky what you can accomplish uh with some simple URL hacking uh right you can essentially have a dedicated up-to-date research assistant that you don't have to keep feeding uh you know different websites right if you know what you're doing uh around some simple Google search operators uh yeah it's extreme extremely powerful. Uh, last but not least, or at least I do that all the time with uh GBT 52 Pro, and I have tested it a little bit in the new deep research, but I got to do a little more testing to see if it is consistently handling that.

And then last but not least, use case 5, the follow-up assistant that scour your inbox and calendar. Uh, this is huge. I miss so many opportunities, so many emails. I stink at it mainly because I get spammed, right? So my example here, I said, "Please carefully comb through the last 6 months of the connected Gmail inbox and Google calendar for Gmail. Pay specific attention to my outbox as my inbox gets spammed a lot and a good majority of what lands in my inbox is not important. However, if I have replied to something uh via my outbox, that means it is generally important. For my Google calendar, please look to see what which meetings I've had with other people in the last six months and cross reference that with correspondence in my Gmail inbox. The goal is to both follow up on opportunities for everyday AI where I may have dropped the ball or forgot to respond, as well as to re-engage older conversations that may have already closed in theory, but may be worth revisiting. Please keep in mind all the contacts that you know about me, as well as looking at the two attached informationational sheets. And what I've attached here is I essentially have these living breathing markdown files that I always update anytime I'm working in really any large language model. uh both about everyday AI and about my role uh kind of you know my day-to-day uh what it looks like. So I have two different markdown files about everyday AI and then about myself. So that I mean y'all I should actually spend way more time on this because there's no reason for me to suck at email. It's just more or less overwhelming, right? When I run these it's like here's you know 85 extremely important emails that you haven't got to. Right? Unfortunately, they can't send or draft replies yet, but hey, maybe one day. [snorts]

All right, so that is a wrap. So, now you know what is new in OpenAI's new updated deep research and five ways that you can use it today. So, yeah, if you want to go check out that trend report, make sure to go share this and repost this on LinkedIn. And then 712, 713, don't forget those numbers. That is the 2026 AI predictions and roadmap series. If you haven't listened to those, you have to. And then please go to your everyday.com, sign up for the free daily newsletter. Thanks for tuning in for putting AI to work at Wednesday. Hope to see you back tomorrow and every day for more everyday AI. Thanks y'all.