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In this episode, James Connor dives into the world of artificial intelligence with AI expert Sol Rashidi, author of “The AI Survival Guide.” Sol demystifies the rapid advancements in AI, explores the impact on jobs and productivity, and shares how AI is reshaping industries like investing, mining, and tech. With startling insights into how investors should prepare, she answers the burning question: Is AI a bubble or is it real? Learn why every investor needs to understand AI’s transformative potential and how it can either create massive wealth or disrupt the job market. Key Takeaways: *How AI will transform wealth management and financial advising *Why the resource exploration industry is ripe for AI disruption *The surprising truth about job losses and productivity gains *Strategies to harness AI for personal and business productivity


James Connor  0:00  
Hi and welcome to Wealthion I'm James Connor. In November of 2022 Chat GBT was released by open AI and since this time, AI has become ubiquitous throughout society. Even consumer products like Nike and Coca Cola have adopted AI enhanced strategies and products. And so what I want to find out today is, is AI real? Or is it all hype? And is it just another bubble waiting to pop? My guest today will help us answer these questions.

Sol Rashidi is an AI expert and the author of a book titled The AI Survival Guide, and we're gonna find out from soul if AI is real, or just type soul. Thank you very much for joining us today. How are things in Miami?

Sol Rashidi  0:46  
Thank you, James, for having me. Don't be mad, but things are lovely. It's 74 degrees. And sunny and beautiful. So yes, it Miami is lovely.

James Connor  0:57  
And there's always so much happening in Miami and in the state of Florida. So we have the Formula One race coming up soon. Are you a big fan of f1?

Sol Rashidi  1:06  
I like attending, but I'm not really a fan of the sport itself. But you're right. We have quite a few things happening in Miami. Yep.

James Connor  1:15  
And there's a the NHL playoffs are also happening. And there's a big battle going on right now with Tampa Bay Lightning and also the Florida Panthers as your hockey fan.

Sol Rashidi  1:24  
Yeah. Can you imagine Florida? We do well on ice.

James Connor  1:29  
I know. I know. You're You're making my hometown look bad.

Sol Rashidi  1:33  
And you'll forget soccer, right? We've got Messi, that's always lovely as well. So we're coming a bit of a sports Mecca down here.

James Connor  1:42  
That's right, there is so much going on. So let's move on now. And I want to talk about artificial intelligence. And I know nothing about AI. And so this is where I want to start with the very basics, what is AI? 

Sol Rashidi  1:54  
Well, just just a little bit of background to build some credibility with the group, right? The challenge we're having in the marketplace, for those of us who've been in this space for quite a bit of time, is you have a lot of researchers, you have a lot of academics, you now have a lot of consultants, and everyone's claiming they're an AI expert, or an AI advisor. And humbly speaking, I would never even call myself an AI expert, because the pace of change is just so fast right now that we're barely able to keep the finger on the pulse and understanding the developments, the releases the applications. So it is a very fun and exciting space. But I can't even imagine for what I can't imagine what it's like for people who are in this space to be hearing about AI this and AI that I've been doing real world deployment since 2011. So the book is predominantly based on not theory or academics. But a lot of the mistakes I made a lot of the assumptions I made that just didn't hold true. And then what it's actually like deploying these AI machine models into a variety of functions across businesses to increase productivity, efficiency and effectiveness, how to use it is a growth strategy. So the basis is really just based on real world deployments, and not hearsay, or hype or theory. So hopefully, I'm effective and sharing some of that, with with everyone in the audience here. You know, without getting too technical, at its most basic, basic form. Artificial Intelligence is simply the capability of a machine imitating human intelligence to a certain degree, whether it's within businesses, and actually enhancing the decision making, or automating repetitive tasks, or analyzing massive, massive amounts of information that previously were nearly impossible to do with our compute, Chip storage, infrastructure limitations, but now we can. So it has the ability to complement and supplement what we do at speeds that we've never been able to see before. And you know, there is a comparison between Watson, which was launched back in 2011. And open AI, it also has the ability to understand context and inference. So my running joke is, you know, if someone says I'm Gray, well, are we referring to the color? Or are we referring to the mood? Or if you say, I have wet feet? Can the machine understand? Is it because you're standing in the rain? Or is it because you're about to get married and you're nervous? So it's not perfect yet with contextual understanding. But in the past 13 years, and that evolution that's happened since Watson. It's darn near close. And I think that's the magnificent thing that we're seeing today in today's evolution. 

James Connor  4:32  
And you mentioned IBM and Watson and I vaguely remember this, but maybe you can just take us through that backstory. What was Watson and why was it so important? 

Sol Rashidi  4:43  
I mean, I don't think IBM deserved it's not getting the credit it deserves for what it introduced with Watson. And I know there's mixed reviews and people have different thoughts of was Watson successful or not successful? But for those of us that were a part of the team that helped launch Watson, for those of us that actually did deployments, it was magnificent for its time. Because if you think about just the resource needs, that are needed to be able to run an AI application from workloads, to storage, to processing, only companies like IBM or Intel, because Nvidia wasn't really in that space back, then were really able to handle those workloads and those resource heavy components that were needed. And my running example is is like, you know, right now you can go to Amazon and buy a four terabyte jump drive, just something you stick in your computer to save all your files for $129. A decade and a half ago, that would run you millions in a server room. And we forget that right. So it's Moore's law in full effect. So what IBM Watson set out to do was, we are also going to take in massive amounts of information, digest it, process it understand context and learning. And then through an just an application layer similar to chat GPT. Someone can ask a question, and it can retrieve an answer. This was it's not dissimilar to chat GPT. But just different architecture, different datasets, different volumes of data sets, different processing speeds. But Watson beat Ken Jennings in jeopardy, because it was able to do just that. And so the Watson capability in and of itself, however, was only available to other businesses. So it was very much b2b, oriented and enterprise oriented. But Watson was the first commercial grade AI application that was available to large enterprises. And it was well regarded for a while. But for a variety of reasons, it didn't necessarily take the traction that it should have, I'm happy to share those two later on. But whereas with open AI, because of the advent of data storage is now cheap, data processing is now cheaper. They were able to democratize this capability to everyone for free at first. And now they're charging everyone for it. So they went from a nonprofit to a profit, but their marketing their go, excuse me, their go to market was direct to consumer. And the original intention was to make this capability available to the masses and not just enterprises. And that's a major distinction between the two.

James Connor  7:13  
So as you mentioned, IBM was one of the first to come to market with this AI concept. But one of the things I'm kind of curious about, I want to hear your thoughts on this, the concept of AI really exploded in November of 22, when chat GPT came out. And why did it take off then.

Sol Rashidi  7:34  
Because of the direct to consumer go to market approach that they had taken. So when you have because AI isn't new for those of us who've been in the space, I mean, it's been in research for heavy research for four or five decades. You know, I would say that the original chasms started at Dartmouth in the 70s, where they actually had a conference where they named it artificial intelligence because they were able to find and discover that there were machines that could dictate or excuse me replicate human behavior. And so they were pulling together all the researchers to come together to discuss what the evolution of this was going to be. But this was in the 70s, and Dartmouth. And so fast forward, there's a lot of research that's been done. And even something as simple as a Roomba. If you own one, right the vacuum with sensors, like that is robotics in motion, if you add embedded intelligence that is also the first consumer, commercial, you know, AI product, and then we took Siri for granted, we took Alexa for granted, but those are all minute AI applications. We just never called it AI. So AI is not new. It's been around many, many decades. I think the chasm however, is is when Open AI released chat GPT in November 2022. College students were using it heavily in the universities. Parents heard about it cousins heard about it, it was available for free, anyone could download it and access it. And the and what happens is when you democratize something, it's for free. It's available, it becomes astounding. And then it takes things by storm. So their channel strategy and the availability for excuse me, their channel strategy and the fact that it was free, really is what set it off. And that's how I got traction. It was no longer restricted to enterprises or universities that were deep and academic and research.

James Connor  9:30  
Yeah, it's amazing when something is free and you give it away like people will line up for by the 1000s 

Sol Rashidi  9:36  
100%. I mean no different than how Microsoft has now crossed that chasm. Microsoft was a closed circuit, closed circus, excuse me. Microsoft was a closed circuit. They were not into open source and then when they opened their model to becoming open source Microsoft group similar to what meta is doing right now, with llama three. It is the largest open source large language model that's available out there. By democratizing these capabilities, they're able to scale and embed their capabilities across a variety of operations.

James Connor  10:11  
So let's have a discussion on what AI is, I want to look at how we can use it now on a day to day basis. And because at wealthier, we focus on investors and macro trends, etc, I want to look at the investment industry. And one of the big trends we've seen in investing here in the last 10, or 15 years is this big movement away from active management, toward passive, and that includes algorithms and ETFs, and so many other products like that. And so maybe you can just talk about that. And maybe you can also answer the question is, is because of AI, will active money management becomes obsolete, obsolete.

Sol Rashidi  10:54  
I'll share my prior example. And some of the deployments I did in 2011, and 12, and 13. What I'm saying now, and then just a point of view, when Watson went to market, financial services, was the first industry was healthcare and financial services were the two industries that it had invested heavily in. And the number one use case across financial services was really around an AI agent to support the financial advisors. Because the number of products and services, knowing anywhere from interest rates, fees, performance lending credits, it was just too much, quite frankly, for financial advisors to understand and know everything that was available and how the market was moving. And so as a means of making them intelligent, the intention wasn't to put them to school, and then have them go through yet another eight month training course, to be able to understand the entire portfolio of service offerings. The intent was is why don't we create these AI agents to complement and support what they were doing day to day. So they had aggregated their entire portfolio of products and services. And they had taught the machine to understand what these products and services were. So if a financial advisor got stuck, and I'm not talking about the basic, oh, I have a client who's 60 years old, risk averse, this is their income to debt ratio. What should I advise, although you could do that, you know, especially if you were a new financial advisor, and you had to be productive, you could enter in those attributions of a client that you're trying to help out. And it would give you the ideal portfolio mix of what to suggest and what to offer. Well, that was back in 2011. And there were things around commercial banking, where should the price points be for products and services based on existing interest rates, lending rates, mortgage rates, and also global economic, you know, circumstances that were about to hit us? So a lot of embedded intelligence, will you fast forward from 2011 to now? Morgan Stanley, I don't know if you've heard, but they have heavily invested in artificial intelligence, so much so that the chief data officer is now the chief AI officer. And guess what the number one use case is exactly what I mentioned and what we did in 2011. It's to be able to create an AI virtual agent to all the financial advisors. So not only can they give prompt responses, and not only to give junior individuals access to experience and insights, but because the number of products and services has increased tremendously. It's impossible to know everything. So how do you fulfill client obligations? How do you fulfill customer requirements? How do you fulfill NPS scores in terms of satisfaction, if you can't memorize everything that's available, and so virtual agents to be able to support the financial analysts is the number one use case right now. So not much has changed. Everything around AI in its use cases is meant to amplify, accelerate or expedite our knowledge and ability to serve us better. And that's the number one use case right now within financial services is to help the financial advisors do a better job at their job.

James Connor  14:09  
And if you look out five years from now, or 10 years from now, would a traditional financial adviser or stockbrokers they used to be called Do you think that job just disappears? Because a big part of their job right now it's just gathering assets, right? And then I think they take an individual's information, they throw it into a computer and they say, Okay, this is where we're going to allocate resources. Do you think that's gone, like in five or 10 years from now, and we have these like robo advisors doing everything?

Sol Rashidi  14:42  
I think that's the path that we're going the role will definitely evolve. Because at some point in time, the customer is going to say, Why am I paying you a percentage point when you're giving me the same answers as chat GBT or Gemini? or Perplexity, or because I was Siri seven certified for 17 years, I traded options. But I started out as a financial advisor, before I became a broker. And as a financial advisor, things you don't get really creative, you know, if fundamentally, they're 16 above, they're close to retirement, they're low risk exposure, it's all about passive income coming in, you're gonna invest in bonds, but you're not doing these like high risk growth based investments, you're just not. That's meant for a younger stiches constituency group, and then based on their income level, and what they can save, that's where you play with numbers. We this was in the early 2000s, when I was doing this, you fast forward, that formula has not changed how you treat a 21 year old that's in their growth period and part of their wealth accumulation phase, versus how you treat someone who's over 65. And let's say assume they don't have a family office. So they're not making the investments. At this point in time, it's just to be able to retire comfortably. The formula has not changed, it hasn't been too creative. So why do I need to pay a percentage point to someone to tell me what I need to do when all I have to do is type the question in, and it's going to tell me what to do. In addition to that, whether you're investing in American funds, or fidelity, it doesn't matter which funding group, they're all looking at embedded AI, because they want the business to come directly to them, which now means if you're going to invest with a fund, they're building these capabilities on the website, where you tell them a little about yourself, and they're going to ask you anywhere from six to 10 questions. And then they're going to suggest what the right investment makes us for you. So I would say at some point in time, we're going to wake up and ask the question of why do I need to pay you to do these when I have these capabilities available to me.

James Connor  16:44  
And they're performing better. 

Sol Rashidi  16:46  
And that's a bit of a provocative area to step into. I'm sure people can debate that. But to be honest with you, yes, I have seen that. It's almost like watch, used to have to pay for email. And then now everyone has email. And it's for free. It's a democratized service. So I think that the way AI is going to disrupt things, is not replace jobs, but it's going to force the evolution of certain jobs. And things that we used to pay for, are now going to be available for free at our convenience when we need it when we want it.

James Connor  17:26  
Interesting. So I do a lot of work with resource companies, oil and gas, base metals, precious metals, etc. And this can be a very laborious process, looking for oil looking for minerals. And it's also very costly, and especially when it comes to oil, like if you have an offshore oil rig, depending on many variables, how deep the will is, etc. It can be very expensive, like an offshore rig can cost you anywhere from $250,000 a day up to a half a million dollars a day. Mining is a lot cheaper. But nonetheless, it's still a very laborious process with mining, you're flying around in a helicopter, you're trying to identify certain minerals, you know, on the ground, and then you pick up these targets, and then you start drilling. But it's not really efficient. How do you think AI will be utilized when it comes to resources in the exploration of resources?

Sol Rashidi  18:23  
This is gonna sound so odd. But I love this space and that vertical and industry for two reasons, because there's an AI hat that I have. And then there's a data hat that I have. On the AI hat, like there are just five glaring use cases that a lot of the mining companies or mineral extraction companies are starting to play around with and some have actually been deployed. The easiest one is around predictive maintenance on the equipment, right. So predicting failures before they actually occur in order to reduce and minimize the downtime, which if equipment is downtime, it impacts top line revenues, right, that's just the life of it. But also being able to measure shelf life, and being able to predict when repairs need to be made in order to avoid productivity loss. Then another application that I recently read, and I researched and I thought this was amazing. It's not in production yet, but they're actually exploring it is around or grade prediction. So predicting the grade of an ore that's being extracted by analyzing the geological data from different drill samples. This is giving companies the ability to increase the accuracy of their grade predictions, and they're seeing a yield of anywhere from 10 to about 18%. Now, this hasn't been deployed at scale yet, but with the proof of concepts that are out there, they're starting to see an average of about a 15% uptick in inaccuracy. I think another area is really around environmental monitoring and compliance. So being able to see see potential compliance issues in real time time and making sure that you reduce environmental incidences with it could be employees or regulatory standards that have been set forth. There's applications that already live in scale and prediction around autonomous drilling. So autonomous drilling rigs. And just like Tesla is investing in autonomous vehicle, having autonomous trucks equipped with AI operations, so that they could reduce the labor load, and increase the safety of these drills and vehicles. But more importantly, also reduce fuel consumption, which is interesting, I wasn't aware that fuel consumption is one of the primary costs for or extractions, which I thought was interesting. And then the last one is really around resource exploration. So leveraging AI algorithms and models to analyze geological data, to be able to identify new mining sites. And they're able to do this by taking a scan of the geological terrain and understanding the composition, just based on a variety of different infrared technology. So this space, in particular, there's a lot of exploration, I would say most of it is exploration, very few things are in production at scale. But it's definitely, definitely see a lot of momentum that's on the AI side. And then on the data side, there is a fundamental challenge that companies that operate on edge have that I don't think people are exploring. And this is something that, you know, there's a company that I'm partnering with our And what's interesting about them is telco logistics, oil and gas mining, these massive corporations and companies and enterprises that are the backbone, quite frankly, of our economy. They don't operate necessarily in centralized cities, where Wi Fi and connectivity is just readily available. They're operating in remote areas, because they're doing, you know, mineral extraction and these mines. And so they fundamentally have two challenges. One is connectivity. So even if you were to leverage AI, or even if you were to deploy sensors across a variety of equipments to be able to do prediction, monitoring, connectivity is a challenge. So how do you partner up with the starlings of the world to be able to enable Wi Fi connectivity? So even if the data is coming in? How do you process it? And how do you understand if an equipment is about to fail or not. And then the second is around data processing. Because even if you have connectivity, it's not like you have a server sitting out in the middle of the desert, or in the middle of the mines. Everything is being leveraged through cloud, but you cannot transmit that amount of data over the cloud without these heavy heavy workload costs. So unless you're willing to spend several millions, right, in a week, just being able to do data transfer, it's nearly impossible to extract the insights from the sensors and these AI applications out on edge. So the technology and the solution that we're working on is how do you process information on edge, and then bifurcate, the essential data from the non essential data, and then send only what's relevant the essential data through the cloud based workloads into corporate or into enterprise. And so people aren't thinking about the stuff but it's not just let's deploy AI, it's, you've got to think about, well, I need connectivity. I need to process the information, how am I going to actually transition the information so it could be analyzed? And so these are all the things that we're working on behind the scenes to be able to make these AI applications scale.

James Connor  23:36  
Yeah, it's gonna be interesting to see how this whole resource industry evolves in the next five to 10 years, because like I said, so much of it's just so inefficient, like they spend hundreds of millions of dollars looking for things, they never find anything. But okay, so the next so that's a good discussion on on how it can impact various industries. And another element that I read about quite a bit is what it's going to do to productivity. And also jobs. And Goldman Sachs recently came out with a report saying the biggest impact of AI will be the economic growth, and they predict that a 15% increase in product productivity will result because of AI. And because of that increase in productivity, GDP will grow by $7 trillion over 10 years. And the same report also suggests that millions of people will lose their jobs because of automation. What are your thoughts on this?

Sol Rashidi  24:30  
I know Goldman Sachs released that and McKinsey has their own version PwC has their own version CrunchBase has their own version. It's not my job to say if they're right or wrong. But if you take a look at past and historical reports, when they did their predictions in 2023 and 2022 and 2021. These reports are trillions off. Now I know it's hard to measure so there's no I'm not criticizing and how they came up with the numbers. But if anyone's looking at the past, as a measure of the future, the predictions have been trillions off. And I would say, just know that directionally, it's going to have a compounding effect for us. But in terms of productivity, the job market and the workforce, I can only share with you my observations of actually having deployed these things and seeing productivity gains, my productivity gains have not been 15%, they've been upwards of 27 to 38%. It's been amazing. But I have not let go of staff whatsoever, I have reallocated my staff to two areas, the growing backlog of work that my team doesn't have the capacity or the bandwidth to handle. And it has been deprioritize. And my backlog every quarter continues to grow. So for those that haven't necessarily upskilled I've reprioritized their work in that 30% productivity gain to the backlog of work that's been piling up over the quarters. And I think, quite frankly, I need the knowledge workers, I need the ones with the institutional knowledge. I'm not replacing that staff whatsoever. I think the other area that I've reallocated to staff where we've increased the productivity of the capacity of the team, and we've given let's say 30% time back, if they naturally are more inclined to upskill, if they want to do other things within their career, and we value where they are, we've reallocated them towards growth verticals, because they have the context of the company, they have the context of the industry, they have context, quite frankly, of the functions within the company. And so we have a new product launch, or we have a new market launch. Or if we have a new channel launch. They're excited experience matters. And so for growth based divisions, that fundamentally you need to be able to support. So the supply and demand demand aligns. Well, instead of adding headcount, adding opex taking a hit on your EBITA. I've reallocated the resources towards those growth based verticals. So I always say with the productivity gains, it's not a matter of reducing your workforce, it's been able to do more with the same, which naturally has a positive impact on your EBITA, which I know everyone's concerned with, most people are. So these numbers that I'm saying of, you know, 30%, layoffs, 20% layoffs, and like, what organizations are you a part of, because there's a growing backlog. And if you are growing as a company, there are growing verticals and divisions, you need to take those institutional workers that have that context and experience and reallocate them to those areas, and help your EBITA. 

James Connor  27:40  
So I started this whole interview off asking the question, is AI real? Or is it just hype? And if it's another bubble, and so I want to get your thoughts on this now and every company in the world has adopted some sort of AI strategy. Right. And more recently, Coca Cola has adopted a campaign called create real magic using AI tools to let consumers design visual imagery. I'm not sure why. But anyhow, that's what they did. And another very interesting article I read about a Finnish coffee company called Kapha. has used the AI to craft a new type of coffee blend, apparently, it's very good. And is another side note, the Finns are the largest coffee drinkers in the world. Did you know that?

Sol Rashidi  28:22  
I did not. And I'm a coffee aficionado, like I roast my own beans. I did not know. Now I have to actually do research.

James Connor  28:32  
I'm not sure if it's because of the cold weather or the dark days or what but yeah, they drink a lot of coffee. And then of course, Tesla, they just came they've been having issues here in the last few months. And they recently came up with their numbers they missed on every matrix. But one of the things that Elon Musk is really pushing is this move away from EVs or the production of EVs toward autom automated driving, right or autonomous driving. But what are your thoughts on this? Like, is this whole AI thing? Just another bubble reading the waiting to pop? Or is it real?

Sol Rashidi  29:05  
It's not a bubble. It is real. But I think there's different trends. And I'll allow make an analogy to the trends. You know, Blockchain came and went. And there's still applications, no doubt about it, but we don't hear about it as much. But the applications of blockchain were limited to a very few. It's not like everyone can leverage blockchain. So it never really grew the way the original hybrid intended. Then there's we web 3.0. Right? That was a bit of a hype. Everyone hopped on that one. But then if you remember the mid 2000s, it was about big data. And everyone wanted to leverage big data capabilities, the ability to collect more aggregate more, synthesize more, analyze more retrieve more. Well, the big data trend has not gone away. It's now embedded into the threads of every enterprise because the proliferation of information coming As from the volume and the variety in the velocity of sources is massive. Is anyone tagging it is big data is anyone labeling it as big data do people even know to recognize if it's big data, not really. But it's embedded within the operations. And I think AI will not be a web 3.0 by any means. But AI will be like big data, we may not be talking about it actively. But it is going to be embedded in everything we do from the way we interact with our phones, the way we interact with our computers, the way we interact in business, the way we interact with customer support systems. I think like, for example, when you need help on something, you're not getting a person immediately on the chat, like on the chat, you're actually getting a chatbot. First, there is so much room for improvement, and I get frustrated connecting with a chatbot. But it is going to start closing the gap between what it feels like having a human connection versus a chatbot connection. So I think it's going to be embedded into everything we do, we're not going to even realize it at some point in time. We just won't be talking about AI 24/7. It'll just be in our lives. And I'll give you an example. I use about five to seven different AI tools on a daily basis consistently. I left corporate America, I call myself a recovering and retired C suite executive and I went out on my own. So now I advise I consult for companies who want to start AI but they don't know where to start. How do you establish a strategy which use cases to pick? Should it be external facing internal facing? Who do I need? What type of talent like business executives, family offices, non technologists, I struggle with this, and they don't necessarily have access to talent, unless it's like another consulting firm that selling we are AI expert services. So how do you even get started. But what's interesting as part of my strategies, there are things you can do daily that will help your personal productivity. And there are things that you can do within your organization that will actually help your margins and EBITA. And then I teach them both angles, so they can understand the full potential and the full powers of what AI can offer. So to answer your question, I don't think it's going to go away, I just don't think we're going to say the word as often in two years, it's just going to be embedded into everything we do.

James Connor  32:19  
Couple of questions, one point, first of all, you talked about the chatbots, I have to give a plug for Apple, because every time you fall in there, of course, you speak to one of these automated chat bots, I guess. But their service and it has been, it's always been the best. Like I don't know how they figured it out. But man, every time I got an issue, I phone it up and I speak to this chat bot and they always resolve it and they do so fast.

Sol Rashidi  32:45  
Well think about Apple has the largest market share on everyone's phone, they have literally engaged with nearly every question a human is going to ask the amount of information that they have, they can they're just putting it to good use. And this is what I implore with everyone, it doesn't matter. If you sell rugs, it doesn't matter. If you manufacture stainless steel equipments, it doesn't matter. If you have a massive national dry cleaning business. AI can be embedded to improve customer support customer satisfaction, bring back productivity and capacity. I just don't necessarily think that people understand how we can help them because they're still thinking, there's this visual image of robots taking over the world. But when you get past that the smallest components of AI actually can help your day to day business. It's just no about where to start. And what's right for you in the company. So I think you're right, Apple does have an advantage because they're sitting on top of all that data. And I think no matter what your business is, you're sitting on top of data and you don't even know you are and not necessarily knowing how to extract the insights from it. So some people are just better at analyzing their information and proactively doing something about it. versus taking a passive stance and saying, oh, yeah, we have the data, but I'm not sure how to access it. 

James Connor  34:06  
Yeah, saying and the other interesting thing about Apple is they always know why you're phoning. They say Oh, since while you're phoning like it's just, anyhow, the other thing you mentioned that you deal or use five to six AI tools every day, what are they?

Sol Rashidi  34:22  
Well, I'm not big on vendor plugins, are you sure you want to know, at least some of them are?

James Connor  34:28  
Well, maybe you can just explain what the what they're used for.

Sol Rashidi  34:32  
So I'll give four examples. I'm an avid reader, I think in our space you have to be and it doesn't matter if it's Forbes, New York Times or the academic research, MIT review, Harvard Business Review. I wake up at five and all I do is read until about a quarter to seven. The problem is is that the pace of change that we're going I can never read enough and understand and keep my finger on the pulse and so I struggle with a little bit of  FOMO However, there's one AI app that I use that every web article, every white paper, every PDF, even if I use, like the web version of times, what it will do for me is, it's a little button, I push on my computer, and it goes through the entire article, something that would take me 22 to 26 minutes. And it summarizes for me, key points, relevant areas and statistics. And it gives me three paragraphs of an executive summary. And it does it in less than a second. I exaggerate, it doesn't in less than three seconds, that then I read the summary, I then decide if I'm going to invest the 22 minutes in reading that article, if it's worth it, or if it's too high level and too generic. And it's information I already know, or if it's really poignant, and there are things that I want to dig deeper into, when I will invest. So I use it as a tool to help me decide where to allocate my time, based on things that I need to read within the industry. That's one example. The second example is, I get a lot of emails on LinkedIn. And in my personal emails, you know, amongst WhatsApp, teams, text, my Yahoo account, my Gmail account. It's just too much. But a lot of the inquiries that come in of hey, can you speak to our organization? Or hey, are you willing to teach around AI literacy to our organization? Or we deployed AI, but we got stuck here? What's wrong? There are questions like apple that I get that are very similar in nature? Are you willing to do this, that if I were to respond to every single message on LinkedIn, and email, it would be my full time job? So what this AI application does is it allows me to create templates of my responses. So for example, I get an email from Dan Smith saying, Hey, I heard you on this. We're going through AI exploration, would you have 30 minutes to connect with me and my team, we'd love to see if there's an advisory agreement. Instead of me replying back individually, which would take anywhere from one to four minutes, I've created templates of responses, all I have to do is put a letter on the keyword, and it auto populates. It knows to extract the name. It knows how to reference the project. It does it for me. And all I do is take a quick glance, and either I add to and personalize it. Or I think it's good enough as it is. And I click send. So something that could take me up to four minutes and a reply not takes me less than six seconds. I can get through more much quicker. The third application is you know, when I hop on calls, most of us are busy. I'm taking notes, I want to understand more about the organization, what are the action items? What are the concerns? Is there a way for us to collaborate and partner taking ferocious notes, and then the ability to actually go back and saying thank you for your time, here are the things you know, here, here's some notes that I took as next steps, here's what I want to do, it takes time for you to not only write down the notes, because you're now multitasking. But to summarize it into an email as a follow up, which is the right thing to do. And then to track the follow up and make sure everyone's doing what they need to do. I have so similar to how zoom says we can record this call our team says we can record this call. There's a variety of tools out there that you could use that record the call. But when the call is done, it sends you the email that summarizes the notes, who said what, and what the action items were and who is responsible for them. You then look at the email, you either approve it or you make some tweaks and then all you do is say click send and it actually sends it to everyone that was on the call. Like so these are things that just fundamentally they may sound simple in nature but they save you so much time so I'm personally a lot more productive in my reach to everyone was a lot more efficient.

James Connor  38:55  
While I'm going to have to check out some of these products because we have to definitely I definitely have to become much more efficient in my day. So I something else I want to ask you. Of course there's many we everybody knows about open AI is product and check GBT. But there's also Microsoft has a product called coal pile that meta has meta AI, Google has Gemini. What are your thoughts on these products and which one is the best one?

Sol Rashidi  39:25  
I think they all have their own purpose. If you're in an enterprise and you're using Microsoft or if you're running a small business and using Microsoft use the embedded AI capabilities that come with copilot but I'm on Google, I use the G Suite I use Gmail, I'm no longer in enterprise. Gemini is easy because it can easily go through and help you with meetings and emails and whatnot. Open AI is great. Like I have perplexity. I have open ai i have Gemini open at all times. Because I'm learning that they're all good at different things. It's not one size fits all. So I will act actually test and try perplexity is really good with pointing out the sources of information when you ask it a question. But like, it's not amazing at, hey, I need a communication strategy, make it eight pages long in a deck. And here are the points I want to cover, what should I cover. But if I say that I'm trying to understand what the current trends are within oil and gas, and what the macroeconomic factors are, complexity will not only give me a detailed response, but actually the sources of information. So I now get to vet whether or not the sources of data are good enough for me, whereas open AI doesn't do that. But open AI, I was like, Hey, give me a communication strategy. And it needs to be like this. A very good at the general stuff, but it never gives you the source of information. So there's no best, just use it. And the way people use it and make it a habit is whatever is easiest. So if you're using Google, use Gemini, if you're using Microsoft, use co pilot, if you're using that, and you don't want to use either, and you don't want to pay for any of the licenses, which is dirt cheap anyway, use the things that are free. There are so many Chrome extensions that are free and available to you. And by the way, to the three that I mentioned, are free. They're Chrome extensions. So there's a lot out there, and they're, they're free. 

James Connor  41:16  
Free is always good. Well, listen, this was a fascinating discussion. And I want to thank you very much for spending time with us today and just educating us on this subject. And if someone would like to learn more about you and your various services, or if they would like to check out your book, where can they go?

Sol Rashidi  41:31  
I appreciate you asking that question. Um, so the book is on Amazon, knock on wood. It is a best seller now in three areas: starting a business, who knew, leadership and performance and in artificial intelligence. My name is Sol, S-O-L Rashidi R-A-S-H-I-D-I  and the book title is your AI Survival Guide, scraped knees bruised elbows and lessons learned from real world deployments. But I am very active on LinkedIn. I post nearly every day to non technologists on the adoption of AI and trends and comparisons and how to leverage certain things to increase productivity and efficiency. So if you look at my name, Sol Rashidi on LinkedIn, just follow and that's a great way. And then I also have a newsletter, where I go really deep into certain areas of if you are a business and you want to start with AI, how do you even establish an AI strategy? What are the 14 tenants that you have to consider? So newsletter is where the detailed stuff is. LinkedIn is what I call your two minute mini appetizer. And then Amazon has the book.

James Connor  42:39  
Well, that's great. I will reach out to you on LinkedIn. Make sure you connect with me.

Sol Rashidi  42:43  
Most definitely. Thank you. 

James Connor  42:46  
Thank you very much.

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