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What Is OpenAI’s Deep Research?
OpenAI’s Deep Research is more than just another feature in ChatGPT. It’s an agentic, autonomous research assistant that uses advanced reasoning models to find, assess, and integrate information from multiple online sources. Here’s a closer look:
Agentic Behavior
Unlike a straightforward search function or a simple summarization tool, Deep Research can pivot its approach based on what it finds. If it encounters contradictory data in the middle of its exploration, it will shift course or dig deeper—much like a skilled human researcher.Chain-of-Thought Reasoning
The engine behind Deep Research is a specialized, fine-tuned version of OpenAI’s unreleased “O3” model. This variant is specifically designed for longer and more complex tasks. It uses chain-of-thought reasoning, meaning it “thinks” through multiple steps in sequence rather than conducting a single pass search.Web Navigation and Source Checking
Deep Research can navigate to external websites—potentially visiting several or even dozens of sites—and then cite sources as it synthesizes data. Users receive a final summary complete with citations, enabling easy verification of any fact, figure, or quote.
Currently, Deep Research is available to ChatGPT Pro subscribers, which costs $200 per month. Over time, OpenAI plans to roll out scaled-down versions: ChatGPT Plus users ($20 per month) will gain access to a limited quota of searches, and even free-tier users may see a small monthly allowance for Deep Research queries.
Deep Research NOW Available to Plus Users (Updated 2025)
ChatGPT Plus now has deep research available. The deep research context window for ChatGPT Plus is generous. ChatGPT Plus lets you have a deep research context window of up to 128,000 tokens. This is basically 300 pages of text or 100,000 words. When you are in deep research mode as a Plus user, the deep research context window will let you work with large volumes of data that you upload and keep context within a conversation. The deep context window extends to PROJECTS or if you ask ChatGPT to recall something.
How OpenAI's Deep Research Compares to Google’s Deep Research
On the surface, you’ll notice both OpenAI’s Deep Research and Google’s Deep Research share the same name—but the similarities largely end there.
- Quantity vs. Quality
Google’s Deep Research, which uses Google’s vast cached pages, can sometimes review hundreds (even thousands) of web pages. It then summarizes the key points using a transformer-based approach.
OpenAI’s Deep Research, meanwhile, emphasizes more quality-driven exploration. It may not consult as many sources, but it deploys genuine reasoning to pivot its approach in real-time, ensuring it’s digging deeper into the most relevant or contradictory points. - Omnidirectional vs. Agentic
Google’s version follows a largely omnidirectional process—casting a wide net and providing a broad summary. OpenAI’s Deep Research is agentic, meaning it performs an iterative cycle of finding, assessing, and refining the data. If it encounters a surprising bit of information, it can follow that thread to produce a more nuanced final analysis. - Use Cases
Both tools are helpful for large-scale research, but if you want a deeper “detective-like” approach—especially for professional research, financial analysis, or advanced business tasks—OpenAI’s Deep Research will likely be more powerful (though slower in its current form).
OpenAI Deep Research in Action: A Quick Demo
Imagine you want to compare Nike’s quarterly financials to Adidas and Under Armour. With ChatGPT’s Deep Research:
- You Type the Request
Let’s say you ask for Nike’s Q3 2024 performance, including key metrics like revenue, net income, earnings per share (EPS), and future forecasts. - Deep Research Asks Follow-up Questions
It might ask for clarification: “Which quarter are you referring to? Do you have preferred sources?” This step ensures the AI understands your query fully before embarking on a potentially 30-minute-long research session. - Agentic Web Exploration
Deep Research visits official investor websites (e.g., investors.nike.com), relevant financial news sites (Bloomberg, Financial Times, investing.com), and possibly competitor reports. It will pivot to Adidas or Under Armour if the Nike numbers suggest something interesting, such as a jump in gross margins that might also be mirrored—or contradicted—by competitor data. - Cited Final Report
Once done, it generates a comprehensive report of around 1,000+ words, fully cited with hyperlinks. You can hover over each data point to see precisely where it came from. If Nike’s net income is stated as $1.3 billion, it will link you to an earnings press release or a reputable financial outlet confirming that figure.
Under the Hood: The “O3 Reasoner” Model
One of the critical differences between regular ChatGPT (or even GPT-4) and Deep Research is the O3 Reasoner model:
- Unreleased Full Model
According to OpenAI, the version of O3 powering Deep Research is not the same as the O3 Mini or O3 Mini High found in ChatGPT for standard prompts. It’s a more robust, fine-tuned variant focused on autonomous research tasks. - Stunning Benchmarks
Early tests show Deep Research outperforming almost every other model on challenging evaluations like “Humanity’s Last Exam,” a new standard designed to test advanced reasoning and real-world knowledge. Where GPT-4 or Claude might score in the low single digits, Deep Research scores much higher—up to 26.6%. - Large Context Window
Deep Research may allow up to 200,000 tokens, meaning it can handle massive amounts of text at once. This feature opens doors for in-depth literature reviews, big data summarization, and other tasks that require scanning enormous volumes of online content.
Pairing Deep Research with Other ChatGPT Tools
In the past two months, OpenAI has introduced three major features:
- ChatGPT Tasks
Lets you schedule prompts for future times—ideal for recurring tasks. Not primarily built for in-depth analysis. - Operator
Essentially a virtual desktop that can run a variety of tasks outside ChatGPT. You can direct it step-by-step or have it autonomously perform a series of commands. However, you still have more “human in the loop” involvement than with Deep Research. - Deep Research
A specialized agent inside ChatGPT that scours the internet autonomously, focusing on reasoning and synthesis for extended periods.
Each tool has a different specialty; combined, they can tackle a wide range of complex projects—from analyzing huge datasets to automating blog-writing schedules.
Deep Research's Potential Impact on Industries—Especially Consulting
A significant hot take from many AI analysts, including those who have hands-on experience with Deep Research, is that it will disrupt how management consultancies operate:
- Research at 10% of the Time
Traditional consulting can spend hundreds of billable hours performing industry studies, competitive analyses, or due diligence. Deep Research can, in theory, complete a substantial portion of that work in a fraction of the time. - Renegotiating Contracts
Medium and large companies might push consultancies to lower fees, given that advanced AI tools can automate much of the work. Whether consultancies will candidly disclose how much time they save remains an open question. - New Model of Value
Rather than strictly selling “man-hours,” consultancies may need to pivot to providing specialized insight, expert interpretation, and advanced AI strategy. In other words, the human interpretation of agentic AI research may become the new premium service.
Best Practices for Using Deep Research
If you’re ready to experiment with Deep Research, keep these pointers in mind:
- Provide High-Quality Context
Before sending the agent off on a 30-minute exploration, give it solid background or attach relevant files. This step ensures it starts from the best source. - Review the “Activity” and “Sources” Tabs
Deep Research will provide a summary of its process. Check which websites it visited and consider blacklisting or whitelisting certain sources for the next run if you spot anything unreliable. - Run Multiple Iterations
After seeing the final report, refine your instructions and run it again. You’ll notice the second or third run often yields more accurate and better-structured outputs. - Maintain a Human-in-the-Loop
Despite the advanced reasoning, AI can still hallucinate or lean on outdated data. Always do a final check—particularly if the information impacts business strategy.
Final Thoughts: A Leap Toward AGI?
OpenAI’s leadership has been candid that Deep Research is an important step on the path toward Artificial General Intelligence (AGI)—a system that doesn’t just follow our instructions but can uncover new knowledge on its own. While that future raises ethical and regulatory questions, today’s Deep Research offers a glimpse of AI that operates with near human-like discretion, adjusting its direction in real-time and citing sources meticulously.
Whether you’re a consultant, a research analyst, or simply an AI enthusiast, watching—and possibly embracing—Deep Research should be on your short list. In an era when knowledge workers spend a vast chunk of their time searching the web, a smart, patient, and highly adaptable AI researcher can boost productivity exponentially. The bottom line is clear: if you’re not exploring these agentic, advanced tools, your competition probably is.
Topics Covered in This Episode
1. Overview of OpenAI’s Deep Research
2. Comparison to Google’s Deep Research
3. How Deep Research Works
4. Use Cases, Limitations, and Best Practices
5. Deep Research Larger Implications
Podcast Transcript
Jordan Wilson [00:00:16]:
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. Alright. I hope you're excited for today's show. I am too because this is a pretty big agentic 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. Alright.
Jordan Wilson [00:01:04]:
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 livestream 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 gonna get to that in a couple of minutes. So if you are new here, thank you for tuning in.
Jordan Wilson [00:01:35]:
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 everyday AI.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. Alright.
Jordan Wilson [00:02:11]:
So make sure you go check that out. And while you're there, make sure you check out our 2025 AI predictions and road map series. Yeah. Just because it's February doesn't make that any less valuable. I'm gonna 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, 2 or 3 weeks ago have already started to come true. So you need to go pay attention to that. Alright.
Jordan Wilson [00:02:36]:
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 wanna see an open AI 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 out loans outlined scenarios where it might not release certain AI systems due to potential risks.
Jordan Wilson [00:03:18]:
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.
Jordan Wilson [00:03:59]:
Alright. 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 gonna be a big trend is you're just gonna 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. Alright.
Jordan Wilson [00:04:51]:
And then last but not least, OpenAI making some more big partnerships in Asia. So OpenAI is making significant moves in Asia. It's 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 livestream audience, let me know if I'm getting that one wrong if you know. But it will include developing a Korean language assistance called Kanana in integrating open AI 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.
Jordan Wilson [00:05:39]:
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, broadening 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 $3,000,000,000 annually to integrate Crystal Intelligence and OpenAI's ChatGPT enterprise across its group companies. Alright. So, yeah, a lot going on as always, with AI news.
Jordan Wilson [00:06:35]:
Alright. So, let's get into it. Love love to love to see all the, the livestream people. So thank you for tuning in as always. Yeah. If you if you're a regular listener of the podcast, maybe you wanna ask questions when we have guests on, come join the livestream. 7:30 AM Central Standard Time. We do it on LinkedIn, Twitter, everywhere else.
Jordan Wilson [00:06:53]:
So, thank you everyone for joining us. Michael and Big Bogey, Douglas, Marie, Zulfia, 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. Alright. So here's first things first.
Jordan Wilson [00:07:20]:
Probably not gonna have access to it, at least right now. So right now, it is only available to ChatGPT pro subscribers. So that is the $200 a month pro plan. Alright? But it will be rolling out quote, unquote soon according to Sam Altman to ChatGPT plus users. So if you have that $20 a month subscription, you will get 10 searches a month. Alright? 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 gonna get maybe, like, 2. And then we have the ChatGPT, plus, users with 10 a month, and then ChatGPT pro.
Jordan Wilson [00:08:05]:
But right now, you only have access to it with ChatGPT pro. So, if you do want, f I I, y'all know, I I do have the pro account. So if you have questions you want me to run, go ahead and leave them now. And, you 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 gonna have access to this now, but I would, I would guess by, sometime in March, OpenAI might start rolling out access to everyone else. Alright. So we're gonna do this one a little different, and look at it live right away.
Jordan Wilson [00:08:42]:
Alright. So livestream 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, livestream audience, if you could let me know if you can see my screen, that would be great. And we're gonna do just 2 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, kind of even walk or talk the, the podcast audience through, what we have going on here.
Jordan Wilson [00:09:20]:
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. Alright. So it's a little different. It's not a mode that you would select in the drop down menu. Right? Which now, it's it's like naming alphabet soup. Right? Like, oh, there's o three mini and o three mini high and o one pro. Right? If if you have the, the pro account. Alright.
Jordan Wilson [00:09:46]:
Thanks. Thanks, Michael and and and Marie and, Sandra for letting me know you can see the screen. Alright. So now when you log in to ChatGPT, once you do have access to this, you will see a new deep research button. Alright. And it is very much different than the deep research from Google. I'm gonna show you how. But we're gonna do just 2, 2 of these live right now.
Jordan Wilson [00:10:09]:
So you can kinda see how it works. And most of these take between 5 and 30 minutes. That's why I'm gonna start 2, different deep, research queries at the beginning of the show. And then at the very end, we're gonna check-in on them and see how they did. Alright. 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. Alright.
Jordan Wilson [00:10:44]:
So this is something think can I mean, there's so many different use cases? Alright. 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. Alright. So I'm gonna go ahead now and click the deep research button. Okay. So you have to have that toggled on, and then I'm gonna go ahead and click the send button. So what's gonna happen first is, deep research reads this, and then it's gonna ask me some questions.
Jordan Wilson [00:11:10]:
So it's just like Google's deep research does, it wants to clarify because before it starts off on a 5 to 30 minute journey, you really wanna 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? Alright. So I'm gonna say, I'm just gonna say q 4 of 2024. Actually, I'm not even sure if all q four earnings are out for these companies. So I'm actually gonna say q3 of 2024 just to make sure because, yeah, I think, quarterly or earnings, at least for a lot of the tech companies, have happened this week. So, I wanna make sure I don't know if if Nike and Under Armour and Adidas. So I'm just gonna say quarter 3 of 2024. So I know that all that information should be there, and it should be publicly available.
Jordan Wilson [00:11:55]:
So for publicly traded companies, you know, you can always go and look at their, what is it, 10 k's or whatever to see all their financials. Alright. So that's all. It's asking me some other, some other questions. Do you have any preferred sources? So I'm just gonna say I'm gonna say preferred sources. I'm gonna say, reputable sources. Right? I would normally go through and 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.
Jordan Wilson [00:12:27]:
Right? So if you've taken our, prime prompt polish course going through the refined queue steps, I would generally go through that, but we're doing a live demo here. So I'm gonna go ahead, just answer those 2 questions and go ahead and click the send button. Alright. So I'm gonna 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.
Jordan Wilson [00:12:53]:
I will provide the findings once the research is complete. So, what I've seen here is it kinda 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. Alright. So now, like I said, I haven't tested how many concurrent deep research chats we can do. So I'm gonna go ahead, pop open another one.
Jordan Wilson [00:13:29]:
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 gonna walk you through these 2 deep research queries. So this one, I'm just saying find the latest news and information about deep seek. Alright. Yeah. Something that the whole world is talking about. So some qualifying questions saying, are you looking for information on DeepSeq AI, the language model, or something else related to DeepSeq? Also, do you need technical details, business updates, or general news? So I'm gonna say DeepSeq AI, the llm company, and I'm gonna say, you know, b 3 and r one.
Jordan Wilson [00:14:13]:
Those are the things that I wanna know about. And now it's asking for, like, the news, and I'm gonna say just I'm I'm gonna say just give me everything. Right? Alright. So now I'm gonna 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 gonna go ahead and explain here in a second kind of this activity and sources.
Jordan Wilson [00:14:41]:
And then, I'm guessing our deep seek researcher here. Right? That's so meta to say. Deep deep seek researcher within deep research mode in open AI. Not to be confused with Google seek research. Right? Alright. 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. Alright.
Jordan Wilson [00:15:03]:
So let's get back into the details to understand it. Alright. So we already kicked off the AI news for today. We already, got 2 of our, kind of deep research prompts running now while we give it time because, like I said, it's gonna take anywhere from 5 to 30 minutes. Alright. So, let's go over how to use it. So I just showed that to you. So number 1, you know, maybe you just joined in the middle of this live.
Jordan Wilson [00:15:31]:
Well, you have to have that $200 a month right now, ChatGPT 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 gonna show you how it's actually agentic and what that kinda means. But, how OpenAI describes it is an agent that uses reasoning to synthesize large amounts of online information and complete multistep 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.
Jordan Wilson [00:16:14]:
So a couple of 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, uh,000, but I will say this. I don't know if that will be the same, for ChatGPT plus users or if that 200 k is more, aligned to pro users. So just keep that in mind.
Jordan Wilson [00:16:49]:
Alright. 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 I'll say this. Google deep research doesn't really have agentic or reasoning capabilities. Alright? 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 a 1000 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.
Jordan Wilson [00:17:29]:
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 gonna change directions based on the findings. Opening eyes deep research is a little different.
Jordan Wilson [00:18:02]:
It is a gentric. 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 03. So we do have, o three mini and o three mini high already released even for free users inside of ChatGPT. But according to OpenAI, this new deep research is using a fine tuned version of the yet to be released 03 full model. Alright? So that's important to keep in mind, and that's one of the things that differentiates it from Google deep research. Alright? So multidirectional and agentic versus omnidirectional and more transformer model based.
Jordan Wilson [00:18:57]:
Then we have to talk about the difference between this and OpenAI's other recent kind of agentic or, you know, task based, features. Right? Because these are different. Because in just in the last couple of weeks now, we've gotten 3 pretty big steps. You could say 2 of these are agentic. So we got ChatGPT 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 task 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.
Jordan Wilson [00:19:39]:
Right? So, essentially, you can schedule prompts to be run at any time, but you can do really anything, within the GPT 4 o, set of capabilities and tasks. Operator is a little different. Operator operates technically outside of ChatGPT. Alright. And that also right now is only available, to ChatGPT Pro, where task is available, to ChatGPT 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.
Jordan Wilson [00:20:17]:
Whereas deep research, the newest, release from OpenAI, you don't have that kind of granular control. However, it does work, quote, unquote, inside ChatGPT, 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 can do a lot of those things in operator, but, like, why would you want to? Right? Because if 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. Alright. So hopefully that makes sense.
Jordan Wilson [00:21:05]:
So we do have 3 very different new offerings and features, from ChatGPT. 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 qual 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.
Jordan Wilson [00:21:47]:
I mean, Google has come roaring back, the last 2 months. Actually, should have a very special guest from Google this week, for you all that I think you're gonna wanna 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 100 of websites, and and both of them work kind of the same way. Another important thing to keep for for people to keep in mind, because everyone's like, oh, OpenAI just copied Google. Oh, not really. Right? The the term deep research, was actually, pegged to reporting on OpenAI back to, I believe, of June of last year before Google's deep research. Right? So, you know, it was reported on more than 8 months ago, kind of this deep research feature within OpenAI.
Jordan Wilson [00:22:39]:
So everyone's like, oh, OpenAI just copied Google. I mean, I I don't know. You you know, but, it is very similar, but it's also very and completely different. But, yeah, maybe if there's an appetite, y'all, if you wanna see a dedicated, Google deep research versus OpenAI deep research, let me know. Alright. 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. Alright.
Jordan Wilson [00:23:25]:
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 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. Alright. 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.
Jordan Wilson [00:24:26]:
I heard that I was like, wait, what? I was like, WTF? Did he just say that? And I 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 superintelligence, and that's essentially when AI can make itself better. Well, hoping I kinda just said the quiet part out loud. It's like, hey, We're hoping our future models are just gonna go work on their own and discover new knowledge for itself, not for the human user. Alright? So, yeah, had to point that one out. Alright.
Jordan Wilson [00:25:19]:
So what what am I talking about a digital detective in this omnidirectional and and 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 100 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 gonna look at this. So when you use it, there's an activity tab and 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, 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 is working on that to improve speed and to cut down inference and compute costs, on this new model. But you can literally see it and go through and look at the step by step activity. So it's kind of like how right now, if you're using 3 Mini or 1, 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.
Jordan Wilson [00:26:35]:
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. It or if 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. Alright.
Jordan Wilson [00:27:15]:
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 o three model, essentially the built in chain of thought process to plan and execute multistep 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.
Jordan Wilson [00:27:49]:
So, yes, you can also direct deep research what you want. Do you want a table? Do you want a long, long, verbose 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 03 Reasoner model.
Jordan Wilson [00:28:27]:
This is an unreleased version. And also the benchmarks on this thing are nutty. Right? And you might be wondering, well, how? Well, number 1, it's a model that we don't have access to anywhere else. The o three model. This is, again, according to OpenAI, a fine tune version of the full unreleased o three model. Whereas right now, you know, if whether you were a a free even free users have a little bit of o three minutei, and then, you know, paid users have o three minutei high. Yes. The naming is bad.
Jordan Wilson [00:29:00]:
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 I believe, a couple of weeks ago called humanity's last exam. Right? So one thing is with all of these models, there's arguments now that they're being, overfitted or overtrained 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 a large language model gobble them up. But so this new humanities last exam, it is a much more difficult, kind of benchmark, and it blew everyone else away by getting a 26% on that exam.
Jordan Wilson [00:29:48]:
And you'll see even the GPT 4 o 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 use to have a model. So even the GPT 4 o model on this humanities last exam got a 3.3%. Not that great. Right? There you have Grock, actually did a little better with a 3.8. Claude, everyone loves Claude. I'm not a big Claude fan.
Jordan Wilson [00:30:21]:
It got a 4.3. OpenAI's o one, got a 9.1. Then you had the 03 minutei 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 3 times as good as the next best competitor, not named OpenAI, which was DeepSeq's r one. And then Gemini's thinking got a 6.2. So exponentially better.
Jordan Wilson [00:30:56]:
However, I am curious, if OpenAI just used deep research or if deep research had access, to other tools. Like, as an example, the 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. Fantastic. Right? And we saw reports that OpenAI actually hired, countless PhD students to specifically train, the o three model. So that could be another reason as well why the full o three model did very well, but you combine it, with the ability to go research in an agentic way. It's powerful. Alright.
Jordan Wilson [00:31:51]:
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 ChatGPT plus if you will still have that same context window or if it will be a 100,000 or if it'll be 32,000. So that matters because if you wanna give, if you wanna 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 wanna, you know, copy and paste a lot or if you wanna 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 and what I think are fantastic use cases. But, I mean, talk about research and analysis and finance.
Jordan Wilson [00:32:52]:
That's gonna be huge. Alright. I actually think this is going to potentially crush, the management consultant industry if they do not use it. Alright. Give me, like, 5 5 to 10 minutes, and I'll show you that at the end. But I also think this is gonna 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 ecommerce decisions.
Jordan Wilson [00:33:29]:
Right? For me, that's not that's not what's getting me out of the bed in the morning at 7:30 AM. 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 gonna use this year? Are we gonna 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.
Jordan Wilson [00:34:01]:
You're looking up competitors. You're you're helping do r and d for a 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 5, a top 5 AI tool. And I think, this deep research from OpenAI, immediately catapults itself into that top 5, top 3, top 3 place right there. So like we said, right now for its constraints in current deployment right now, you only have a 100 use a 100 queries a month, on that $200 a month plan. So live stream audience, if you if if you wanna test it out, if you don't wanna pay that $200 a month, I still probably have, like, 50 or so queries. I'll go ahead and run it for you and and put those results in our newsletter.
Jordan Wilson [00:34:54]:
Alright. 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 5 to 30 minutes, delay in the future. And also future plans include scaled down versions for lower tiers 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.
Jordan Wilson [00:35:30]:
We gotta talk about this. I'm not gonna 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 and will, 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 feet up and sip on coffee like I'm doing now. If anything, I think this even heightens the increased role and responsibility of the human in 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.
Jordan Wilson [00:36:27]:
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.
Jordan Wilson [00:37:11]:
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 avoid that entirely. Alright. 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 re, regions, that have stricter data and privacy reg regulations, the EU, UK, etcetera. Alright. So let's go back and now visit.
Jordan Wilson [00:37:55]:
Let's see if, let's see if both of ours worked. Alright. So this is why we did 2. 1 of them, one of them timed out. Alright. So this is brand new. Alright. So, unfortunately, our one about deep seek timed out.
Jordan Wilson [00:38:08]:
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 highlights any discrepancies and analysis opinions. Alright. 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.
Jordan Wilson [00:38:50]:
But if you really wanna see what it did under the hood, you gotta make sure to go find that and click that. So once I click that now, I'm gonna have this, kind of 2 tier thing, activity and sources. Alright. So I'm not gonna go through this whole thing. I'll actually probably share it in a document. And if you wanna 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.
Jordan Wilson [00:39:18]:
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 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 for a comprehensive and analyst. And then the first thing it searched for so it searched for Nike Q3 earnings, comparisons. Alright.
Jordan Wilson [00:39:56]:
So you can go through and see. So it said, I'm exploring Nike's raw financial data for Q3 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, 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, fibre to fashion. Right? So I don't know. I'm looking at this website.
Jordan Wilson [00:40:28]:
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 you if 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.
Jordan Wilson [00:41:07]:
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. Alright. So after it went to this FIBRI 2 fashion, it says, seems like the article mostly highlights Adidas and Under Armour. So the the 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.
Jordan Wilson [00:41:54]:
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 fib ray to fashion, it just gave me the main domain. So, you know, make sure to check the activity and sources. Alright.
Jordan Wilson [00:42:19]:
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 fibbrae 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 you 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.
Jordan Wilson [00:42:55]:
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 if you've taken our free prime prompt polish course, we go through the refined queue process. I would first go through that refined queue process before you send deep research off on its own. Alright. So then it went to investors.nike.com. Then it says, I'm figuring out Nike's q three, fiscal year numbers for net income and diluted EPS, then looking at Adidas revenue. Right? So then after that, it searched for Under Armour's.
Jordan Wilson [00:43:39]:
Alright? And it's talking it's talking through. It's saying, interestingly enough, result 0 aligns with Under Armour's official site for Q3 2024 announce 2024, results. So interestingly enough, result 0 is usually a knowledge graph. Right? So, that's what I assume result 0 means, not result 1. Alright. So then it says it went to retail.insight.network. I'm scrolling through here. Right? I'm not gonna take 30 minutes, and then it went to Global Newswire.
Jordan Wilson [00:44:10]:
Then it went to, Finance Hill. It looks like, reports, that they did on that. Then it went through and searched for Adidas last. Alright. 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.
Jordan Wilson [00:44:42]:
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, q 3 20 24 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 Q3 2024. Revenue was essentially flat year over year at 12,400,000,000. 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 gonna click on it, and then I'm gonna search for that same number.
Jordan Wilson [00:45:29]:
Right? So let's go for 12.4, and there we go. 3rd quarter revenues were slightly up on both a reported and currently neutral basis at $12,400,000,000 So within, let's go back there. Okay. So, hopefully hopefully, y'all saw saw that right there. There we go. There it is. Alright. So, we can double check there on that, on that citation that deep research provided.
Jordan Wilson [00:45:55]:
So let's just take a look. So, you know, we have the Nike fiscal quarter 3, 2024 highlights there. Then it goes into Adidas, quarter 3 highlights. We keep scrolling down. There's Under Armour's, all cited and sourced, from it looks like 16 different sources. Then we have, compares 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.
Jordan Wilson [00:46:34]:
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. Alright. So that's a wrap. I didn't want this to be a multiple, hour long.
Jordan Wilson [00:47:16]:
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.
Jordan Wilson [00:47:44]:
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 cast a wider net and doesn't really use that that reasoning or that logic. Whereas open AI does it probably more like a human would. Right? I doubt that, you know, for most research tasks, you're gonna go to 200 or 300, you know, websites. There might be some instances, but for the most part, I think your day to day researching, you 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.
Jordan Wilson [00:48:34]:
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.
Jordan Wilson [00:48:58]:
Right? The concept of a of a 5 shot chain of thought or, you know, a 32 shot chain of thought is always gonna outperform a 5 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. Alright? 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.
Jordan Wilson [00:49:37]:
Alright. 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 the that's that's a crazy take. No.
Jordan Wilson [00:49:59]:
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 I think this is really going to disrupt the entire consulting industry. I'm not gonna come in with a a hot enough take yet and say, oh, you know, the consulting industry is going to die. No.
Jordan Wilson [00:50:38]:
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 gonna like this, you need to renegotiate the terms immediately because this ex this does the this does the job for that these companies would do. It's it's not doing it a 100%, but it is doing it in 10% of the time. And in often times, 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 the middle tier of companies that use management consulting. So not your global fortune 100.
Jordan Wilson [00:51:39]:
Right? But I'd say that middle tier, they're probably gonna 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 6, 7 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 gonna say, oh, we don't use ChatGPT. Yeah.
Jordan Wilson [00:52:32]:
They all use ChatGPT. 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 ChatGPT. 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.
Jordan Wilson [00:53:02]:
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 Gbq 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.
Jordan Wilson [00:53:19]:
AI stinks. No. It doesn't. You stink at AI. Right? Just like how I could go draw a stick figure and be like, look. Art sucks. No. It doesn't.
Jordan Wilson [00:53:29]:
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.
Jordan Wilson [00:53:59]:
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. Alright? Or if you're on Twitter, LinkedIn, I think that's the only where way I can see.
Jordan Wilson [00:54:23]:
Just send me a message. Give me a couple 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 gonna wanna 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 everydayai.com.
Jordan Wilson [00:54:52]:
Sign up for the free daily newsletter. We're gonna 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 and fun announcements lined up for February March. So thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks y'all.