EP 275: Be prepared to ChatGPT your competition before they ChatGPT you

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If you're not using AI to get ahead, there's a good chance that your competitors are. It's something that I say a lot here on the everyday AI show that you know, hey, it's no longer 2023. There's no longer time to be experimenting. If you're not implementing generative AI in 2024, you are asking for your competition to pass you. Or maybe you're asking for your competition to chat GPT you right? So I'm extremely excited for today's conversation where we're talking to and we're saying literally this to be prepared to chat GPT your competition before your competition. ChatGPT is you so extremely excited for today's show. Before we bring on today's guests, just as a reminder, as always, if you're listening, whether you're in the car, on the treadmill, walking your dog, wherever you are, make sure afterwards or right now to go to your everydayai.com Sign up for our free daily newsletter. I already know today's conversation is going to be full of so much insightful information where you're going to want to be taking notes. Don't worry, we got that. It's going to be in the newsletter that we're recapping. And hey, as a reminder, I'll still be in the comments live answering any questions you have. Today's you know, we're debuting this show live but it's technically pre recorded. So with that, I hope you're excited. I am. And please help me welcome. Let's bring on our guests to today for today. There we go. We have Barack Tarofsky, who is the VP of AI at Cisco. Barack, thank you so much for joining the Everyday AI Show.

Hi Jordan. It's a pleasure to be here. I'm very excited.

All right. So you know, I'm sure mostly everyone in the world knows, knows Cisco. Cisco, you know, a Fortune, Fortune 100 company, one of the biggest, you know, companies in kind of the tech and cyber security space and technology in general. Right. But Brock, tell us a little bit about what you do in your role of VP of AI at Cisco.

Yeah, I will want to start a little bit about my background and talk about how I got into that space. So I kind of have this funny way to introduce myself that I work on, quote unquote, those esoteric things like AI and large language models Long before they became the hottest thing on earth. On a more serious note, my to large extent claim to fame was that I was working or leading product. That was the first product that productized LLMs at scale. It was called Google Translate. I spent 10 years at Google leading the Languages AI product team and 2015, 2016 we basically did multiple technological breakthroughs across software side and hardware side to basically to be able to run what's called deep neural networks on a huge corpus of data which is a prerequisite for what we now call LLMs. Then then it transformed, actually transformed to a transformer research paper that in many cases as you know it's a basically a baseline for ChatGPT from everything else. Ironically enough most of the researchers that work with us on translation on some shape or form, if you read those papers and mostly talk about translation as a use case because obviously that was a use case that at that time was more successful exciting. Then I worked on productizing. The first productization of transformer technology was called Bert Bidirectional Transformer bidirectional embeddings based on Transformers also done at Google. We used it initially for Google search and that created a huge jump in search quality and understanding Google user intent or queries. And finally worked on products like Google Ads where we added multibillion dollar lift in Google revenue based on better AI, best targeting Google cloud, et cetera. And now in my current in, in addition to that in between Google and Cisco, I led product engineering and AI teams as chief product and technology officer at a large late stage startup focused on computer vision AI. And now I'm at Cisco working on applying cutting edge technology to networking domain at Cisco. Wow.

So you know, maybe Brock won't say this outright but if you, if any of that went over your head just know he is one of I, I'd say a leading expert. Right. So more than 25 years in the artificial artificial intelligence and in, in related fields and you know, working on, on AI teams at Google. You know, with that Brock, I'm curious you know, because yes, a lot of people maybe don't understand. Yeah, AI has been around for many decades, right. It's not new but what is new is kind of, you know what you talked about is now these transformers and technology and in large language models, you know, now being available and accessible to everyone. So I'm curious from, from your you know, vantage point especially when we're you know, out there talking maybe about or if we're talking to business leaders and when you know, can you talk A little bit about how impactful large language models are even, you know, in, in the course of someone like yourself who has decades of experience in the space.

Yeah, as I mentioned, I consider myself extremely lucky because we're technically in the second wave of AI, or maybe you can even call it second hype of AI because the first wave of AI, as I mentioned was 2015, 2016, when Google or Google Translate showed that you can actually run deep neural networks. Now what we call large language models on a huge source of data, we actually believe that will be such a revolution. I actually encourage people to read an article called Great AI Awakening from New York Times Magazine that you can probably even find in podcast Description. It's a really good article with the history of AI, but I think that hype was pretty short lived because very quickly a lot of enterprises understood it's pretty expensive and actually need an amazing amount of talent to do it, I think. And that's why a lot of the innovation was limited to companies like Google, Microsoft, meta, et cetera, because they need a concentration of talent, compute, et cetera. What I believe ChatGPT did this very clever UI and also building a lot of amazing technology technologies that Google and others developed and they democratized access to it, right? And now a lot of people suddenly discovered the beauty of LLMs. One thing I would caution everyone is that it's democratized access to try it, but productizing it at scale and still it's, it's way easier than before. It's not a rocket science at all like a nuclear bomb development as it was before, but it's still pretty complex. And it's complex because almost on every product you have a, in technology have this Pareto principle, you spend 80% of the effort on 20% of functionality that actually makes the product work well. So but as I said, a lot of use cases, this technology makes a lot of use cases way better than it was before.

And speaking of use cases, I'm excited to dive into that here and we're going to be talking about that a little bit. But first I do want to set the stage a little bit for even the premise of this episode or even the title. Right. So I started off the show Barack by saying, Hey, 2024, if you're not implementing generative AI and if your business isn't using large language models already, you might be in for an uphill battle. What are your thoughts on that before we dive into use cases and this concept of you got to chat GPT your competition before they chat gbtu yeah.

So we'll talk about the use case. Obviously not every use case will be immediately available or served by LLMs, but there are many use cases and many areas where it will be pretty technology is mature enough that could be productized well. So if you take it, if we start on like a generic statement that any business, sizable business that has sizable number of customers, I believe will have must connect their internal knowledge sources, databases and internal communication channels across emails and chats and speech to be served through large language models. If they do it, I think they will see pretty significant increase both in customer satisfaction and in productivity. But if they don't, I believe within three to five years that will be in a very increased risk to be disrupted by their existing competition on newcomers that will be able to offer way better level of customer interactions at a fraction of the cost. And I think it's something that needs to be taken very seriously.

Yeah, and you know, kind of beside the point but kind of related is, you know, at least my take on this. I think that you know, especially small, medium sized business owners don't fully understand generative AI and don't fully understand large language models. I tell people, you know, kind of the lines between large language models and traditional Internet search seem to be blurring. Right. So when you talk about Google's sge, you know, and bringing this know more of a large language model type search into Google, you know, perplexity. You know, we just, we, we just saw a new model update GPT4O from, from OpenAI today. Should business owners even start to think of the two kind of the same? Right. Just like you wouldn't, you know, put together a big presentation without using the Internet. Should they be putting together a big presentation or a big sales deck without using a large language model?

Yeah, it's obviously use case dependent. I would argue for some simple use cases it might be important but might be less important. But definitely when you look at scalable business, the sizable number of customer interactions, I think then where it becomes way more important. Again as I said on a local level, on the medium to small businesses, it also depends on your competition. But yes, even on a small level, if your competition is starting to use it, if they, if we create a small chatbot that shows, you know, in interactive way what products and services you offer, that's by itself could be very powerful and start taking away share of your business.

Yeah, absolutely, absolutely. So let's, let's go ahead and jump into some of these use cases here. So for our, for our live stream audience this is, this is probably going to be pretty, pretty straightforward, but for our podcast audience we essentially have. And we're going to put this. So check the show notes for a link where you can go take a look at this. But we, we have kind of this, this four quadrants here in different use cases for AI. So everything from, you know, low accuracy and low fluency on the lower left hand corner cascading up into the right to high fluency and high accuracy. So, you know, Barack, walk us through a little bit about this kind of graph that we have here. And I'm also curious where, where did the idea for this come from? Because I love it. It's to visualize, you know, use cases for generative AI.

Yeah, it was actually very funny. The idea came from the fact that when ChatGPT launched, I was on vacation in Hawaii and then there was, I don't know if you remember, but was a classical market panic or market excitement that Google is finally disrupted and Microsoft will take all the share because they now have an open AI. So literally every investor in the world wanted to talk to me to understand because I had no clue what it is and is it true or no? It should be short Google now what should we do? And I will basically explaining to people that actually I believe search is not the best use case to start these LLMs. And people listen to me and say, oh wow. And I basically was explaining to them the biggest misconception is actually the fluency between fluency and accuracy. And people like, oh wow. All those investors told me you just totally publish it because it's so helpful. And I was actually very proud that within three months as people looked and said, oh, Google share is not really moving and Microsoft is not disrupting. And don't get me wrong, Microsoft will do a lot of money on other places like cloud, but definitely they're not disrupting search. And that's probably partly because it's like the framework. So I basically came with a framework that a lot of people find very helpful and I wanted to dispel some of the misconception because I believe the biggest misconception and this overexcitement about LLMs is that people, especially people who are not in the industry, like for 10 years, like myself, they suddenly discover that machine can produce content that is in many cases better than average human produces. You're talking about what I call fluency, the polishness, the confidence of ChatGPT. Answering questions are pretty amazing. And it's amazing technological advancement that is built on top of clever ui plus all this transformer technology that was developed by Google and others. But I think what people tend to forget that high fluency creating a very compelling story polish charismatic story doesn't mean being providing correct or accurate information. It's a very different dimension. It could be extremely, extremely polished, extremely confident and still say complete crap. And my example, my example of, you know, closest human analogy to this behavior LLMs at the end of the day answers the question or guesses next word or next sentence, right? So think about a person who can confidently and in a very polished and charismatic manner talk literally about any topic in the world, but they're prevailed to give you an answer. If you ask them a question they don't know, they will never say I don't know. They will make up stuff on the spot. And because they're so good at it, their delivery is so polished you will actually believe in what they say. And it's very dangerous. Human analogy could be con artist who does it on purpose to defraud you. And that's why con artists are so successful because in many cases their fluency is amazing. It's a delivery especially in topic you don't understand. Or it could be very successful entrepreneur. Good example is Steve Jobs who basically, you know, has a had a reality distortion field and sold things that others didn't. There might be less good examples, I don't know like FTX where you speak and you really believe in what you do, but it's actually not necessarily true, right? So all those examples at the end of the day the results are not necessarily great. That's why it's really important when you think about the use cases to look at this over two dimensions and not one and not be only influenced by the fluency. And finally I believe another thing that I represent by colors in my this two by two grid is also be very realistic. What is the consequences or what is the risk of getting the answer wrong? Because if it's a low stake use cases, it's probably okay ish And I will give an example. For example, in those who can see it or can after in the notes look at my framework. In the low accuracy high fluency quadrant we have bunch of green bubbles which I call low stake use cases that are roughly creativity or productivity use case like writing science fiction book, writing children's book, composing music. The beauty of it. There is no objectively right or wrong answer here. It's all about the story. That's a perfect use case where like fluency is so important. If you look on the Other side on the left in the low, low, low fluency, high accuracy, high accuracy, high fluency use case you have bunch of red bubbles. Those are the use case that roughly let's call them Google search plus. It thinks like, hey, what is the earnings call? What is the earnings growth of Google last 10 years? You just need an answer, no story, right? Or I want to buy a dishwasher, tell me which one to buy. But why like give me a recommendation or I'm going to pay this and I want to get a recommendation for a hotel. You do need a story, you need an explanation. But if a story will come with an accurate data that doesn't help, the product is useless, right? And then in between you actually have very interesting use case that I call them productivity enhancement use case. Those are use case like writing business memo, email or like creating a business presentation or writing a review. The beauty of it, it's not that it's not important, it's important use case. The beauty of it, you start with a draft and draft you need both a story and pretty high accuracy. And the beauty of it if it's a draft is that unless the people are really dumb, they will not send it automatically or not publish automatically, they will use it as a draft and the division of work between machine and human would be machine. Please give me a good story because many of us, Jordan, you're probably an exception, you probably good in stories but most of people have really good mastery of their facts but they're struggling to create a story. It takes effort and time. If you help them create a good story from the fact it's a huge productivity boost, huge value creation for a lot of people. And if you create the UI right and explain to them, hey, it might be wrong, you need to, it might be inaccurate. Make sure you double check and adjust if needed. That's to me very compelling product to start this I would not start in use cases where you actually need high stakes. If you make a mistake, it's actually bad, right? And also use case like search because we are talking about enormous scale here, right? You're creating millions of presentation, billions of search queries in search. You cannot really put a person behind every query to validate it. And if you ask users to validate it, that's effectively what search does today. So I would recommend people to start with use case that are much more grounded in value creation of creating a good story versus Let get me the one answer right from the beginning. So that's basically the gist of this. What is more Important for the use case. The more the fluency is important, the more the story is important, the better it's a fit. The use case is a fit for LLMs, at least in the short or medium term.

Yeah. And this is again, this is one of those. If you're on the podcast, we always appreciate your support but you got to come watch the video of this or you know, make sure to check out the graphic that we'll include in the show notes because I really think that does really just help better understand the framework for using generative AI.

Right.

And know when it's just okay, is this just maybe, you know, high fluency because Brock, it just. Just because something is highly fluent. Right. Just because a large language model can can spit out a bunch of content doesn't necessarily mean that that might be the best use case. Just because it is a use case. So speaking of use cases, let's go ahead and talk here. So now we have some, some more illustrations here on the same graphic but talking about different actual use cases. So walk us through here about some of these, you know, specific use cases and then we'll kind of dive into that.

Yeah, so I mentioned it a little bit but here in this graphic it's the same two by two, but I just created two clouds so to speak to basically I noticed something very interesting trend that basically was actually going back here. Yeah. So the trend here is that what I called the area of creator workplace productivity use case. It's actually very good fit for LLMs because as I said it's either the, the there is no right or wrong answer. So accuracy to some extent doesn't really matter or you cannot really measure it objectively. But, but that's for industries like entertainment etc. It's very specific industries that I'm not an expert so I don't want to talk too much about it. Even though I partnered with someone and wrote an article about adjusting to me it was like a testing ground to understand whether my framework applies. But if you think about some of the yellow bubbles on the visual and even some of the green bubbles is like writing business memo, email or creating business presentations, that's to me the crux of the use case that can start become useful in the enterprise setting and if you kind of focus on families of use cases. So families of use cases that in my opinion on short to medium term are relevant. One is entertainment that I mentioned perfect use case even though it has other dynamics, you know, it's highly litigious industry. There is a copyright protections and all this kind of stuff. But the use case, techno technically one is a perfect use case. No right or wrong answer, story is important. The next one is what I call for productivity and I think on a high level it's two big buckets the first, but there are kind of interact interrelated the first one, I call them roughly customer facing interactions. What it means is that anything where you have frequent interaction with the customer, where actually story and facts both are important. But this use case like customer support, technical Support, sales service, etc. Another use case that in many cases in highly technical domains, for example the networking domain like Cisco, is interrelated related to coding. Coding. By the way, it's a very good use case for LLMs because to some extent it's a language. There is a reason those models are called large language models, but that's human language. Coding is a human created language that was created artificially and it's much more structured. So LLMs are very good in dealing with that. And in some use case you actually combine those two because in many cases you need to run code to resolve some customer problem. Right? So that's a very important use case. And finally the next, the last one, I call it education professional certification. As you probably know, ChatGPT passes bar exam, medical exam, science exams with flying colors. One of the reasons for that is that it's actually very good in understanding and choosing from closed number of questions because it's very good in reasoning of understanding slight nuances and questions. That's what usually do. It's not an open ended question, it's actually understanding from a choice of questions. And there is multiple interesting use cases related to professional certification or education that I think will be very relevant to LLM. So that's on a high level. Again, it's not an exhaustive list of use cases, but that's kind of how I basically look look at the framework and then try to translate some specific families. In this case.

Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Genai. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hard hundreds of their employees on how to use gen AI. So whether you're looking for chat GPT training for thousands or just need help building your front end AI strategy, you can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and built a straight path to ROI on gen. Yeah, and like, like I've said multiple times, you've, you've got to just be able to take a look at this. So we're going to include this in our newsletter as well and break it down a little bit more. But Barack, let's, let's talk a little bit because I think now any business leader, any decision maker, you know, heard what you just said and if they weren't already, you know, all on board with using large language models for certain use cases. I'm sure they are now. Right. Like you can't just listen to someone, you know, with two and a half decades of experience and break it down this simply and still say, I think my business is going to pass on this whole large language model thing. Right. So maybe let's talk about, you know, how you actually choose the right model for the right task. Because I know there's no, you know, blanket answer for that, but how do you go about. Right. So you know, you see all these stats in these, you know, McKinsey studies that say, oh, you know, large language models are gonna, you know, automate up to 80% of knowledge work and you know, everyone's scrambling to figure it out, how do you find the right model for the right task and how do you go about making sure that it's actually working for your company?

Yeah. So I believe there is also a bit of a high overhype or maybe, you know, mis, misconception. And again, it's totally understandable because ChatGPT, which I call maybe, you know, first of all, a little bit of methodology. There is no small language models here. They're all large. Right. There is like large, homongous and gargantuan or something of that sort. Right. So those homongous or gargantuan large language models like Gemini or ChatGPT4 are pretty amazing and they can cover wide range of use cases. I mean, to some extent, any use case case. Right. But I encourage everyone to understand there are limitations on accuracy. Right. And there are other limitations. One limitation is cost that are expensive. And yes, it's Very easy and frankly cheap to try it out. But when you start scaling it for a big use case that become pretty expensive, right? The second one is control and maybe I mentioned it a little bit on the entertainment side. It's a highly copyright protective right. Like there is a lot of. There is huge importance for many enterprise of maintaining control alters their unique elements when utilizing AI models. Unique elements could be proprietary training data like an entertainment, right. The unique components could be privacy, security, etc and frankly I think it's for many enterprises it's also important to maintain maximum control over the destiny of the model because in some cases you might want to work on a domain that is not a priority for ChatGPT or Gemini and then you need to wait, right? And finally the last one is domain specific quality. You might find out for your specific domain or for tasks for your domains the quality needs to be upgraded, it needs to be higher and then you basically need to pay more to customize the model and you would still use a humongous model for a task that may be very specific. Because of those three things I think it's very important to be grounded and realistic and understand in some cases humongous foundational generic model works well. But in some cases if you have a relatively constrained use case and you want to achieve better accuracy with lower cost, you might actually go and try to create what's called fine tuning your custom domain specific model. And the good news about it, until year ago obviously doing it on your own required a lot of talent, a lot of compute. But now some companies like Mistral or Facebook or Meta actually created those pre trained large language models that are pretty good for generic knowledge. You can take it and fine tune it if you have your own proprietary data and actually create a potentially a better combination of cost versus domain specificity and maybe even more control. And as I say, it's very use case specific for some use cases, generic model works. For Ms. Case domain specific model works. But it's very important to understand there is no one side fits all here that you can all generic models work for every use case speaking.

Speaking of size and models, this is something I'm curious and you know, it's not often I get to talk to someone with this type of experience. You know, my viewpoint, and this is from, you know, an outsider that doesn't know a lot but you know, you know you mentioned, you know, Mistral and Meta's llama, right. And it seems like the models that they're coming out with at least are getting smaller, right? You know, we talked about, you know, hey, there's small models, large models and then these gargantuan models. Yeah. Are we going to see a future where models are actually are going to become smaller and that might actually help drive down costs and drive up, you know, use cases across the business spectrum Just as the fine tuning process becomes better compute technically becoming more affordable. Is that a trend that we're going to see is smaller models for more specific use cases?

Yes. So first of all, I think just to repeat it, nothing is small. It's like large. But yes, if you want to use numbers, the smaller today or large is like 7 billion parameters. Right. The humongous maybe 50, 60 billion and gargantuan is like 500 billion parameters. Nothing is small. But just again, as I said, a lot of current experience come from previous experience. For example Google Translate, many years ago, 2016, they had a model of 6 billion program parameters that was gargantuan at that time. Like it was nothing at the time. And we actually had a problem at that time. You know, cost was not such a big problem for Google, but we had a problem on performance. Our problem was that those models were at that technology and it's the level of GPUs at that time it was 100x lower than our production system to serve what's called inference. That's actually led Google to do two things. A, to develop their own custom hardware TPUs. That's why Google is so advanced in that space, believe it or not, was developed. The first use case was Google Translate. But the second thing we invested in technology that artificially shranks the model. It's called distillation where we basically effectively did the trade off. Okay, let's shrink the model and it will give us better latency. Yes, we will lose some in quality, but it will not be significant loss. And then maybe we'll kind of custom train or fine tune and get back that. So I think a lot of the things that we did this time now could be applied in enormous scale here. As I say, that's all use case specific. If you take this large 6 billion or small model, just for the sake of argument, relatively small model, and you have sizable proprietary data and maybe even create a program with high quality human data, you might actually get to relatively high quality bar with a fraction of the cost. Right. In some cases you cannot do that if you don't have enough data or if you don't have enough talent to do it, you might need to use a bigger model. So I think it's a range of possibilities. But I think it's very important to understand it's not only generic models plus RAG or retrieval augmented generations that people think that will solve all problems. Frankly, it can make it even more expensive because now you send a lot of context to this huge model, right? It's good for Nvidia, it's good for OpenAI, but not necessarily good for enterprises. So I think there is a lot of it requires to define the business problem. Well, meaning, like what is the quality bar? What is the, you know, what are your margins of your business? Can you afford to use those models? You obviously don't want to use, you know, a nuke nuclear bomb to, to kill a fly. Right. So it's important to understand and use a wide range of tools at our disposal to try to achieve your, you know, to, to serve the use case.

Yeah, that's, that's, that's a great analogy there.

Right.

And I'm just wondering, I'm just wondering the amount of compute that's gone out the window to, you know, write 50 versions of a, of a haiku or something just for fun, you know, so, so, you know, you did say something in there, Barack, about data, right? And, and that's how we kind of started this show is, is how, you know, most companies are going to have to start leveraging their data and bringing their data into large language models. Yet that does seem, you know, maybe the tide is turning a little bit. But that does seem, especially in the first kind of year or two of this popularization, so to speak, of large language models is so many people and companies and business leaders were concerned about the data and they're like, you know, they go from banning large language models to now all of a sudden they're building their own and fine tuning them. But you know, in your unique position, as you know, the VP of AI at one of the largest security companies in the world at Cisco, what's your thought on these companies that maybe still are hesitant to put any of their data into an enterprise level graded security large language model? What's your thoughts on that?

Yeah, so I think it needs to be practical. I think the most important message that I want to bring back how we started and I apologize that I'm using the fear factor. But like, if you don't do it, somebody will disrupt you. So you need to start with that. You have to do it now. There are ways to do it. Right. It doesn't mean you need to use it and just give all your data to OpenAI. That's why sometimes creating a custom model and investing in some kind of a small amount of high quality talent to develop those models. If you're worried about control, if you're worried about privacy, if you're worried about security, invest in your own custom domain specific models. There are also other range of possibility. For example, if you prefer, if you're worried about security of OpenAI, you can use an enterprise grade version of OpenAI, open AI through Azure, you can use Gemini through GCP, you can use Anthropic through, you know, AWS. That's the beauty of a cloud based provider that can provide you way better level of security and privacy. But again it depends on the industry how important the data is, how proprietary you consider the data. Are you okay that this data will be used potentially to sell to your competitors? That is all those questions that are very important to address. But the good news is that there is a range of possibilities how to address it, especially for enterprises and they need, they definitely need to think about it. Right. I in my opinion just say we'll ban it outright. It's not applicable. If you have large number of customers and customer interaction, I think you're setting yourself for being disrupted. Right.

All right. So, so Brock, this has been a conversation that I've, my hands are hurting because I've been typing so many notes because we've talked about, you know, kind of your, your very useful framework on the best ways to leverage or when to use AI for certain situations. We've talked about different use cases from entertainment and customer facing interactions and then we talked about how to find or when to use the right model for the right use case. So as we wrap up, maybe what's your best piece of advice for someone that is out there now? Again, not saying we're going the fear factor, but maybe someone is now a bit little, a little scared of saying, huh? You know, we've been hesitant and I'm scared that some of my competitors are going to be doing this. What is your one takeaway piece of, of specific advice for those people in order for them to really leverage generative AI?

Yeah, so I think it's embrace the technology, understand the technology. It's generally your friend. I really like the example of Steve Jobs that he was giving about technology. He basically said if you put, you know, if you do a competition on stage speed between all type of species on Earth, you know, humans without any technology will be, you know, in like 25 bottom, 25 percentile. Yes. Faster than, you know, a turtle, but way, way slower than a cheetah. Right? But human on a bike, human on a plane, human on a car will be way faster than cheetah. So I believe that's a very good analogy of technology. But it requires you to learn new skills. Right. If you don't learn how to drive or ride a bike, you will be left behind. Right. So you need to look at this technology, understand it's your friend that your strategic potential to take your business to the next level. But you need to invest understanding it. You need to invest upskilling yourself and upskilling your organization to be able to meet this. Yes, it's very exciting but also frightening challenge. But we need to embrace technology. You know, planes and fire and trains are all scary initially, but we embrace them and learn how to manage them. And that's what we need to do here too.

Love to hear. There's nothing more that I love than ending an episode with a beautiful analogy like that.

Wow.

This is a good one. So, Barack, thank you so much for joining the Everyday AI show and sharing all of your insights. We really appreciate your time.

Thank you. It was a pleasure.

And hey, as a reminder everyone, yeah, that was a lot of great insights from an industry leader, someone that's been around decades doing this thing at a high level. So you know, like I said, maybe you weren't able to watch live as we are going through kind of this diagram of use cases. So don't worry, make sure if you haven't already to go to your everydayai.com Sign up for our free daily newsletter. We will be recapping everything there as well as well as leaving links to more resources that are really going to help you make sure that you don't get chatgpt by your competition. Thank you for joining us and we hope to see you back next time for more Everyday AI. Thanks y'.

All. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit your everydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.