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OpenAI’s 01 Pro: Deep Dive into the Future of Reasoning Models
The New Frontier in Large Language ModelsOpenAI's 01 Pro introduces a new breed of AI models, designed to tackle reasoning tasks with unprecedented depth. Unlike traditional transformer-based GPT models, 01 Pro specializes in chain-of-thought reasoning, which allows it to break down complex problems logically and systematically. It’s not just about generating text anymore—it’s about thinking.
Understanding the DifferenceWhile GPT models excel at conversational exchanges and rapid-fire tasks, reasoning models like 01 Pro are built for deep work. Think of it as the difference between a colleague who thrives on constant interaction versus one who prefers methodical, uninterrupted focus. The result? Insights that are not only accurate but comprehensive.
Who Is It For?Professionals in STEM, legal, finance, healthcare, and data-intensive fields will find the 01 Pro particularly invaluable. Its ability to handle intricate coding, legal research, and complex data analysis sets it apart from other models. Even non-technical users can benefit from its high-level reasoning, provided they feed it sufficient and structured data.
The Cost of Intelligence: Is 01 Pro Worth $200/Month?
Breaking Down the PricingThe 01 Pro subscription is priced at $200 per month, significantly higher than the $20 ChatGPT Plus plan. However, this steep price tag reflects its capabilities:
Enhanced Context Window: Process larger datasets in a single query.
Advanced Reasoning: Ideal for tasks requiring step-by-step logical breakdowns.
Reliability: High consistency in responses, particularly for professional use cases.
Real-Life Use Case: Podcast AnalyticsUsing the 01 Pro model, podcast performance analytics were generated in just 11 minutes. This included:
Calculating adjusted averages (removing top and bottom outliers).
Listing episode performance percentages against the adjusted average.
Analyzing patterns in titles, release times, and themes.
Suggesting optimized episode titles and identifying trends in underperforming content.
Without AI, these tasks would have taken several days; even with GPT-4, they would require 3-4 hours of manual back-and-forth interactions.
Key Features of 01 Pro
Chain-of-Thought Reasoning01 Pro excels at breaking down tasks into smaller, logical steps, ensuring reliability and depth in its answers. Whether it’s analyzing dense datasets or performing legal research, the model shines in domains where precision is critical.
Strength in STEM and BeyondFrom coding to case law, the model’s accuracy in high-stakes scenarios makes it a trusted partner for professionals. Notably, it offers unmatched performance in math, physics, and financial modeling.
Adjusted Prompting StrategyTo maximize its potential, users must provide:
Comprehensive context and structured prompts.
Concise but informative phrasing.
Examples or templates to guide the output.
Where It Excels (and Falls Short)
Advantages
Accuracy in Complex Domains: Outperforms GPT models in areas like data science and case law.
Time Efficiency: Significant time savings for intricate tasks.
Reliability: Consistent, repeatable results under demanding conditions.
Limitations
Time-Intensive: Response times can range from 4 to 15 minutes.
No Internet or Tool Access: Limited to offline reasoning without the support of search or advanced tools like canvas mode.
High Cost: The $200/month subscription may be prohibitive for casual users.
Predictions for the Future
With OpenAI continually refining its models, 01 Pro could soon gain access to additional tools and functionalities, potentially enabling:
Integration with Internet Access: Expanding its utility to real-time research.
Enhanced Agentic Behavior: Automating complex workflows beyond simple task execution.
Wider Applications: Bridging the gap between reasoning and collaboration.
Conclusion: Is 01 Pro Right for You?
For professionals who rely on data-driven decision-making, 01 Pro is a game-changer. Its advanced reasoning capabilities and potential to save time make it well worth the $200/month price tag for those in STEM, finance, healthcare, and other high-stakes industries. However, casual users or those focused on conversational AI might find GPT-4 sufficient for their needs.
As reasoning models evolve and tools are added, the line between AI as a tool and AI as an autonomous assistant continues to blur. OpenAI’s 01 Pro is undoubtedly a bold step into that future.
Topics Covered in This Episode
Topics Covered in This Episode:
1. Difference between transformer models and reasoners
2. OpenAI's new reasoning model, o1 Pro
3. Different variations of o1
4. Use-cases for o1 pro
5. ChatGPT Plus Overview: What's included for $200
Podcast Transcript
JJordan Wilson [00:00:16]:
There's a new breed of large language models that you probably haven't used. Even the dorky among us that follow generative AI everyday, So many people haven't really used or figured out or found use cases for these new reasoning models like OpenAI's o one and o one pro. So today, I'm gonna be specifically going into o one pro, telling you exactly what it is, telling you who it who it's for, how it works, and ultimately, if it's worth the price tag. Yeah. You've seen the price tag on this new o one pro? $200 a month. There's other things that are included in that, but I think we've kind of been spoiled by how cheap, and available large language models have been, especially as prices continue to plummet. And then you see something like a $200 a month subscription for a model like 01 Pro, and you're like, what is this and is it worth it? Alright. We're gonna be tackling that and hopefully a lot more on today's edition of Everyday AI.
Jordan Wilson [00:01:34]:
What's going on y'all? My name is Jordan Wilson. I'm the host of Everyday AI. This thing's yours. This is your cheat code. This is a daily livestream podcast and free daily newsletter helping us all not just keep up with AI, but how we can actually use it to grow our company and our career. This is how you become the smartest person in your company, in your department at AI. Alright? And we all need it whether you know it or not. Right? Been saying this for many years.
Jordan Wilson [00:01:59]:
Even if you don't think that you're a a dorky or a techie person, we all have to learn how to get the most out of AI. Alright? And where you start doing that is, well, here, you're listening, but also at your everydayai.com. That is our website. There, you need to go sign up for the free daily newsletter. Each and every day, we recap our show for the day as well as a lot of other information in that daily newsletter. You know, the latest news, trends, fresh finds from across the Internet, tutorials, everything you need to know. It is your guide every single day. Go read it.
Jordan Wilson [00:02:33]:
It doesn't take long. Also, on our website, there's more than 430 episodes that you can go listen to from the world's leading experts all for free sorted by your category. So go click on that AI learning tracks or that episodes on our website. Go find whatever you care about. It can be marketing, legal, tech, governance. I don't care. It's all there for you for free. So make sure you go check that out.
Jordan Wilson [00:02:56]:
Speaking of checking things out, Monday, January 20th, mark your calendars y'all. We are, next week, going to be doing 5 episodes. These are not just our 2025 AI predictions, but they are more like a road map on how to deal with everything that's coming. Yeah. I literally spent thousands of hours in 2024 talking to smart people, thinking about AI, reading about it, writing about it. This is the culmination of all that. You are not gonna wanna miss it. Get your board together.
Jordan Wilson [00:03:26]:
Get your team together. You need to tune in. Alright. Before we get into today's show, I'm excited about it. Let's first go over the AI news for the day. Livestream audience, thanks for joining. Got a couple questions for you. Let me know.
Jordan Wilson [00:03:38]:
Alright. So first, Google is partnering with the Associated Press to enhance Gemini with real time news updates. So Google's AI chatbot, Gemini, is set to integrate real time news from the Associated Press, marking a pretty significant collaboration between the tech giant and a major news publisher. So the deal allows AP to deliver a continuous feed of real time information to the Gemini app. AP's chief revenue officers emphasized the importance of this collaboration, highlighting a commitment to nonpartisan reporting and accurate journalism. Financial terms of the agreement have not been disclosed, raising questions about compensation and how AP's content will be credited within the Gemini app. This is another piece of news related to this. This, Google and AP deal comes literally at the same time as OpenAI announcing a new partnership with Axios to expand local newsrooms.
Jordan Wilson [00:04:35]:
So OpenAI will fund Axios' local newsroom expansion into a couple cities, cities, Pittsburgh, Kansas City, Boulder, and Huntsville, marking the first time that it has directly funded newsrooms in a publisher deal. So this 3 year partnership allows OpenAI to use Axios journalism for chat gbt responses while providing Axios access to AI tools for content creation and distribution systems. Yeah. We're, I'm actually excited. We're gonna have a specific episode coming up very soon, so make sure you tune in, on AI's impact specifically on journalism. I was a former journalist, so, you know, it's gonna be a conversation near and dear to my heart. Last piece of AI news. Microsoft has unveiled new usage based AI powered Copilot chat for corporate users.
Jordan Wilson [00:05:23]:
So, Satya Nadella, Microsoft's chairman and CEO, introduced a new category of PCs and of Copilot with built in generative, generative AI tools at a recent event. So the newly launched Microsoft 365 Copilot chat offers an alternative to the existing Copilot service, which costs organizations $30 per employee per month. So now this is based on usage. So, yes, Microsoft 365 Copilot chat based on usage. So the new model allows organizations to pay based on actual usage. Right? So if you have thousands of employees at your company, maybe everyone's not ready to, you know, throw down a couple $1,000,000. So the new model, like I said, allows, organizations to be charged for actual use use with charges calculated per message sent starting at just 1¢ per message, which could, encourage wider adoption among companies. So Copilot chat, like normal Copilot, can summarize documents, fetch web information, and create task performing agents.
Jordan Wilson [00:06:23]:
So unlike the traditional Microsoft 365 Copilot, which is also integrated into applications like Word and Excel, Copilot chat is accessible via the Microsoft 365 Copilot app on various platforms. Alright. So, let me know. Do you guys do you guys want to see more on on Copilot Copilot chat? Let me know. Alright. I'm excited to get going. So let's talk about it. OpenAI's 01 Pro live stream audience.
Jordan Wilson [00:06:49]:
Thanks for joining us. Have you all used this? Same thing. Podcast peeps. I always say check the show notes. You can reach out to me on LinkedIn, email the show. I wanna hear from you all. Right? I can only make this better the more information you tell me, but I'm curious for our live stream audience. Are are are y'all using OpenAI's 01, 01 pro? Let me know.
Jordan Wilson [00:07:11]:
But let's let's just dive straight into it, enough enough chit chat. So here's the gist of 01 pro. Alright. So it is a reasoner model. Okay? It's 2 different classes. So the GPT family of models, those are transformer models. Right? This is just the easiest way to separate the 2. And a lot of other companies, you know, Google came out with their, kind of flash thinking, which is a reasoning model.
Jordan Wilson [00:07:40]:
Amazon Nova has a reasoning version. DeepSeq, the Chinese AI company. So just in the last, like, 4 weeks, just about all the big tech companies have said, alright. We need a reasoning model because we see how powerful it is. So o one is a reasoner model different than, than a GPT model. Right? GPT 4 o. You know, all these other models that we've been using for, you know, now 2 plus years, they've been these kind of transformer models, and they're completely different. And we're gonna talk about the difference between the 2, but that's the biggest thing.
Jordan Wilson [00:08:12]:
It uses chain of thought thinking kind of under the hood. And I like to say when you're working with ChatGPT, like, do any of you have different types of colleagues, right, for those of us that may be still going in office or are hybrid? You have those colleagues, you know, you're getting work done, but you have those colleagues that work by talking. Right? And you're just going back and forth, back and forth. And then you have those, employees or, you know, coworkers that work by putting their headphones on. You know, you talk to them once and then you check-in with them at the end of the day. Right? Those are those deep work employees that put their headphones on. They just crank it. Think of that as, like, those 2 different types of large language models.
Jordan Wilson [00:08:51]:
Right? There's some people that you work with, right, that require a lot of talking, right, require a lot of conversation, require a lot of collaboration, you know, and and that's how they work best. And then there's others. It's just like they don't wanna talk. Dump all the information on them. Give them their instructions. Let them ask questions up front if they have any. But then they go to work, and you see them later. So the latter, that is what these new o one models are.
Jordan Wilson [00:09:17]:
Alright. So as an example for our livestream audience here, you probably see it on the screen. I took a screenshot of this, but, yeah, generally, when I ask these model questions, it's gonna take anywhere from 4 to 15 minutes to get a reply. At least how I use, o one, or at least o one pro. So I do use the, you know, the, chat gpt pro account. Right? So that is $200 a month. So I'll I'll I'll share, what that includes. But, you know, for the most part, when I'm using o one pro, I know this is my deep work colleague.
Jordan Wilson [00:09:49]:
I'm still using GPT 4 o all the time, all day every day. I like using it in canvas mode. I just started really using, the new task mode, which if you listen to our show yesterday, we went over that new mode. By the way, for for those of you that shared the episode yesterday, I took I spent probably about 3 hours on a document, that showed you how to use tasks. And to go over this concept of task concept, task stacking, if I'm being honest, it's one of the best documents I've ever created in my life. So if you haven't gone and shared that task episode, go do that, and I'll share that document with you. Alright. So that's the gist of it.
Jordan Wilson [00:10:29]:
This is a reasoning model. This is not a GPT model. Takes a long time to think. It uses this chain of thought process under the hood. Right? So, you really have to use it at the right time for the right purpose for the right reason. Alright. So let's just take a quick look at the tiers or subscriptions. So there is a free version of chat g p t.
Jordan Wilson [00:10:56]:
I had a lot of chat gpt shows this week because, you know, it's early in the year. A lot of people are asking and, you know, we had some popular episodes like a year ago, and I'm like, yo, these are old. So literally this week, we did an episode on chat gpt free versus plus. Alright? So you have your free chat gbt, which is actually pretty good now. Right? I used to tell people don't touch it with a 10 foot pole. It's dangerous. It's not like that anymore. The free version's pretty good.
Jordan Wilson [00:11:18]:
You have the $20, plus plan that I think a lot of people are on, that just gives you essentially almost all the features except the o one pro model. So even on the chat GPT plus, you have o one. It's much more limited in terms of messaging, and you have an o one mini. But on the pro plan so if you wanna use o one pro, which is technically open AI's most powerful large language model, you do have to pay $200 a month to be on that pro plan, o one pro. But there's a lot of other, I guess, features and benefits. So when we talk about Sora, you have way more, usage in sorta. You have unlimited usage of GPT 4 o, whereas, you know, normally, even on a chat gbt plus plan, you you run-in the limits. So, essentially, you get more limits on the pro plan.
Jordan Wilson [00:12:06]:
It's it's unlimited, GPT 4 o. It's unlimited advanced voice mode where on the plus plan, it's a little limited, and then you get access to o one pro, which you can only access via, that $200 a month subscription. Yes. It is confusing. Yeah. Allison's kinda talking about the naming here. It is weird because the plus is that $20 a month where a lot of companies like Microsoft, they have their, the $20 a month is called pro. So even I get confused all the time.
Jordan Wilson [00:12:42]:
But yeah. So chat gbt plus $20, pro $200. Not to be confused with all these other ones that say pro for the $20 tier. Yeah. Alright. So let's let's talk about how open AI describes their model. So they say more thinking power for more difficult problems. So they say chat gpt pro, in this instance, they're talking about o one pro actually, provides access to a version of our most intelligent model that thinks longer for the most reliable responses.
Jordan Wilson [00:13:10]:
In evaluations from external expert testers, o one pro mode produces more reliably accurate and comprehensive responses, especially in areas like data science programming and case law analysis. Compared to both o one and o one preview, o one pro mode performs better on challenging machine learning benchmarks across math, science, and coding. Alright. So, yeah, speaking of benchmarks, essentially, 01 pro, it's PhD level. Right? It's it's no longer where you really have to work. So that's the other thing. I think with, like, a model like GPT 4 o, you can get these, you know, quote, unquote PhD level, responses. You just have to have a master's degree level to get it there.
Jordan Wilson [00:13:57]:
Right? It's different with o one, especially with o one pro. You don't have to have a lot of experience to get it to that kind of, we'll just say, quote, unquote PhD level. It kind of can do it on its own because it uses this kind of under the hood, you know, step by step chain of thought reasoning. Right? So it's weird, and I talked about this all the time. And if you've taken our our free prime prompt polish course, you understand. The GPT 4 o family of models is extremely capable, but to get the most out of it, you gotta know the basics of, like, prompt engineering. Right? Without getting too technical, there's things called shots. Right? And when working with a a transformer model, or a transformer family of models, regardless of if you're talking, you know, chat GBT, Gemini, Claude, etcetera.
Jordan Wilson [00:14:45]:
Right? If you do some basics of prompt engineering, it's gonna be better. So a a 5 shot prompt as an example is always gonna do better than a 0 shot prompt. So what that means, and I'm gonna oversimplify it here. So sorry, machine learning PhDs. A shot is when you give a model an example, an input, and output. You tell it good, bad why. That's what I like to say. Input, output pairing, good, bad why.
Jordan Wilson [00:15:08]:
So you are essentially shotting this model. So that's what the o one and technically o three, right, like OpenAI teased the o three model. It's not out. I don't think it'll be out anytime soon, but this o family of models kind of goes through that process, on its own. Right? It doesn't give itself examples, but it goes through that chain of thought thinking, and I'm gonna show some examples for you live on the screen. But from a benchmark perspective, the gains are huge. Right? So some of the biggest gains between, like, the o one family and the GPT 4 family are in math. I mean, you're you're automatically like Olympiad, you know, Math Olympics, like gold silver medal.
Jordan Wilson [00:15:48]:
Right? So it's smarter than 99.999995% of humans, in math. Physics, same thing, huge jump from, 04 to or or or sorry, to GPT 40 and the o one. Getting ahead of myself. Skipping skipping over 3, you know, 4 00301, alphabet soup already. Other categories. So, yeah, mathematics, physics, LSATs. Right? So, you know, they have actual models take exams. So huge.
Jordan Wilson [00:16:18]:
So obviously, if if you are in software development, if you are in research, if you work in anything that has to do with complex, math, complex equations, business intelligence. Right? If you are essentially working with numbers, working with research, I think o one makes the case for itself. But I'm gonna keep going, and we're gonna talk about even some everyday use cases. So first, you might you might have got confused. Right? Because I'm dropping all these different, words on your head. O one this, o one that, o one. Right? Because, technically, o one has been out for a while. So we saw o one preview and o one mini in September.
Jordan Wilson [00:17:01]:
So, yeah, all those other big companies that have that are releasing these kind of quote, unquote reasoning models, This is just the last few weeks. OpenAI has been here for a few months since September. They released o one preview and o one mini. And then in December, they essentially knocked the preview off of it and said, okay. Now this is o one. So, technically, if you think of power, and not all these models are here, but you have o one mini, o one preview, which is now no longer there, o one and o one pro. So o one pro and the kind of full version of o one are new ish. They've only been out for a couple of weeks.
Jordan Wilson [00:17:39]:
I've been using it. I didn't get it right away probably about a week or so after it was released. So I've been using it now for about 3 weeks pretty heavily. Alright. So let's just go over quick, and and live live stream audience. Yeah. Keep keep getting your questions in. I'm gonna try to, tackle some of these at the end.
Jordan Wilson [00:18:00]:
Mark says it looked like a lot of work, Jordan. Thanks for sending it. Oh, yeah. That's the, the task thing from yesterday. Task stacking. Everyone slept on the chat gpt tasks. Even I think open AI missed missed the point. Alright.
Jordan Wilson [00:18:12]:
So let's go over the bullet point details. I'm gonna go through this quickly because I'm gonna show you here at the end of the show for a podcast audience. I'm gonna try to walk you through it. O one in action for even nontechnical reasons. So, what is a one pro? Alright. Let's go through all the bullet points here. I wanna make sure I give y'all, the details. So o one pro is OpenAI's premium model available to chat g p t users for $200 a month.
Jordan Wilson [00:18:40]:
Also, there's other third party, third party platforms where you can just pay by usage. Right? You can't get access to all the other tools and all that, but, you know, here's here's the thing. At least right now, the o one model, it doesn't have all the other tools anyways. Right? It doesn't have Internet access. Right? The the the o one pro model. The o one pro, you can actually upload files, which is nice, and you can with that on o one as well. Whereas previously, the o one family of models, you couldn't, you couldn't upload files. And still on o one mini, you can't upload files anyways.
Jordan Wilson [00:19:18]:
But, it's described o one pro is described as an AI colleague for complex tasks, and it's more about reasoning than collaboration. So how does it work? Well, like we talked about, enhanced reasoning. It has this chain of thought processing to better, to kind of enable better, logical breakdowns, accuracy and reliability. So we shared about this on the show before. OpenAI kind of went over this, like, 4 of 4 reliability concepts. Right? Where, you know, there's a little more variability in the kind of GPT, family of models. With the o series of models, there's much more reliability. And, also, this is specialized for, you know, professionals.
Jordan Wilson [00:20:01]:
If you are a specialized professional, o one is for you. So it's strong in STEM coding, legal, and data science. Advantages. What the heck are the advantages of this? Well, like we said, enhanced reasoning. So the chain of thought processing under the hood, enables better logical breakdowns. We talked about the 4 of 4 reliability. So that's essentially, you you know, when OpenAI did their internal benchmarks, they wouldn't just say do it once and, like, oh, okay. Yeah.
Jordan Wilson [00:20:31]:
This passes. They would do it actually 4 times for more consistent results. Alright. So where does it excel? So I said, hey, here's where you can use it. There's strong use cases here. It excels in scientific research. So analyzing data sets, develop, developing hypothesis, designing experiments, financial modeling, forecasts, complex calculations, legal workflows. Right? Analyzing case law, summarizing documents.
Jordan Wilson [00:20:59]:
It excels at anything STEM related. Right? Anything. So it specializes in kind of synthesizing and analyzing dense data sources. If and and here's the thing, and I'm gonna show you some more nontechnical use cases at the end and how I'm using it. We all have access to data. Right? Data over the past 5 to 10 years, data used to be something for for the geeks. Now we all have access to data. There's more and more data being collected, which is why I think there's actually broad use cases for people to be using the o one, pro models.
Jordan Wilson [00:21:32]:
Alright. So what is it good for? Kind of already, talked about this, but this is what people are always asking. Right? Right? Where does Excel? What's it, like, what is it good for? Who should use it? So I I I wanna tackle this from all areas. So, who is it good for? So professionals in STEM, finance, law, and health care. Great health care, use cases as well. So any users with high stake tasks requiring accuracy and advanced reasoning. So, developers also great for anyone in software development coding. So handling intricate coding and debugging requirements, professionals in fields like medicine that require precision.
Jordan Wilson [00:22:11]:
Alright. Now let's do the breakdown. People are always asking, well, what's the difference? Should I just be using 4 o? Should I be using 01 pro? The way I like to say that think of these 2 chatty colleagues. Right? For the most part, we've been kind of spoiled by having these transformer models that are highly capable. If you know how to use them, I would still say 80% of the the the business world has no clue how to use something like chat gbt. Something that has now become, synonymous with AI and has name recognition like Google. People still don't know how to use it. Right? And people in positions of leadership, kinda scary.
Jordan Wilson [00:22:48]:
But 01 pro excels in complex reasoning. GPT 4 0 excels in, well, fat being fast, speed, general tasks. Right? Also GPT 4 0 has access to more tools, which is important. So you can upload files in o one pro, but you can't use things like canvas. You can't use things like tasks. You can't use things, like chat gbt search. Connect it to the Internet. That's an important thing, because when you are using, an a non connected model, you have to keep in mind, you better be feeding it a ton of up to date data, or whatever you are asking it should hopefully not require a lot of up to the minute real world information because that model does not have it.
Jordan Wilson [00:23:34]:
Alright. So how should you prompt o one pro? This is where it's completely different. And, again, think of my comparison. Does anyone else have that chatty coworker and then the coworker that just has the headphones on? Right? I'm the latter. You never would have guessed. Right? Someone that just talks about AI nonstop and sometimes talks for way too long. But, you know, speaking up like even my journalist days. Right? I used to be the guy.
Jordan Wilson [00:24:01]:
I would go in there after an assignment. I would go talk to my editor, get all the information I would need. Right? Then I would go sit in the back, put my, you know, headphones on. They weren't even headphones. I don't know if this makes me weird. I would have, like, the, you know, sound canceling. There's no music. I would just put those things on.
Jordan Wilson [00:24:19]:
I would just go to work, do the whole thing. Right? But then there's other people, you know, hey. They wanna check-in at every single every single point. Right? But that's that's the difference. So with o one, you have to begin with, number 1, a lot of data, a lot of context. You need to have clear and structured prompts to define task parameters effectively. You need to provide examples or templates to guide the model's output format, and you need to use concise yet informative phrasing to maximize response relevance. If y'all have taken our, free prime prop polish course, yes, we're gonna have new ones in 2025.
Jordan Wilson [00:24:53]:
Give me a minute. I'll explain why later. But, you know, we we we walk through something called refine queue. So if you have taken our free course and there's been, like, I don't know, 8,000 of you, use that refine queue method for setting up your first prompt, 2 0 1. You're still gonna have to answer a question or 2 because that's the, you know, how we set up that refined queue. But try that out. It's gonna work fairly well. Alright.
Jordan Wilson [00:25:20]:
So let's get to the big question. Is it worth $200 a month? So let's talk about the pros and the cons. Well, the pros are high accuracy and reliability in complex domains, unique raisin unique reasoning capabilities, especially if you are in some of those more technical professions, software development, engineering, anything with math, research, data, science, STEM. Right? If you're there, yeah, probably is. Probably a no brainer. What about for everyone else? Because there's cons. $200, it's not cheap. Although if I'm being honest, I think we've been spoiled by these free and $20 a month world class state of the art models that essentially now have mini rag.
Jordan Wilson [00:26:05]:
Right? We've been spoiled. Right? Because the big companies, they know a lot of them are losing money. Right? Like OpenAI reportedly lost, I don't know, 4 or $5,000,000,000 in 2024 because they're not worried about making money. What we are getting, if you're a power user, you're getting way more than that $20 a month. Right? OpenAI CEO Sam Altman said even on this $200, pro plan, they're losing a ton of money is what he said. This is still relatively cheap, whether we're talking about the $20 a month or if you have a use case for it, I think even the $200 a month, pretty affordable. Right? All things considered, and we're gonna see an example of that. So ultimately, there's the pros and the cons.
Jordan Wilson [00:26:50]:
It's having a PhD level, companion that will think about things, give you better results, higher accuracy if you know how to direct it, but it is much slower. Right? So if you're used to just jabbing back and forth and that's how you like working with large language models and you don't see any problems right now with outputs, then it's not for you. But I think it's actually for more people than you think. I think people are literally just thinking, oh, oh, one. That's for, you know, engineers, data scientists, researchers, etcetera. I don't think so. So you also need to just ask yourself. Yeah.
Jordan Wilson [00:27:33]:
Do you need the advanced reasoning and professional tools? Are there use cases in your domain that are worth that premium price? So you really have to ask those questions. There's no blanket answer. I think if anyone asked me, you know, chat gpt free or chat gpt plus, it's easy. I don't care what you're doing. Chat gpt plus. $20. It's a steal. Right? I've always said all along, if chat gpt plus was $200, I would still pay for it.
Jordan Wilson [00:27:57]:
Right? Obviously, I have a chat gpt pro plan. Alright. So let's look live. Alright. And please keep getting your questions in. I'm just scrolling through the comments. So, thanks everyone, for, you know, getting getting your questions in. I'm gonna try to tackle them at the end.
Jordan Wilson [00:28:15]:
I'm just scrolling through all the comments looking for question marks. So, yeah, make sure if you do have a question, then then, you know, get it in. Doug was talking about our show yesterday. Great task write up. I think yesterday was the first day. I saw the link to your post on your website. Yeah. Alright.
Jordan Wilson [00:28:34]:
Let's get after it. Let's do some stuff live here. So bear with me y'all. Alright. Here's what we're gonna do. Live stream audience, as always, I never know if this works or if my audio is still coming through. Can you let me know? Can you all see my screen? And can you see, what's what's going on here? So I'm going to explain to you what I'm doing after I get this started. Okay.
Jordan Wilson [00:29:04]:
So I'm gonna be copying and pasting a bunch of information in here. Alright. Give me a second. There we go. Alright. So this is information that I've exported from my podcast stats. Right? I really wanna make sure I have okay. Good.
Jordan Wilson [00:29:26]:
Alright. Thanks y'all. Alright. Everyone says they can see. Thanks y'all. Thanks y'all. Okay. So I am in o one pro mode.
Jordan Wilson [00:29:35]:
Alright? I'm gonna tell you what these, I'm gonna read this to you, but I I'm gonna get it going first. Because like I said, this might take a couple of minutes. Alright. So here's my first kind of tip. Right? Provide a lot of context. I'm gonna walk you through the context that I provided, but also something that's changed recently. I I don't know when it was, probably a year ago. You can run concurrent chats now.
Jordan Wilson [00:30:00]:
Generative AI, even o one pro, it's generative. You can run the same prompt even when you give it a ton of information. You might get very different things. You might get similar things. So I'm gonna go ahead, even though it might slow it down, I'm gonna follow my best practices. Right? If I'm waiting, I'm just gonna wait. So I'm literally running the exact same prompt in another tab. Alright.
Jordan Wilson [00:30:23]:
So let's go ahead. Let's let's check on it here. Okay. And we're going to walk through. So sometimes it will give you details. Sometimes it'll tell you what it's doing under the hood. Right. And I know I have my my token counter here, but the context window is much different.
Jordan Wilson [00:30:40]:
I probably should have mentioned, the context window differences, because that's important. That's important as well. Right? So essentially, o one pro has a much, much larger, context window. So, let's let's go ahead and I'm gonna read now I'm gonna read now what I actually put in. Alright? I'm gonna try to go quick, but like I said, I exported, some recent podcast episodes. There's a ton of stats. This is an example, and I want you to think, what data or what large amounts of context do you have? Because this is I found myself when 01 came out. I'm like, okay.
Jordan Wilson [00:31:27]:
I may not be in STEM. I may not be in in data analysis, but I have access to a lot of data. And either I don't have time or when I am analyzing it, I'm really just looking for the low hanging fruit. And there's probably so much deeper and so many different more channels I could go in if I had time. Alright. So I'm gonna go fast here. Alright. So I'm saying these are my podcast stats.
Jordan Wilson [00:31:51]:
So remember when I said when you prompt o one, think of it like that coworker that wants all the information, and then they're gonna go in the corner. So I'm saying these are my podcast stats. Keep in mind, today's date is January 16, 2025. For all questions, always exclude the top 2% and bottom 2% of episodes unless otherwise noted. Right? I have a bunch of episodes, their downloads, some other stats, and sometimes there's anomalies. Right? Sometimes there's just problems, and I don't want those problems to be included. Alright. So already you're saying that could be a lot of manual work even if you're good with business intelligence, you're good in spreadsheets.
Jordan Wilson [00:32:27]:
Right? I'm saying also always give the episode number and name number 1. Keeping that in mind, please carefully answer and tell me. Question 1, give me the average downloads per episode. Question 2, give me the complete list of episodes with the new performance percentage over under of the adjusted average. So I'm asking it, find the adjusted average number of downloads. Take out the top 2%, take out the bottom 2%, then go give me for each one, I want how it compares versus the average. So let's just say the average was, I don't know, 4,000 downloads. Right? I wanna see the percentage once you take out the top 2, bottom 2.
Jordan Wilson [00:33:07]:
I wanna see each episode. Is it higher than that kind of median I I don't know. What's the math term? Right? Is it, higher or lower than that? Alright. Then I'm saying question 3. Give me the top 10 and bottom 10 episodes and their respective percentages that they're over or under the adjusted average. Alright. Question 4. For the top 10 from question 3 above, the adjusted average, please suggest 3 slightly adjusted episode titles for each if I were to rerun them.
Jordan Wilson [00:33:42]:
So every once in a while, I'd say maybe, you know, I don't know, depends. Anywhere from 1 to 5 times a month, I'll rerun an episode. Yes. Sometimes I get sick. Sometimes I can't be here y'all with y'all live, you know, at 7:30 every single day, although I try to. So I'm essentially saying for the 10 episodes that performed highest above the, adjusted average, suggest 3, additional, titles. But don't just, like, look at it and randomly suggest them. Look at the trends.
Jordan Wilson [00:34:13]:
Right? Look at find common themes. So here's where we're really working with structured and unstructured data. This is where it's great to work with a large language model with natural language processing. Right? So I'm like, yo, here's 100 of episodes. Go find the ones that are really good, and then those top percentage, you know, try to develop, you know, some way to see what's working and what's not, and then apply that to some of these top percentages. Alright. And then I'm saying, like, look, find common naming trends. Example, title lengths, title length, psychological marketing angles, superlatives, word choice, etcetera.
Jordan Wilson [00:34:49]:
Be exhausted in your pursuit of spotting common and hidden trends. Question 5. What are the most common patterns among underperforming episodes, and how can I avoid them in the future? Question 6, how does title length or structure correlate with episode performance? Break it down from every angle you can think of. Be pinpoint specific. Question 7, how does release day impact episode performance? Please exclude Mondays as that is usually our AI news that matters day, and we don't usually run other types of shows on those days for needed context. So even though the large language model should know, I'm saying, you you know, giving it, hey. This date was a Friday. So you can make sure you have it correct.
Jordan Wilson [00:35:29]:
Question 8. How does the release time or hour affect episode performance? Do not group them together. Go individual by hour. Be exhaustively precise and give me a chart that shows hourly performance. Right? Sometimes we get our episode out by 8:15. If I stop yapping, today's not gonna be that. It's already 8:0:6 AM. Sometimes something comes up, and we might not get it published until 11 AM.
Jordan Wilson [00:35:53]:
So I wanna know hourly, how does that impact it? Then I'm saying, here's here's one that would take a long time to figure out. I'm saying staying power and average decay. So, in this document in you know, and I pasted all of this in there. I didn't upload a spreadsheet. I just pasted it in there. Essentially, it was information from a CSV. But for 100 of podcast episodes, it gave 7 day downloads, 30 day downloads, 90 day downloads, all time downloads. So what I'm asking here is to essentially figure out staying power and average decay.
Jordan Wilson [00:36:25]:
So saying, hey. When do average like, across hundreds of episodes, when do they normally quote, unquote go stale? Right? When do they stop really, you know, getting listened to? Because people are searching for these all the time. It's not just people, like, hopefully, you subscribe and thank you if you do. Right? Everyone else is searching for podcasts and they're discovering. So I'm trying to see which ones have staying power, which ones are more evergreen. And then I'm asking it, to show me the top ones because then I can develop new episodes based off that. Question 10, how do episodes featuring specific brands or keywords? Example, OpenAI, Chat GPT, Google, large language model, AI, Claude, compare in performance. Question 11, Please also categorize all of these episodes according to what you can gather from the titles.
Jordan Wilson [00:37:12]:
Example, marketing, chat gbt, enterprise, AI use cases, etcetera. Only put 1 episode in a category and try to create at least 20 different categories. In doing so, please also give me the average, the category performance versus the averages that we identified earlier. Right? A lot of this, I wanna see what sticks. What do y'all like? What do listeners actually care about? Right? And I think after you have, you know, 1,000 tens of thousands of pieces of data points, yes, I can go figure out some of these things with some simple calculations in, you know, Microsoft Excel, Google Sheets, etcetera. But this is where we're really combining a lot of data, but with also unstructured. This is bringing in unstructured data, right, structured data. So structured data are are numbers, things that you can plot on a graph.
Jordan Wilson [00:38:01]:
Unstructured data is is words. Right? You can't necessarily plot them. So we're combining structured data, unstructured data with a reasoning model. Right? And I gave it a ton of information. Alright. And then I'm saying essentially, you know, I'm giving it some additional encouragement, like how to format it, all that stuff. And then I'm also saying at the end, give me essentially a quick summary. And then here's all the data.
Jordan Wilson [00:38:26]:
So I pasted I pasted in about, 13 pages of those questions and data. Alright. That was a lot. Marie said, how long did it take you to come up with these amazing with these detailed questions? Amazing. I'm a fast typer. I think all the time. Probably took me, I don't know, 12 12, 13 minutes to type all these up. So, yeah, there's there's no AI in helping me formulate these questions.
Jordan Wilson [00:38:58]:
We always talk about human in the loop. Right? What role do do do do do humans have? And one of the things that AI, and I think especially these reasoning models like o like o one, o one pro, it allows you to really let your expertise shine. Right? One of my expertise, I think, I'm background I have a background in journalism. I have a background in in marketing and advertising, and I don't know. Maybe you saw some of that in play there. Right? This is how my brain works. I'm like, y'all, we got so much data. I need to be able to to to identify trends and to build something better to help you all.
Jordan Wilson [00:39:31]:
Right? Alright. So let's now jump back in and see how our chats are doing. So you'll see it's been oh, live stream on us. Can you still hear me? I got I got something that said can't hear, but let me know if you can. I got a something, something on my screen just said, I lost audio. So we'll see. Alright. So here is our details.
Jordan Wilson [00:40:03]:
Alright. Thank you, Sam Sarah from YouTube. Alright. So I can click details. So essentially, you can see kind of slash sometimes how the o one model is actually thinking about this. Alright. It's weird. I've done, you know, similar prompts like this, and I'll always do AB testing.
Jordan Wilson [00:40:22]:
Right? I'll run the same prompts on 01 pro twice. I'll run, you know, the, prompt on 01 pro versus 01 normal. I'll run the same prompt on 01 pro, 01. Right? I do a lot of testing. And even on 01 pro, sometimes it'll give you all of the details, sometimes it won't. And then it says, oh, sometimes 01 does better when it doesn't share the details with you. So is there ultimate transparency? Not really. But I'd I'd say more times than not, you do kind of get to click that details, and you can see kind of what's happening under the hood.
Jordan Wilson [00:40:54]:
So in my other one, it looks like looks like I timed out in my other one. Bummer. So maybe I should have been doing 2 at once, because this one fought for about, about 9 minutes, and then it said, oh, I'm done for. Alright. But luckily alright. Luckily, we finished. We finished over here in our first chat. So I'm gonna go ahead, why not, and regenerate the other one.
Jordan Wilson [00:41:21]:
That one thought for about 10 minutes, ran out of steam. So this one let's see if I can see exactly how long this, this one thought. Let me go up. Lot of information here, y'all. Lot of information. My gosh. Alright. This one thought for 11 minutes and 22 seconds.
Jordan Wilson [00:41:42]:
So I think my I think my record is maybe, like, 5th 15 or 18 minutes. I give it a lot. I give it a lot. Alright. So I'm not gonna read all of these 1 by 1 because it's gonna take a while, and I don't want this to go, to to go too long. But let's just see very quick overview how well it did. So it's saying below is a comprehensive step by step response that follows all of your instructions precisely. You know, I've counted all the episodes.
Jordan Wilson [00:42:09]:
So it's telling me what I've what it did. So it took out the top 2% and bottom 2%, which in this case was 6 total episodes, computed and listed everything after removing those 6. Right? So pretty good. It it kind of first gave me, an overview of how it did it. Then it gave me the preliminary steps. So it went through, identified the total episode count, identified the top 2 and bottom 2 percentages. Right. Listed them all.
Jordan Wilson [00:42:37]:
That's good. And then it kind of said, hey. Then there's a 122. I didn't give this all 400 of our episodes because I knew I did testing and it worked fine, but it took way too long and it timed out too much. So I only uploaded, like, the last, probably 6 months of episodes. Okay. So here we go. Question 1.
Jordan Wilson [00:42:58]:
So now it's getting I told it to label it. So here we go. Question 1. Average downloads per episode. So here you did. It did some, little bit of math, so thank you for that. I don't like math. Alright.
Jordan Wilson [00:43:09]:
And then it says answer to question 1. Oh, I was I was about right. About 4,000 downloads per episode. The downloads are weird. Download streams. Everyone looks at it differently. So, yeah, I think we're almost at, like, 2,000,000 downloads. So thank you all for listening.
Jordan Wilson [00:43:24]:
Alright. So question 2, list of all remaining episodes versus adjusted average. This is what I wanted. Alright. So here, it says, below is the performance calculation for each of the 122 remaining episodes. Is is there anyone smart in math? I don't even know what this means. I don't even know how to read it. I don't know.
Jordan Wilson [00:43:43]:
It created some kind of, formula. Sometimes I ask chat gbt to create algorithms. I just give it a bunch of data. I'm like, create new algorithms for me and tell me things that, you know, I can't find out in a spreadsheet. That's fun to do. I didn't do that here. So there's a a calculation. So let's see if it gave me the full table.
Jordan Wilson [00:44:02]:
Sometimes it does. Sometimes it doesn't. Right? So full table. Okay. Here we go. There we go. So, it looks like we have all of our episodes here, listed by episode number. It gave me the all time downloads.
Jordan Wilson [00:44:19]:
It did the performance. So I can see this one right here was, about 0.6% below average. Right? So I can go through here. I could ask it or I could copy and paste this, and give it to as an example, like o one mini or GPT 4 o and have it turn it into an actual spreadsheet. One thing I I I realized o one pro isn't great at is creating documents. I don't even know if technically has that, you know, that capability or or functionality, but the advanced data analysis mode inside GPT 4 0 is great. GPT 4 0 is great at creating different types of documents. So if I wanted to, I could copy and paste this, but let's see.
Jordan Wilson [00:44:57]:
Yep. There we go. It's giving me, you know, 28% above adjusted average, 18% below, 3% below, 15% below. Right? So this is great. Oh, let's see. It said, it looks like it might have truncated. I had a feeling that even the o one model was was not going to complete this in its entirety, because it says and so on. So it didn't do all 160.
Jordan Wilson [00:45:22]:
It says due to the length of this list, to fully comply with your request, this table would extend for well over a 100 lines. I have demonstrated the exact calculation method and the format above. The same format applies for every single remaining episode. Below, I continue the listing in concise bullet form. Each line follows the same pattern. Episode number, and title all time, then the resulting percentage. Okay. So it did actually go through and do it.
Jordan Wilson [00:45:48]:
It just didn't show me the math for each one, which is fine. I didn't need that. Alright. Here we go. Question 3. Top 10 and bottom 10 episodes and the percentage over. This is what I wanted to know. So here's the top 10 overs with their adjusted average over.
Jordan Wilson [00:46:06]:
So when will we achieve AGI that perform well? AI agents, everything you need to know. Top AI tools and features of 2024. How AI agents can bridge the gap to the future of enterprise work. Google's a, Google's $1,000,000,000,000 AI mistake. Right? So there we go. This is good. I I I mean, again, I could have sorted this by downloads, and I could have found out some of this, but I wanted to see how much higher because there's some anomaly episodes where I'm like, okay. Were these actually, you know, is this a bug? Sometimes, you know, as an example, Apple Podcasts or Spotify Podcasts will, you know, feature an episode, you know, if their algorithm says it's good, and it'll put it on, like, a top episodes and technology page.
Jordan Wilson [00:46:50]:
So I know sometimes some of our episodes get way many more downloads, but I'm like, I don't really want those. I wanna just focus on on the guts there. So it did a pretty good job. Bottom 10 episodes. There we go. Slightly adjusted title names for each of the top 10. There we go. It's giving me all of those.
Jordan Wilson [00:47:09]:
Yep. For each of them, it's giving me adjusted episode titles and also why. Right? That's interesting. I didn't even say why, but it gave me for each of the 10. It gave me some some other episodes. Question 5, common patterns among underperforming episodes and how to avoid them. So it says titles that are too generic or vague, overly long titles without a clear hook, insufficient mention of strong keywords. And in each of these, it's giving me very specific examples.
Jordan Wilson [00:47:38]:
Right? It's not just giving me these general guidelines. It's telling me how to avoid it. Then, question 6, how does title length or structure correlate with episode performance? It's all good. Question 7, how does release date impact episode performance? Let's see. Tuesdays show moderate to good performance. Wednesdays, Thursday, engagement list because listeners have midweek energy. Fridays, it says, can be hit or miss. Alright.
Jordan Wilson [00:48:04]:
So maybe I shouldn't schedule big shows for Friday since they can be hit or miss. Yeah. Sometimes people check out. Let's see. I I did specifically ask for a table for time or hour. So for question 8, let's see here. Good. It did it.
Jordan Wilson [00:48:19]:
So it gave me the release hour and then the average all time download. So I can see yeah. Apparently, sometimes I release some late. That's weird. Some of those might have been bugs. I should have asked in this one to also give me the total number, of episodes that have been published, in that release hour because, yeah, sometimes there's bugs with our, you know, our host. We use Buzzsprout. Yeah.
Jordan Wilson [00:48:41]:
Sometimes there's just weird anomalies. So I should have asked for the number of episodes, but it looks like for the most part, it looks like maybe our sweet spot is when it gets published by 9 AM. So maybe, you know, when we do publish it super early, maybe it misses people. Maybe people are are, you know, listening on their commute to work. But it looks like for whatever reason, it looks like that sweet spot is epicing sorry. Releasing the episode by 9 AM. This is all our local time. Alright.
Jordan Wilson [00:49:10]:
Question 9. Staying power in average decay. This is one I was really looking forward to. So I'll go through and read this if if you're interested, you know, you can you can let me know. But it did good. It it gave me kind of the average 7 30, 90 in total. It identified, certain episodes that extended past that. You know, some of the agent episodes.
Jordan Wilson [00:49:36]:
Alright. So did did a pretty good job. I wish it would have given me a little more depth on this. But again, what I would do in theory, I would look at the responses and I would update that prompt that I did, and I would just run it again. Right? Because I can see some of these things. I'm like, I forgot a little bit here. I should probably go back and add add some. Question 10, episodes featuring specific brands or keywords.
Jordan Wilson [00:49:57]:
So there we go. You know, obviously, OpenAI, Chat GPT typically see an average of about plus 10 to 30% higher than your overall adjusted average. Google, Gemini, or Claude are about 5 to 15% higher. Large language models said it doesn't really show, you you know, any strong difference. Okay. That's pretty good. And then here's the one that would have taken me for forever. Right? 100 of episode titles and then to categorize them and then compare against averages.
Jordan Wilson [00:50:27]:
So it went through and it gave me, it looks like a list of 20 different categories. It didn't give me the category average download. Alright. But it did break everything down by category. Alright. And then we have our answer guide here, which I said at the end, just give me very straightforward bullet point answers. So, how how did this do? What do you all think? I know this took a while. Do y'all think, o one pro, was that worth $200? Because I'm trying to think.
Jordan Wilson [00:51:01]:
If I went through as a human and did this myself. Right? If someone gave me the exact same questions, I don't know, it probably would have taken me 3 or 4 days. Right? I think I probably could have done a little better because I probably would have done a better job inferring certain things. In certain instances, you saw even o one pro truncated responses or didn't give me full things. Right? That's frustrating. So what I probably would have done in the future probably would have broke broken this up. Right? I gave it 11 extremely difficult tasks. And if I was using GPT 4 0 as an example, I would have done each of those as dedicated chats or, you know, taking them, tackling them 1 by 1, and going back and forth with chat gpt at least probably 3 to 10 times on each of those 11 questions.
Jordan Wilson [00:52:04]:
Right? So, from a time savings perspective, I think absolutely. Would it be me? Maybe not. Although on the math and some of those more complex things, absolutely. I wouldn't have known, you know, especially without AI. You put me in a on a computer with with, you know, I can't use AI, you know, in just a spreadsheet only. I don't know if I could have gotten these answers. Right? And I'm decent decent at basic math. Right? I have an analytical brain.
Jordan Wilson [00:52:37]:
Obviously, me knowing this is everyday AI, I I I run it. Right? But if someone else came to me with this same data and said, you can't use a large language model, or they said, you can just use GPT 4 o. I think if I would have to do it by myself, it would have been at least 3 or 4 days. If I would have used GPT 4 o, it probably would have been, I don't know. I'm guessing 3:3 to 5 hours, because it would have required a lot of back and forth for each of those 11 questions. You have to worry about context window, you you know, to get that kind of quote, unquote chain of thought reasoning. You, the human, have to be the one pushing that chain of thought button. Right? You have to be the one giving you examples, going back and forth, steering and guiding it, whereas, you know, o one is more of like that full self driving car, kinda guides itself.
Jordan Wilson [00:53:26]:
Right? With when the GPT family of models, you have to do that. So if I'm being honest, so we got this done in 10 minutes with o one. It probably would have taken me 3 or 4 hours with GPT 4 o, and it probably would have taken me a couple of days if I just had, you know, the Internet and spreadsheet and no AI. So is it worth it? I don't know. I don't know. For me, this isn't perfect, but what I would have done is I would have went back through those responses. I would have updated my prompts, and I probably would have obviously broke this down into, you know, 3 or 4 questions. It was too much for it to handle.
Jordan Wilson [00:54:05]:
Even though it was within the, context window, you you know, I didn't kind of, you you know, put in too much context. It was a little too much thinking. Right? Or, you know, there's probably something in OpenAI's training that's like, hey, when someone asks for, you know, hundreds of things, you know, and if and if it's part of of multiple other queries, just, you know, showcase your ability to understand. Right? So I could have that that one where it kind of cut things short. If I just would have done just that one, question and given it to o one, it probably could have done it. But I gave it 11 fairly difficult, questions that required a ton of response. So I do think this isn't a capabilities thing. This is more of a compute and training.
Jordan Wilson [00:54:52]:
O one pro probably could have done this, right in its entirety, but I'm sure there's some things that OpenAI has worked in there to say, hey, you know, at a certain point, if there's, you know, this many questions and all the questions are multi multi step, maybe you have to truncate. I don't know. Alright. There were a couple of questions. Let me see if I can get to them very quickly, because I made you wait to the very end. I'm just scrolling through. If I see a question mark, I'm starring it. Alright.
Jordan Wilson [00:55:26]:
Let's see. Dennis, if you have teams, can you upgrade a single user to pro? No. As far as I know. I'll ask my contacts at OpenAI. I did ask them about this like 3 weeks ago because I have free plus Teams enterprise and pro accounts. I don't have the option to upgrade anything on Teams. So as far as I know right now, the $200 pro, which gives you a one pro, is only available for individual users. Actually the last time I checked on that was like like a week or 2 ago, so I should go back and double check.
Jordan Wilson [00:56:00]:
But previously, there was no option to upgrade teams, and I'm not sure about enterprise accounts because any enterprise account I'm on, I'm not the kind of admin of that, but, you know, I'm an individual enterprise user. So, So, yeah, people don't know that. Someone DM me on LinkedIn. They're like, oh, you do trainings? I'm like, yeah, that's what we do. So if if your team, whether you're on, you know, Chat GPT teams or Chat GPT enterprise or CoPilot, right, that's what we do. We train people. I talk about AI every day and, you know, if your company, if your department needs help, you know, you can call us in. Alright.
Jordan Wilson [00:56:31]:
Let's see. I think Michael might have been asking this to someone else, but, you know, asking about GitHub Copilot. Yeah. There's there's other, you know, Cursor, GitHub Copilot, You know, there's other platforms that do great for some of these things, you know, database, coding, software engineering. Yeah. I think Cursor, Microsoft GitHub Copilot, great. Kieran says, isn't time taking to respond to Khan? Absolutely. Right? But that's why I'm generally I'm not just giving an 11 minute task to chat gbt and then, you know, sitting there sipping my espresso and, you know, judging it.
Jordan Wilson [00:57:10]:
I'm doing other work. Right? I'm opening another window, another account, you know, putting something similar in in Claude or Gemini AI Studio. Right? I'm always running things in parallel, especially when I, you know, go through that time to put some to put this content together. Obviously, I would have to break it down into smaller chunks for non reasoning models, but, yeah, Kieran, absolutely, it's a waste not not a waste of time, but the time it takes is a con. Right? Especially in, like, we're in this society where we want everything now. I I don't wanna wait 11 minutes, but I waited 11 minutes, and it did, like I said, probably work that would have taken me either multiple hours with GPT 4 0 or days without any AI. So is the time worth it? Patience is a virtue, and doing things right, pays off in the age of, you you know, this instant gratification. Right? Because now if I wanted to, I would go back.
Jordan Wilson [00:58:03]:
Like I said, I would improve, that info that I gave, o one. I would probably break it down into, you know, 2 or 3, and I'm sure it would go I don't know. If I had to grade it, I would give it a 85%. If I broke it down, improved, improved how I asked the information, this was user error. That's user error. Right? I gave it too much information, although I think for that part, it should have been able to handle it. But for a lot of the other things, I'm like, oh, I should have worded that differently. Right? I didn't do a good enough job.
Jordan Wilson [00:58:31]:
People always think an output, that means, oh, l l, like, Chad GPT sucks. It's dumb. No. In in that case, I was dumb. I didn't do a good enough job. Some of my communication was not precise enough, but sometimes you only know that by going back and forth. And I love being able to look at the details and seeing how ChatGPT that's a cheat code if you are using the o one even on the, ChatGPT Plus plan. Look at how it's reasoning.
Jordan Wilson [00:58:56]:
Look at those details. That's going to improve how you communicate with a large language model because if it's struggling with something, you know. If if if it's halfway through the process or in the first 10, 20% of the process, if it's already tripped up, guess what? Then it's going to get even worse. So you might need to move some of the information from the bottom up top, you know, provide a better summary, you know, give it, you you know, more clear role, priority, goals, all that stuff. Right? So, yes, it is a con. Marie, does it go down any rabbit holes with its chain of thoughts reasoning? It depends on how open ended, your, input is. With an open ended, yeah. Absolutely.
Jordan Wilson [00:59:36]:
Right? Sometimes for for fun, I say, you know, solve the world's problems. You know? Solve hunger. Solve, you know, violence. Right? Like, solve inner city or whatever. Right? Solve this big problem. Right? And then I like to see it think, and, you know, I think that has more to do with the training of the model than the model's capabilities. But, yeah, it can go down rabbit holes if you give it the opportunity. In this case, there is no rabbit holes, because it was pretty well refined, and and defined.
Jordan Wilson [01:00:07]:
Right? Then, Juliet said, sorry. I'm not up to date on the lingo. I have the $20 paid subscription to chat gbt. Is that pro? I think someone already answered that, but no. $20 chat gbt plus, you get the, the general o one model. It is very limited in terms of the amount of work that you can do with it. If you want the o one pro, you have to be on the Chat GPT Pro account, which is $200 a month. Fred, do you compare different models all the time? Yeah.
Jordan Wilson [01:00:36]:
I think I probably answer that later in. All the time I compare different models. So I like using, you know, the AI, arena chat, whatever it's called, limarena.ai. Write the AI chatbot arena to do that. I've I've shared some videos on how I, I use a tool called chat hub a lot where you can put one prompt in. It'll give you up to 8 different large language models. So, yeah, I I compare model responses all the time. ADA, can it access your website and do the analysis from your website? So the o one series of models, at least right now, do not have access to the Internet.
Jordan Wilson [01:01:14]:
They also do not have access to the full suite of tools. And that's a good way to end this, Ada, because I'm gonna say this. I have a prediction coming next week in one of my shows on the future of the o one models, and what that means for not just AgenTic AG, AgenTic AI, but what it means for AGI. Because I do think once you start giving a model like this that can reason, once you start giving it tools, once you start giving it, agency to make decisions on what tools to use, how to go about solving a problem, is right now what 01 Pro can do, it's kind of in a box. And I get what OpenAI is doing there. They're doing it for safety. Right? This is the first widely available reasoning model, and it could go off the rails. Right? And you can't jailbreak models like this.
Jordan Wilson [01:02:06]:
So I get that they're not giving it tools right now. You know, they're working on artificial general intelligence. They have their sights set on artificial superintelligence. So, I get why they're keeping it in the box right now, but as soon as this 01 Pro model gets a little better, this is the first first version of it. Right? The first version's always the worst. It's it's only been out for a couple of weeks. In a couple of months after they've updated this once or twice, and when and if it does get tools, if it gets agentic capabilities. You know, we're talking about OpenAI's operator when that's coming out, the new tasks.
Jordan Wilson [01:02:39]:
It's an extremely exciting time to be on the cutting edge of AI, and that's what you're doing here. So thank you for joining me. I hope this is helpful. I know this was a longer show, but there you go. I'll well, let me answer this. Is it worth $200 a month? I'm gonna go ahead. OpenAI is not paying me. I'm gonna say yes.
Jordan Wilson [01:03:02]:
I'm gonna say anyone that has access to data. So I'm not saying that you need data for your job or that you have a job in data. If you have access to data, if you are a decision maker, right, if you are a knowledge worker, I'd say it's a 100% worth it. You just saw my use case. Right? I can make it better, but the the value that I get from there, what I just saw in there, that's gonna help me grow my my podcast. Right? That's gonna help me bring in, you know, other great sponsors like Microsoft. Right? Microsoft is one of the sponsors of this podcast. That's gonna help me reach more people who want to learn AI, because these are all insights that would take me so much longer.
Jordan Wilson [01:03:50]:
Right? If you have data and you need to make decisions and you understand the basics of the o one reasoning model, 100% worth it. It's a hot take. People are gonna disagree with me, but I think it is. People are gonna say, oh, GPT 4 o is enough. Try doing something similar with GPT 4 o. Time is money, y'all. Can it accomplish the same things that I just showed you in o one? Yes. But like I said, it probably would have taken me 3 to 4 hours.
Jordan Wilson [01:04:19]:
And even during that time, I couldn't have done anything else. Right? During the 11 minutes, I had to wait. I didn't have to do anything. Right? Yeah. I need to improve it and go back and iterate. But I think if you have data to work with, if you have to make decisions, and if you learn the basics, it's a 100% worth it, even at that steep price tag. Alright. I hope this is helpful.
Jordan Wilson [01:04:43]:
Make sure to join us Friday or sorry, Monday, January 20th all week, 5 episodes, our first series we've ever done. You need to listen in. You need to pay attention. Thank you for tuning in. If this was helpful, please let us know. If you're listening on the podcast, sorry this was a long one. You can listen to me on 2x. I'm not gonna be mad.
Jordan Wilson [01:05:04]:
I would too. Alright. But please leave us a rating. Follow the show on Spotify or Apple Podcasts, wherever you get your podcasts. If this was helpful, you're listening on LinkedIn, please share, repost with your friends, someone who needs it. Thank you for tuning in. Go to our website, your everydayai.com. So I'll see you back tomorrow and everyday for more everyday AI.
Jordan Wilson [01:05:22]:
Thanks, y'all.