EP 454: OpenAI’s Deep Research - How it works and what to use it for

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Wait, there's another Deep Research in town? Yeah, you didn't misread that the other day when OpenAI just announced their version of Deep Research, which is very different than maybe the Deep Research that you've been using from Google. So today we're going to explain to you what OpenAI's new deep research is, how it works, and what you should use it for. All right, I hope you're excited for today's show. I am too, because this is a pretty big agentic style step from OpenAI, and it's already changing the way that I use the Internet, just like Google's Deep Research did when it came out. All right, I'm excited to talk. Hope you are, too. And if you're new here, what's going on, y'? All? My name is Jordan Wilson and this is Everyday AI. This is your daily live stream, podcast and free daily newsletter, helping us all not just understand what's going on with AI and all these AI developments, but how we can all actually use them to grow our companies and our careers. Yeah, because if you thought you were using Deep Research and on the cutting edge. Well, not if you're still using Google's version, although I think it's very good and very different. So we're going to get to that in a couple of minutes. So if you are new here, thank you for tuning in. Make sure if you're on the podcast, check out your show notes. We always leave some very helpful notes in there, as well as a link to our website. That's going to be your best friend. We want you to be the smartest person in AI at your company or in your department. And our website is how you do that. Your everydayai.com there you can sign up for our free daily newsletter where we recap the podcast episode that we do each and every day, as well as keep you up to date with all of the other latest AI news and what it means for you. And we have like 430 some podcast episodes, all sorted by category, all for free on our website. All right, so make sure you go check that out and while you're there, make sure you check out our 2025 AI predictions and roadmap series. Yeah, just because it's February doesn't make that any less valuable. I'm going to keep talking about it at least for another week or so, because you need to go check it out. A lot of these things that I, you know, we released like two or three weeks ago have already started to come true. So you need to go pay attention to that. All right, so I am very excited today to talk about OpenAI's deep research live stream. Audience, thank you for tuning in. You know, let me know if you want to see an OpenAI versus Google deep research show in the future. I'll put that together. But let's first start as we normally do, by going over the AI news. So Meta may not use some of their most powerful AI models, according to a new report. If it's too dangerous. So Meta CEO Mark Zuckerberg has announced plans to eventually make AGI or artificial general intelligence openly available. However, the company has outlined scenarios where it might not release certain AI systems due to potential risks, such as so according to new reports, Meta's new policy document, the Frontier AI Framework, identifies quote unquote, high risk and critical risk AI systems. These are systems capable of aiding in cybersecurity, chemical and biological attacks. The distinction between them lies in the severity and manageability of the potential outcomes. High risk systems could facilitate attacks, but are not as reliable as critical risk systems, which could lead to catastrophic results that cannot be mitigated. Yeah, that's not scary stuff. So yeah, if a system is deemed high risk, Meta will reportedly limit internal access and delay release until those risks are mitigated. So at least that's some good news there. All right, some not so good news. Salesforce is cutting jobs while doubling down on AI. Speaking of trends that I talked about, I said that's going to be a big trend is you're just going to see a lot of companies not hiring in 2025 or only hiring for AI roles. So Salesforce, no different, just announced that they are initiating job cuts that affect more than 1,000 roles, as reported by sources familiar with this with the situation, even as it continues to hire for new AI products. So the company is focusing on hiring SalesPeople for its AI agent products while maintaining a focus on profit margins due to pressure from activist investors. So displaced employees will have the opportunity to apply for other internal roles, according to the source. All right, and then last but not least, OpenAI making some more big partnerships in Asia. So OpenAI is making significant moves in Asia now, partnering with South Korea's Keiko and also, as we talked about in our newsletter yesterday, Japan's SoftBank to expand its AI services in the region. So the newest collaboration with Keiko, hopefully that's how it's pronounced K A K A O. So live stream audience, let me know if I'm getting that one wrong, if you know. But it will include developing a Korean language assistant called kanana in integrating OpenAI technology into Keiko Talk, one of the region's most popular messaging apps. So Keiko will also use ChatGPT Enterprise internally enhancing its operational capabilities. So OpenAI's expansion here is partly driven by competition from Chinese AI firm Deepseek, which has gained traction in English language generative AI. So these new partnerships will help OpenAI train its model on Asian language content, brought its linguistic capabilities and market reach. So this comes just hours after OpenAI announced a huge new venture with SoftBank and they established SB OpenAI Japan. So a brand new company or a new joint venture to market an enterprise AI solution called Crystal Intelligence in Japan. That's crystal with an I, not a Y. So according to sources there, SoftBank will invest $3 billion annually to integrate Crystal Intelligence and open AI's Chat GPT enterprise across its group companies. All right, so yeah, a lot going on as always with AI news. All right, so let's get into it. Love, love to, love to see all the, the live stream people. So thank you for tuning in as always. Yeah, if you, if you're a regular listener of the podcast, maybe you want to ask questions when we have guests on, come join the live stream 7:30am Central Standard Time. We do it on LinkedIn, Twitter, everywhere else. So thank you everyone for joining us. Michael and Big Bogey, Douglas, Marie, Zofia, everyone else you know, thanks for joining. Jackie, Fred, my Chicago peeps, good to see y'. All. So let's get straight into it and talk about OpenAI's deep research, what it is, how it works and what it can be used for. All right, so here's first things first. Probably not going to have access to it, at least right now. So right now it is only available to chat GPT Pro subscribers. So that is the 200amonth Pro plan. All right, but it will be rolling out, quote unquote soon, according to Sam altman, to chat GPT plus users. So if you have that 20amonth subscription, you will get 10 searches a month. All right, if you have the $200 a month pro plan right now, you get 100 searches a month and even free users will get a few searches a month. So I'm guessing free users are going to get maybe like two. And then we have the ChatGPT plus users with 10amonth and then ChatGPT Pro, but right now you only have access to it with ChatGPT Pro. So if you do want fii, y' all know I do have the Pro account, so. So if you have questions you want me to run, go ahead and leave them now. And, you know, maybe I'll. I'll leave some of those results in, in the newsletter. So access is the biggest thing. So very few people are going to have access to this now, but I would, I would guess by Sometime in March, OpenAI might start rolling out access to everyone else. All right, so we're going to do this one a little different and look at it live right away. All right, so live stream audience, do me a favor. I have a kind of different setup here, so hopefully everyone can see my screen here as I share it. So, live stream audience, if you could let me know if you can see my screen, that would be great. And we're going to do just two quick examples live, right? Nothing like doing generative AI demos on a live stream, right? Like, what could go wrong? Well, everything. So hopefully y' all kids can see my screen here, but I'm going to go ahead and, and kind of even walk or talk the, the podcast audience through what we have going on here. So if you are on the Pro plan or once it does roll out to plus subscribers, you will see a new button here called Deep Research. All right, so it's a little different. It's not a mode that you would select in the dropdown menu, right? Which now it's like naming Alphabet soup, right? Like, oh, there's oh, three mini and oh, three mini high and oh, one Pro, right. If you. The Pro account. All right, thanks. Thanks, Michael and, and Marie and Sandra for letting me know you can see the screen. All right, so now when you log into Chat gbt, once you do have access to this, you will see a new Deep Research button. All right? And it is very much different than the Deep Research from Google. I'm going to show you how, but we're going to do just two, two of these live right now, so you can kind of see how it works. And most of these take between 5 and 30 minutes. That's why I'm going to start Deep Research queries at the beginning of the show, and then at the very end, we're going to check in on them and see how they did. All right, so the first one, I'm saying, provide an analysis of Nike's latest 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 discrepancies in analyst opinions. All right, so this is something think compatible. I mean there's so many different use cases. All right, so actually if you share this Show, I have 10 use cases, business use cases I already built at the end. So if you share that, I'll send it. Share it with you. All right, so I'm going to go ahead now and click the Deep Research button. Okay, so you have to have that toggled on and then I'm going to go ahead and click the send button. So what's going to happen first is Deep Research reads this and then it's going to ask me some questions. So it's just like Google's Deep Research does. It wants to clarify because before it starts off on a five to 30 minute journey, you really want to make sure it has it right. So it's saying to provide a detailed and accurate analysis. Could you specify which quarter you're referring to? All right, so I'm going to say, I'm just going to say Q4 of 2024, actually, I'm not even sure if all Q4 earnings are out for these companies. So I'm actually going to say Q4 3 of 2024 just to make sure because yeah, I think quarterly earnings, at least for a lot of the tech companies have happened this week. So I want to make sure. I don't know if Nike and Under Armour and Adidas. So I'm just going to say quarter three of 2024. So I know that all that information should be there and it should be publicly available. So for publicly traded companies, you know, you can always go and look at their, what is it, 10ks or whatever to see all their financials. All right, so that's all it's asking me. Some other, some other questions. Do you have any preferred sources? So I'm just going to say, I'm going to say preferred sources. I'm going to say reputable sources. Right. I would normally go through and leave a little bit better feedback on this. Right. And also huge tip, I would first go through and give it access to more information that's relevant to the reason that I'm using Deep Research. Right. So if you've taken our prime prompt polish course, going through the refineq steps, I would generally go through that. But we're doing a live demo here, so I'm going to go ahead, just answer those two questions and go ahead and click the send button. All right, so I'm going to wait and Just make sure. So, so now Deep Research is responding and it says got it. It's going super slow today, so that's always great for live demos. It's essentially saying got it. I will provide the findings once the research is complete. So what I've seen here is it kind of gets sometimes hung up on that before it gives you this new prompt where it says like starting research and then you can kind of see a progress bar. So if you don't see that right away, don't worry about it. It normally pops up. So even right now it's just saying starting research and it's not actually researching just quite yet. All right, so now like I said, I haven't tested how many concurrent Deep Research chats we can do. So I'm going to go ahead, pop open another one and for this one, hopefully live stream audience, let me know if you can see the new tab. So I'm keeping this one very open ended and I'm doing this for a reason. And then at the end I'm going to walk you through these two Deep Research queries. So this one I'm just saying find the latest news and information about Deep seq. All right. Yeah. Something that the whole world is talking about. So some qualifying questions saying are you looking for information on Deep Seq AI, the language model or something else related to Deep Seq or also do you need technical details, business updates or general news? So I'm going to say deep seek AI the LL M company and I'm going to say, you know, V3 and R1. Those are the things that I want to know about. And now it's asking for like the news and I'm going to say just, I'm going to say just give me everything. Right. All right, so now I'm going to go ahead, click enter on those. So we should have, let me just go over and check. Yep, there we go. So we have our Nike's market dynamics already working and I'm going to go ahead and explain here in a second kind of this activity and sources and then I'm guessing our Deep SEQ researcher here. Right. That's so meta to say deep Deep Seq researcher within Deep Research mode in Open AI. Not to be confused with Google Seek Research Research. All right. All right, so it looks like hopefully they'll, the second one will kick off here. It's being a little slow this morning, but that's okay. All right, so let's get back into the details to understand it. All right, so we already kicked off the AI news for today. We already got two of our kind of deep research prompts running now while we give it time because like I said, it's going to take anywhere from five to 30 minutes. All right, so let's go over how to use it. So I just showed that to you. So number one, you know, maybe you just joined in the middle of this live. Well, you have to have that 200amonth right now chat GPT Pro plan and then make sure you always click the deep research button. Okay? It is not a drop down. You have to click that button. So here's how OpenAI describes it and this is very important because it is agentic and we're going to show you how it's actually agentic and what that kind of means. But how open AI describes it is an agent that uses reasoning to synthesize large amounts of online information and complete multi step research tasks for you available to pro users today plus in teams. Next good questions here coming in. So if you do have questions, please go ahead and get them in. So a couple questions already coming in. Let's see, Michael says can you upload files right now? You cannot. Another good question here from Marie. Is there a token limit with deep research? Yes, I believe the context window is I believe 200,000. But I will say this, I don't know if that will be the same. 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 gen AI. 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 Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website will help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gen. For ChatGPT plus users or if that 200k is more aligned to pro users. So just keep that in mind. All right? Also let's first quickly talk about the difference, right? So not just the difference between Google deep research and OpenAI's deep research, which I can't even really get into, but I'll say this. Google Deep Research doesn't really have agentic or reasoning capabilities. It essentially uses Google caches of web pages, right? And it goes through a ton. Google usually can go through 20, 50, 500, more than 1,000 cached pages. So as far as I know, it doesn't actually visit those pages, but Google has a very updated cache version of all of those pages. And essentially it uses Google Deep Research uses a transformer model and summarizes all of the information on all of those pages. Whereas OpenAI's deep research is very different, it is agentic. So I will say that Google's Deep Research is omnidirectional, right? So after you do confirm, you have to do the same thing in Google Deep Research. And I did a whole episode on that, so you can go back and listen to that. But it's omnidirectional, it's not going to change directions based on the findings. OpenAI's deep research is a little different, it is agentic, right? So if it finds something in the beginning of its research that changes the direction or the path, it is going to shift and pivot and go and look into those things a little bit more. So and it is using a reasoning model. So Google uses a transformer non reasoner model, whereas Deep Research uses a fine tuned version of an unreleased model in O3. So we do have O3 mini and O3 mini high already released even for free users inside of chat gbt. But according to OpenAI, this new deep Research is using a fine tuned version of the yet to be released O3 full model. All right? So that's important to keep in mind. And that's one of the things that differentiates it from Google Deep Research, right? So multi directional and agentic versus omnidirectional and more transformer model based. Then we have to talk about the difference between this and OpenAI's other recent kind of agentic or task based features. Because these are different. Because just in the last couple of weeks now we've gotten three pretty big steps, you could say two of these are agentic. So we got Chat GBT tasks. So with this you can schedule chatgpt to do something for you. And one of the biggest things, I know this is confusing, one of the biggest things people are using ChatGPT tasks for is to research, right? So you Can't. At least right now you can't schedule Deep Research and you can't schedule Operator, right? So essentially you can schedule prompts to be run at any time, but you can do really anything within the GPT4O set of capabilities in Tasks. Operator is a little different. Operator operates technically outside of Chat GPT, all right. And that also right now is only available to Chat GPT Pro, where Task is available to Chat GPT plus as well. So Operator, it is literally a virtual agent that operates a virtual desktop. Right? Very cool. You can very much control it. Whereas Deep Research, the newest release from OpenAI, you don't have that kind of granular control. However, it does work, quote unquote, inside Chat gbt and it is going to go off on its own. So in theory, you could accomplish the same things in Deep Research in Operator, but there's more human in the loop if you wanted to do that in Operator, if that makes sense. Because Deep Research, it's working right now, right? I could do a lot of those things in Operator, but like, why would you want to? Right? Because if, if you wanted to do a certain predetermined stack step of like agentic work, you might as well just be doing it as the human. Right? But Operator, I think is a very already, I think it's already being slept on and it's amazing. All right, so hopefully that makes sense. So we do have three very different new offerings and features from Chat GPT. So, so Ken is asking, is it better than Google Deep Research or not? Well, it's different. It's, it's different and it depends on what you want it to, to do. Right. So one thing we'll see as we look at the results, ChatGPT, their version of Deep Research does not necessarily go to the same. It's, it's not a quality quantity play. I think Google does a quantity play and by in doing so, they hope the quality rises to the top. And I love Google Deep Research, don't get me wrong, I called it one of the best tools of, of 2024. I mean, Google has come ro hiring back the last two months actually should have a very special guest from Google this week for you. All that I think you're going to want to pay attention to. So it's different, Ken. It's, it's, it's very much an agentic tool, an agentic deep researcher versus something that just goes to potentially hundreds of websites and, and both of them work kind of the same way. Another important thing to keep for people to Keep in mind, because everyone's like, oh, OpenAI just copied Google. Well, not really. Right. The term deep research was actually pegged to reporting on OpenAI, back to, I believe, June of last year, before Google's deep research. Right. So, you know, it was reported on more than eight months ago, kind of this deep research feature within OpenAI. So everyone's like, oh, OpenAI just copied Google. I mean, I don't know, you know, but it is very similar. But it's also very incompletely different. But, yeah, maybe if there's an appetite, y', all, if you want to see a dedicated Google. Deep Research vs OpenAI Deep Research, let me know. All right, so like I said, I call OpenAI's deep research more of an agentic detective, and it makes iterations in between research runs like a human would. And OpenAI did distinctly say this is an important step toward their goal of achieving AGI, or artificial general intelligence. Or essentially when one AI system is way smarter than any human on every single meaningful knowledge, work, task. All right, so I actually put this little tweet out there, a quote from when OpenAI had their announcement video, which was Sunday night. So from Mark Chen, who leads research, he said, we think it's important to. Important for our models to start doing autonomous tasks for much longer in an unsupervised way. Our ultimate aspiration is a model that can uncover new knowledge for itself. All right, so it's a deep research on the surface, but OpenAI is literally saying this is the first step toward AI that can go work unsupervised. So without human supervision. Right. And go learn new or uncover new knowledge for itself, not for us. Right. I don't know why no one was talking about that. Like, I heard that. I was actually driving and listening to the live stream in my car. I heard that. I was like, wait, what? I was like, wtf did he just say that? And I went back and listened to it and I'm like, yeah, he just said, our ultimate aspiration is a model that can uncover new knowledge for itself. So you have to think right now, right? We are kind of training and using this, and OpenAI is presumably going to be using all of this training data to make an operator model that could just make itself better. Right. We always talk about the steps needed to go from AGI to the big scary asi, artificial super intelligence. And that's essentially when AI can make itself better. Well, opening. I kind of just said the quiet part out loud is like, hey, we're hoping our future models are Just going to go work on their own and discover new knowledge for itself, not for the human user. All right, so yeah, had to point that one out. All right, so what am I talking about? A digital detective in this omnidirectional. And how the heck is this thing agentic? Right? Because if you've used Google Deep Research, I mean, it's amazing, but I wouldn't think anyone's calling it agentic, right? It's just looking through a cache of dozens or hundreds or more than a thousands of web pages and summarizing the most important information to resolve your query. OpenAI's operator, much different. And we're going to look at this. So when you use it, there's an activity tab in a source tab, so you can see as it searches for things and if it finds something. And according to OpenAI, it looks like their version of Deep Research might actually visit the page and not work on a cached version. Which would make sense because although I'm sure that OpenAI is working on that to improve speed and to cut down inference and compute costs on this new model. But you can literally see and go through and look at the step by step activity. So it's kind of like how right now, if you're using O3 many or O1, you can kind of see a summarizing of the chain of thought, right? You don't get the raw chain of thought, which I know a lot of people are complaining about. I don't think it's necessarily a big deal. But you can see a summarized version of what this new OpenAI's deep research is doing. And you can see when it goes in another direction, because it might be reading a story, it might find something that maybe goes against something the user said, or if you're talking about something that's very recent, it might find brand new news, right? It might find brand new news that I didn't know about when I asked my question. Right? So that's the, the beauty of this, right? It doesn't work at the quantity, but I think from a quality perspective, it is right up there, maybe even higher than Google's Deep Research. All right, let's go over all the fine print here. So it is an autonomous research agent and here's the functionality. So it can independently navigate the web to gather information from multiple sources. It uses internal simulated Reasoning through the O3 model, essentially the built in chain of thought process to plan and execute multi step research tasks. So it can integrate with external tools. Right? That's the other thing Right now. So including code execution like Python and processing of multimodal inputs, although that part is not yet released, but it will be released soon, produces it then can produce structure output. So yes, you can also direct deep research. What you want, do you want a table? Do you want a long, long verbiose blog post? Do you want something that's more bullet points? Right. Do you want something in a fun tone, in a serious tone? Right. And also use the context window of that operator chat to steer it in any direction before or after. Right? So that's the thing. Once it's done, you can continue to work in that window. That's another important thing to keep in mind. So let's talk about the actual model that's running this thing. What's the engine? Well, it's using a fine tuned version of the O3 Reasoner model. This is an unreleased version and also the benchmarks on this thing are nutty, right? And you might be wondering, well, how. Well, number one, it's a model that we don't have access to anywhere else. The O3 model. This is again according to OpenAI, a fine tuned version of the full unreleased O3 model. Whereas right now, you know, if, whether you are a free, even free users have a little bit of O3 mini and then you know, paid users have O3 mini high. Yes, the naming is bad, the company knows the naming is bad, don't worry. But the benchmarks are outstanding, right? So there's a new kind of, I'll call it a trendy benchmark, I guess that was just created, I believe a couple of weeks ago called Humanities Last Exam, right. So one thing is with all of these models, there's arguments now that they're being overfitted or over trained to just perform on benchmarks. Right? Because obviously these benchmarks make their way into the actual training data, right? Eventually because they get talked about on the Internet and then the large language models gobble them up. But so this new Humanities Last examination, it is a much more difficult kind of benchmark and it blew everyone else away by getting a 26% on that exam. And you'll see even the GPT4O model, which I think is still probably because of all of the tools it has access to, it is the most powerful model in the world, I'll say that, because even the O models right now, you don't have access to every all these other tools, right? You need tool used to have a model. So even the GPT4O model on this humanities last exam got a 3.3%. Not that great right there you have Grok actually did a little better with a 3.8 Claude. Everyone loves Claude. I'm not a big Claude fan. It got a 4.3. OpenAI's 01 got a 9.1, then you had the O3 mini high got a 13 and then this new deep research got a 26, 26.6. It is more than twice as good as the next best model which is OpenAI. And it is almost three times as good as the next best competitor not named OpenAI which was deep seeks R1. And then Gemini's thinking got a 6.2. So exponentially better. However, I am curious if OpenAI just used deep Research or if Deep Research had access to other tools. Like as an example, did Deep Research have access to operator during this or did it just use Deep Research? I'm not, I'm not sure. However, that shows the power of an agentic tool that can browse the web but also can use a reasoning model at the same time. It's fantastic, right? And we saw reports that OpenAI actually hired countless PhD students to specifically train the O3 model. So that could be another reason as well why the full O3 model did very well. But you combine it with the ability to go research in an agentic way, it's powerful. All right, so right now, like I said, it does support a large context window, up to 200,000 tokens. I don't know once this gets rolled out to the chat GPT plus if you will still have that same context window or if it will be a hundred thousand, or if it'll be 32,000. So that matters because if you want to give, if you want to give Deep Research a lot of context before it gets started, you know, it can obviously go browse the web for its own context, but if you want to, you know, copy and paste a lot, or if you want to have some back and forth conversation before you get started, you will have to see once it's released on the plus version what the context window is. And it does incorporate that multimodal handling to synthesize information. So let's talk about some professional uses, right? And again at the end if you share this, if you repost this on, you know, Twitter or LinkedIn, you know, send me a message, but I'll go through and look later in the week I will share with you a list of 10 very specific in what I think are fantastic use cases. But I mean talk about research and analysis and finance, that's going to be huge. All right. I actually think this is going to potentially crush the management consultant industry if they do not use it. All right, give me like five, five to 10 minutes and I'll show you that at the end. But I also think this is going to accelerate tasks like market research, competitive analysis and literature reviews. Also consumer decision making. That's a use case that at least for me personally, I don't care as much about. But that's something that OpenAI talked a lot about that a lot of their internal employees that have been using this presumably for, you know, months are using this to help them make better essentially E commerce decisions. Right. For me that's not, that's not what's getting me out of the bed in the morning at 7:30am to me it's, it's redefining how we work as knowledge workers. But there's obviously a lot of, you know, consumer decision making or if part of, you know, what you do in your role is hey, which vendor are we going to use this year? Are we going to use the same vendors? Well, it's great for that. And then enterprise productivity, right? Just any internal research, right? So so much of what we do as knowledge workers is reading the Internet. Think about it, right? Maybe you're, you're working on a new project, you're looking up competitors, you're, you're helping do R D for a new product line, whatever it is. So much of what we do as knowledge workers is we read the Internet, right? So the use cases are there. And that's why Google's deep research immediately and still is, I think a top five, a top five AI tool. And I think this deep research from OpenAI immediately catapults itself into that top five, top three, top three place right there. So like we said right now for its constraints in current deployment, right now you only have a hundred use 100 queries a month on that 200amonth plan. So live stream audience, if you, if you want to test it out, if you don't want to pay that $200 a month, I still probably have like 50 or so queries. I'll go ahead and run it for you and put those results in our newsletter. All right. And it is right now designed to balance in depth research capabilities with significant computational costs. So this is the first version, this is the worst it is going to be. I do believe it's not going to have that five to 30 minute delay in the future. And also future plans include scaled down versions for lower tiers and, and wider geographic access once regulatory concerns are addressed yet. So right now there's certain countries in the, I believe in the eu, Iceland, some others that just don't have access to this yet because of those kind of existing laws in those places. Yeah, we got to talk about this. I'm not going to talk about all the sunshine and rainbows and not talk about. Well, are there ethical concerns or regulatory considerations? Heck yeah. So OpenAI did share, and we'll link to that in the newsletter today, all the benchmarks. So they did say that there's reduced hallucination rates, but the agent still does require human oversight to verify critical information. So just because you have an agentic reasoning model going out there and helping you with your work does not mean the human in the loop can kick their feed up and sip on coffee like I'm doing now. It's anything. I think this even heightens the increased role in responsibility of the human in the loop. Right. I think the more hands off you can be with agentic AI systems, the more vigilant the human actually has to be because it is human nature. As these AI systems get more and more capable, it is human nature for us as humans to spend less time verifying or giving it good input to get it going in the right direction. So also, here's the other thing that I don't think people are talking about biases. Guess what's on the Internet? A bunch of garbage, right? So unfortunately you cannot have a definitive way to steer operator where you want it to go and to avoid quote unquote, bad websites or low quality or websites that have biases. Right? You can't do that. It is a reflection of the Internet, which is a reflection of society. You can, with some nice prompt engineering, kind of, you know, do some whitelisting or blacklisting of certain websites or certain news organizations. And OpenAI's deep research to its credit, does do a good job of usually asking you before it gets started what type of sources do you want. But you can't avoid that entirely. All right. Also right now there are some safety measures to limit disallowed content and mitigate misuse. Right? So there are some built in kind of roadblocks and guardrails in there, which is a good thing. And like we talked about, it's initially restricted from certain regions that have stricter data and privacy regulations, the EU, UK, etc. All right, so let's go back and now visit, see if, let's see if both of ours worked. All right, so this is why we did two. One of them, one of them timed out. All right, so this is brand new. All right, so unfortunately our one about Deep Seek timed out. So make sure to go, go check out the newsletter. We'll share the results for that one there. But let's look at the first one. So the first prompt that I gave Deep Research was provide an analysis of Nike's latest 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 discrepancies and analysis opinions. All right, so now all I have to do, I, I do hope OpenAI improves this user interface a little bit. So essentially you have to go click this research completed thing right here and it's very hard to see because it's in this very small light gray font. But if you really want to see what it did under the hood, you got to make sure to go find that and click that. So once I click that now I'm going to have this kind of two tier thing, activity and sources. All right, so I'm not going to go through this whole thing. I'll actually probably share it in a document and if you want, if you want, you can go download it in our newsletter. But here's kind of this agentic process it went through. So I'm reading to you the summarized version of Deep Research Chain of Thought. So it says I'm gathering info on Nike's fiscal year, ensuring Q3 2024 matches others. This involves comparing Adidas and Under Armour's quarters for precise analysis. I'm pulling together Nike's Q3 2024 earnings, focusing on sales income, EPS and gross margin. So you'll see it's already doing things that I didn't tell it to. Right. I didn't talk about sales income, eps, gross margins, but it's doing this on its own because it is using a reasoning model. And then it says comparing these with Adidas and Under Armour, similar quarters but for a comprehensive analyst. And then the first thing it searched for, so it searched for Nike QTH earnings comparisons. All right, so you can go through and see. So it said, I'm exploring Nike's raw financial data for quarter three and comparing it with other sportswear giants. The gross margin increased to 51.3%. Might not be Nike. So it's already starting to see some, some irregular, some things that aren't regular in its research. So I can go, and I can click, I can go click this website. So we went to some website called fibre2fashion right. So I don't know. I'm looking at this website. I don't know anything about it. So if this was important, what I would do. And maybe this isn't a good source. Maybe it is. I don't know. But if you run this, I always encourage you to run this a second time after you go through and read and see where it went to. So I've done this a lot. After I use it, I will either whitelist or blacklist certain websites because as an example, I saw it really went to the Financial Times a lot, and I'm assuming that's because OpenAI had a partnership with the Financial Times, but I saw that it wasn't really bringing in a diverse enough kind of information set. So, you know, after you run it the first time, I would look at what operator or. Sorry, at what Deep Research did or didn't do, especially when it comes to the quality of sources that it went to. All right, so after it went to this fibri2 fashion, it says, seems like the article mostly highlights Adidas and Under Armour. So the downside is it doesn't take you to the exact page. It usually just takes you to the main domain, which I'm not a huge fan of. I wish there was. Or. No, actually, let me just double check that. I believe if you go to the Sources tab, it might take you to the actual. Let's see here. Okay, so here. Okay. So at the bottom, I'm just double checking. Okay, so maybe it doesn't. I thought that you could find it. So in. In some instances, it will give you the actual page that it went to. So I'm opening another specific page. So on investing.com, it gave me the actual page it went to, Whereas on this fibre 2 fashion, it just gave me the main domain. So, you know, make sure to check the activity and sources. All right, so let's keep going because I want you all to quickly understand and see this kind of chain of thought. So after it went to the Fibre to Fashion, then it went to investors.nike.com. so I would have liked to see it start there. But, you know, hey, that's why. That's why, you know, Deep Research is in charge and. And not me. But I could have told it, you know, hey, either start with this or I could have said, oh, and you know what? I was wrong, y'. All. You can start with a file. So I said that that was coming soon. I don't know if that was available right when they released it on Sunday, late Sunday night, but it looks like right now you can start with a file. So I would always, always, always recommend starting before you kind of send Deep Research off on its own. Start with the most high quality source information possible. Right. If you've taken our free prime prompt polish course, we go through the refineq process. I would first go through that refined queue process before you send Deep Research off on its own. All right, so then it went to investors.nike.com then it says, I'm figuring out Nike's Q3 fiscal year numbers for net income and diluted EPS, then looking at Adidas revenue. Right. So then after that it searched for Under Armours. All right, and it's talking, it's talking through, it's saying, Interestingly enough, result zero aligns with Under Armour's official site for quarter three, 2024, announced 2024 results. So interestingly enough, result zero is usually a knowledge graph. Right? So that's what I assume result zero means, not result one. All right, so then it says it went to retail.insight.network I'm scrolling through here. Right. I'm not going to take 30 minutes. And then it went to Global News Wire. Then it went to Finance Hill, it looks like report that they did on that. Then it went through and searched for Adidas last. All right, so that was all the activity. And then you can go to the sources. So it looks like it cited 16 different sources. And then at the bottom you can go to all sources. So it looks like sometimes Deep Research will look at websites but not use them in its kind of final report that it puts together. Right, so for podcast audience, you probably didn't see this, but it did complete a final report here. Right. So I have a very good looking. So here it says, it says Nike Q3 2024 versus Adidas and under Armour financial performance comparison. And then in the middle of this little document it puts together, which is great, it puts citations for all major facts. Right. Because the first thing it says, Nike reported a solid but modest performance in quarter three, 2024. Revenue was essentially flat year over year at 12.4 billion. And then to make sure. Oh, is that made up? Well, right there, I can hover over. That is from investors.nike.com I'm going to click on it and then I'm going to search for that same number. Right, so let's go for 12.4 and there we go. Third quarter revenues were slightly up on both a reported and currently neutral basis at $12.4 billion. So within, let's Go back there. Okay, so hopefully, hopefully y' all saw that right there. There we go. There it is. All right, so we can double check there on that, on that citation that Deep Research provided. So let's just take a look. So you know we have the Nike fiscal quarter three, 2024 highlights there. Then it goes into Adidas quarter three highlights. We keep scrolling down, there's Under Armours all cited and sourced from it looks like 16 different sources. Then we have comparison, comparative analysis and market context. So here it's looking at the different financial metrics across all three companies. Whereas first it broke it down company by company. And then we have market share, market news and context, analyst opinion which we did ask for and discrepancies. So we have a pretty solid. Right, so this isn't super long, which I actually like because sometimes I found that it's just entirely too long. So I'm actually checking here at the Word count to see how long this report actually was. So okay, so it was a 1500 word report full of citations, so pretty fantastic if you ask me. And yet entirely different than Google's Deep Research. All right, so that's a wrap. I didn't want this to be a multiple hour long. Sorry. It looks like Jose said the video feed might have been stuck. Michael, to answer this question, no, it does not visit as many sources as Google. Yes, Google, I generally get it to visit anywhere between 200 to 300. I've gotten it to visit up to like 1300 sites but it's different. Right. This is one of those instances where I don't necessarily think that quality or, sorry, I don't necessarily think quantity is as important as quality and reasoning. Right. So I think the Google Deep Research, they each have their own fantastic use cases. I think Google Deep Research casts a wider net and doesn't really use that, that reasoning or that logic, whereas OpenAI does it. Probably more like a human would. Right? I doubt that, you know, for most research tasks you're going to go to 200 or 3, 300, you know, websites. There might be some instances but for the most part I think your day to day researching, you know, that most knowledge workers would do. I don't think you're going to, you know, 200, 300. I think you're probably going to 16 to 17. Right. Hopefully those are high quality sources. But that's why I always encourage you, if you are using Deep Research, run it a second time first. That's the thing, you set it and forget it. You come back, you look at the sources, you look at what it did and you just give it feedback. Right. So you start a brand new deep research chat, you know, get based on the feedback and results. I cannot emphasize enough how important that is. It's the same, the same kind of quote unquote golden rules that you would do from basic prompt engineering. Right. The concept of a, of a five shot chain of thought or you know, a 32 shot chain of thought is always going to outperform a five shot. Right. Your second and your third iteration of this is always going to be better. Right. And you do have to do a lot of work providing good context before. Right. I did this. And for, for the sake of brevity to go a little faster. All right. The more and better and high quality information you start with is going to help your first output. Your second output is going to be exponentially better because you can look at the activity and the sources and you can steer it in the right direction. I cannot emphasize enough that right there is a cheat code, y'. All. All right, so I hope this was helpful, y'. All. And, and here, let's end it with one hot take. It is hot take Tuesday consultancies are going to completely be screwed if they do not use this period. Right. And you might be saying like, oh, Jordan, that's, that's, that's, that's a crazy take. No, it's not. I was not surprised at all to see the one video that OpenAI featured most prominently on its deep research page was from none other than Bain and company. Right. One of the biggest management consultancies in the world. Yeah, this is, you know what? If you're a young consultant out there, this is the future. And I think this is really going to disrupt the entire consulting industry. I'm not going to come in with a hot enough take yet and say, oh, you know, the consulting industry is going to die. No, but if your company is using a management consultant right now, you need to renegotiate your contracts and you need to have a very detailed and great understanding of how they are using agentic AI research tools. I am not kidding. Fortune, even Fortune 500 companies, if you have a long term contract with, you know, my friends at these companies aren't going to like this. You need to renegotiate the terms immediately because this expert, this does the, this does the job for that these companies would do. It's not doing it 100, but it is doing it in 10% of the time. And then oftentimes if they're using the tools Correctly, it is doing a much better job. This is going to completely disrupt how companies, medium sized companies grow, right? What I see happening is the kind of the middle tier of companies that use management consulting, so not your global Fortune 1000, right? But I'd say that middle tier, they're probably going to stop using management consultant companies unless they are honest, more honest, right? And say, hey, we used to spend, you know, we used to bill you, I don't know, 200 hours of research. Now with these AI tools, that's 20 hours, right? We cut it down by 90%, right? Professional services. I said this on my 2025 AI predictions and roadmap. Professional services are going to go through a price shock this year and this is perfect proof. If you are a medium sized company paying six, seven figures or more to a management consultant company, you need to immediately go back to them, renegotiate those terms and ask them how they are using this, this, this software. And you can't, like they're, they're not going to say, oh, we don't use chat GPT. Yeah, they all use chat GBT, right? All the stories have come out. PwC, Deloitte, right? All these companies have invested, you know, tens of thousands or hundreds of thousands of seats to use Chat gbt. So they need to be using this. And one of the biggest core skills of management consultants is researching and then making sense of all of that. And here's the thing, y', all, I don't care what anyone says. I won national writing awards, right? I don't really talk about that a lot on the show, right. I won ACP story of the year. I was a Pulitzer fellow. I used to be a pretty good writer. Chad GPT is a better writer than me, right? Not the stuff you read online. People are always like, oh, look at this, it's so bad. AI stinks. No, it doesn't. You stink it AI, right? Just like how I could go draw a stick figure and be like, look, art sucks. No, it doesn't. I suck at art. Right? AI is a better writer than me. Someone that used to win national awards, right? This Deep Research is a better management consultant than your management consultant company. So they need to be using it. I can't stress that enough. Sorry, just went on a random hot take there, y'. All. I hope this was helpful. Going over OpenAI's deep research, how it works and what, what you want it to be used for. If you're still listening, if you want to, to see a question, if you want me to run I can't do them all. I think I have like 50 left. You know, maybe just type in, you know, question to run or something like that and I'll, I'll go through, I'll pick a couple. I'll put the results in the newsletter. I hope this was helpful. Like I said, if it was, click that repost button. All right. Or if you're on Twitter, LinkedIn, I think that's the only where way I can see. Just send me a message, give me a couple of days and I'll send this to you. I built out 10 use cases for deep research that I think are really, really good. Right. I did the same thing for the new tasks feature and the feedback I got from the people that shared it, their minds were blown, those that actually put it into use. So you're going to want to go ahead, click that repost button there on LinkedIn or on Twitter if this was helpful. If you want access to those use cases, thank you for tuning in. Please go to your everyday AI.com Sign up for the free daily newsletter. We're going to be recapping today's show and a whole lot more, keeping you not just in the loop, but making you hopefully the smartest person in AI at your company or in your department. Also, keep a lookout in our in our email newsletter. Should have a pretty amazing guest coming up this week and a lot of great guests in partnerships and fun announcements lined up for February and March. So thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks y'. All.

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