EP312: Beyond the AI Hype: How to Actually Use AI to Grow Your Business

Neil Twa (00:00.366) Hey everybody, welcome back to the High Voltage Business…

Neil Twa (00:00.366) Hey everybody, welcome back to the High Voltage Business Builders Podcast. this is a episode, a guest episode. You we've been doing a lot of individual episodes. You should check out the last couple ones where we were talking about some fun stuff around Walmart and unlocking growth hacks. today we're gonna kind of pivot just a little bit to a guest podcast. welcome Russell to the to the podcast. How you doing today, brother? Russell Halper (00:19.575) Hey, I'm doing well. Thanks for having me. How are you? Neil Twa (00:21.782) Yeah, man. Yeah, I'm doing great. I'm actually a little hot. I just jumped out of the shower. We were working in the garden this morning. It's pretty darn hot out there. And it was my job to go get all the the straw from the from the local feed store and then bring it back this morning and unhaul and undo it. So I'm covered head to toe in sweat and straw. That's how my morning started. getting out with my hands in the dirt and doing some work. Which you know temy or it's that tiny year. Did you do it's funny, I moved I moved from the west coast to Florida into Miami. Okay, yep. I love the garden, so we grew tomatoes and Russell Halper (00:40.825) Yeah, you know, it's that time of year. It's that time of year. It's funny, I moved I moved from the west coast to Florida and to Miami. And I love the garden. So we grow tomatoes. And it's always a summer thing back home in California. Here, you try to grow them in summer, it's way too hot. It's like it's a counter seasonal, the whole growing season. So I had to took me a year or two to get all that stuff reset to figure out how do we get the crop cycle and our garden like on the right, on the right rotation, and then what grows well here versus home. Neil Twa (00:52.846) It's always a summer thing in California. Here, you try to grow in the summer, it's way too high. It's like counter seasonal, the whole growing season. So I had to took me a year or two to get all that stuff reset to figure out how do we get the crop cycle and our garden like on the right on the right rotation and what grows well here versus home. Yeah. It's a learning experience, man. It's a whole different kind of I mean, the agricultural side obviously is very different than what you and I do for our for work. But I sure like getting into the dirt and playing around with it and you know understanding what our our zones are, like we're in zone five, like what does that mean? And I grew up on the West Coast. So when we had gardens there as a kid, there was a totally different growing expectation than the one we've had to learn how to manage out here. be patient, get in the dirt, learn how things grow. But it is really cool when stuff actually grows, isn't it? It's like a it's a visual affirmation of the work you put in and the and the belief you had and the faith you had that that thing was actually gonna grow up and produce a tomato or whatever, right? you can do things that you would never be able to get Russell Halper (01:46.039) Yeah, and you can do things that you would never be able to get otherwise. You know, like I love to grow peppers and so we have, you know, we have various types of peppers that you'll never find in the store, but are just tasty and great and you know, you throw into a dish and they just add a little bit of something that would be hard to get otherwise. Neil Twa (01:49.549) Yeah. We have various types of peppers that you'll never find at the store, but are and great. You know, you throw it in the dish and they that would be hard to get otherwise. We got herb garden, we have lettuce and tomatoes and cucumbers and potatoes and beans, and all kinds of variety and asparagus. it's just great to produce. The problem is most people don't understand the amount of work you have to put in. I I think it's like an analogy for business. They don't understand the amount of work you have to put in to have any success with it. and they just think if you stick it in there in water it, magical things are gonna happen and they forget all about the composition of the soil and the fundamentals of the soil and what it actually takes to build something to grow it into something, which is again a metaphor for business. If you're catching that, wink wink. Russell Halper (02:30.637) Well it's also like a lot of things. You gotta put a lot of the inputs in before you see the outputs. Neil Twa (02:37.59) You have to have faith. It's just what it gets down to. I mean, I put stuff in the ground and I believe if we water it and have the soil right, it's going to grow and it's going to produce something of value. And it doesn't always turn out that way, or it turns out very different. Like we we planted these artichoke heart plants. I forget what we we lovingly call them fartichokes. They're a tuber and they grow like crazy out here. I guess there's some family of the potato family or something, or the artichoke family or whatever. And they're now like taking up an entire row. They come back every year, they're totally plorific. We call them fartochokes because we cooked up a whole batch of them one time when they finally produced, we're like, my gosh, we have like all of these, you know, tubers. We'll cut up. They're like potatoes. And we fried up and and ate And then about twenty four hours later everybody was laughing because we called and started calling them fartochokes. I that's a personal sunchokes. We put 'cause sun there's there's something these tubers like that are known specifically for that. Yes, yes. That's right. That's right. So Russell Halper (03:20.793) Are they are they sunchokes? Are you couse sun there's there's sunchokes with these these tubers like that and they're known specifically for that and I forget what it is, but they have a a non digestible fiber in there that tends to do that. So yeah. Neil Twa (03:36.215) Yeah, and we lovingly called and they back every year. So what do we do? We just we turned them into fodder for the animals. So as they grow every year, we tear them out and we throw them in. The chickens and the ducks love them, and who knows what they're doing out there because I'm not around. I just don't want it in my house. But they're really delicious. So I mean if if we had to, you know, live off of them, at least we wouldn't die. But and they come back every year and they're just great in moderation. Yeah, in in moderation. They actually taste pretty good if you've ever tried one. But that's not what we're here to talk about today. Although we've talked about two for a long time. I love the garden, love the outdoors, love. Russell Halper (03:53.231) Yeah, the the grade of moderation. The grade of moderation. Neil Twa (04:05.198) playing in the environment and getting dirty. which is literally another metaphor for business. I'm full of those today. But what we're talking about is something completely different. You want to tell what we're gonna talk about today? So I can talk I mean my world is maybe Russell Halper (04:18.585) So I t I mean my world is maybe different than some. I tend to work with companies doing AI. We mostly focus on large companies, but we're focused on companies that that sell physical products, right? That make move or ship physical products. So retail's been a huge industry for us. lots of experience there and you know, excited just to hear about and talk about, you know, what we're seeing out there in terms of how Neil Twa (04:25.89) Different than some. I tend to work with companies doing AI. Yeah. We mostly focus on large companies, but we're focusing companies that that sell physical products, right? That make move or shift physical products. So retail's been a huge industry for us. excellent. Lots of experience there and you know, excited to just hear about talk about, you know, what we're seeing out there in terms of how how it's affecting the marketplace. Yeah, let's unpack that 'cause obviously Russell Halper (04:49.017) how it's affecting the marketplace. Neil Twa (04:53.314) You know, we're in physical products too, across multiple platforms and retail. so we see a a wide assortment of opportunities and difficulties and and whatnot. What I guess, you know, AI is such a broad word these days. Everybody's slinging around like they have any idea what it actually means, like pretending to know sort of how AI impacts things. And what we're mostly seeing in and to my understanding right now is a lot of marketers telling everybody their business could be fixed with AI. And and what I'm seeing, and maybe you can elaborate on your viewpoint, is a lot of What I'm gonna call garbage in and garbage out. built on the mistaken idea that they have their fundamentals, they have their net profit in line, they got their contribution you know, model and and margin in line and they don't really even know what that means. And they're trying to apply AI to I don't know, usually solve sales problems. Is that what you're saying? Yeah. I mean the sales Russell Halper (05:45.069) Yeah, I mean the sales problems, inventory problems, assortment, understanding, you know, how do you get the right product in the right place at the right time? and it's has quite a few different ways that it can be it can be it can be leveraged. The thing which I think is interesting right now with AI is first of all, it's got a great brand. Right. If you separate out the what the technology is from the actual name, Neil Twa (05:58.68) few different ways that can be it can be it can be leveraged. The thing which I think is interesting right now with AI is first of all, it's got a great brand. Yeah. If you separate out the what the technology is from the actual name. Russell Halper (06:15.425) It it it it really hel it's it's it's an incredible technology. It does a lot of interesting things. just yeah, I'm gonna pause as an aside. I'm getting a echo back from your side with my my voice. Yeah. Neil Twa (06:15.914) It it it it really hel it's it's it's an incredible technology. It does a lot of interesting things. just you know, I'm gonna pause as an aside. I'm getting a echo back from your side with my my voice. are you? Okay. Let's figure if we can figure out the technology here for a second. I'm not hearing that on my end. Yes. I'm hearing my words back to me a half second later. Russell Halper (06:34.479) I figured before we go and record for for a bunch of time, maybe we'd you could you could hopefully edit this out in post. But it was then it was distracting me from speaking 'cause I'm hearing my words back to me a half second later. Neil Twa (06:46.505) man, okay, so noise reduction is on. Echo is on. I don't know if anybody else is hearing it. I wouldn't want it to show up in the recording, of course, if it's gonna be in the background. Echo cancellation is on for you. It is not on for me. Well, let me pause real quick. this endeavor. Russell Halper (06:57.027) I'm seeing Hmm. Could it be something on my side that I can change? Neil Twa (00:01.112) So Russell, where do see the intersection of AI and revenue like management, net profit, supply chain, and maybe unpack the unit economics a little bit and and what we call contribution margin? Russell Halper (00:16.143) So it's it's it's a really good question and It means different things for different businesses, first of all. And so I like to separate out the brand name AI from actually what the technology is. Because it's an incredible technology and it does a lot of things, but the brand artificial intelligence sometimes like sends this image of it's this omnipotent technology that could do anything for you. And the reality is it's a incredible technology that allows the democratization of certain things that were really hard before. But it's still a technology, right? It still has its finite limitations and the things it can do. And so when we look at how businesses are leveraging it, you know, the ability for somebody to go and vibe code an application, for example, right, is something that is incredible. it used to be you needed to go hire a dev shop to do some of that work. Now you still might want to work with a shop, but at least like the work is much quicker, or you can get a lot further on your own. And there's know questions around security and scalability and these things that are are real questions, but you can get a lot further, right? If you're gonna go and download Cloud Code or Codex and start playing with it. so when I think about that in the context of a business, I could get access to all sorts of information that'd be hard for me to have access to otherwise. I can build workflows and things like that that allow me to better direct my attention. Neil Twa (01:29.496) Mm-hmm. Russell Halper (01:51.225) reduce the amount of time I spend on things, get better visibility across different data sources and how do I collate them to understand what's happening in the business so I can understand as I think to as I think about gross margin, where does that play? but all that is predicated still on knowing what to do. You know, with with I I like to say with Cloud Code, it's faster than ever now for people to build the wrong thing. Neil Twa (02:17.237) Yeah. Yeah. Russell Halper (02:17.404) if you could always build something, like if coding is not a new thing, but you could build stuff more quickly, it doesn't tell you if you're building the right thing or the wrong thing. And that's where I think Neil Twa (02:23.136) Right. Neil Twa (02:26.579) No, it's true. You can speed your way right off a cliff before you even realize that your API key has been publicized because you put it on Git 'cause someone told you to store it there and didn't realize that your Git repository was public. Russell Halper (02:37.88) Right, right. There's that. You know, there was a it was in the news releas recently, like there was a a QSR restaurant chain, right? That their you know, they put a chat bot out there for customers and it was hacked and being used for actually coding by, you know, developers through their website. you know, there's all sorts of weird things that are happening with this with and and Neil Twa (02:59.393) Right. Russell Halper (03:05.412) tricky things, but at the end of the day, like if you want it to do something for you, you still need to be able to tell it what you want. Neil Twa (03:12.139) You have to have enough experience is what we're talking about. And and this is one of the things I think that people believe that because AI is so powerful in many ways in its ability to disseminate information, take it in, recall. And of course, it's very good at the the mathematics side of things. Of course, it can also hallucinate on numbers too, if you're not careful and give it boundaries. it will hallucinate calculations. And what we are working on in our world is what we call experience as a service, which is trying to again utilize human knowledge and experience that isn't currently within these machines to some degree, idiosyncrasies of that and help people control fundamentals before they attach AI, or they're going to get garbage out because they simply don't have the experience to know that what they got was wrong. Was the wrong answer, it was the wrong outcome, it was the wrong calculation. It was just wrong. And they at some point I think I feel like people don't believe they can be wrong. Do you get that too? Like they they don't believe the answers could be wrong because how could a machine give you a wrong answer? Russell Halper (04:11.268) I think it's I've seen that, but I would I would phrase it it my perspective is a little different, which is it's not that people don't believe it could be wrong, it's that it it's really programmed to sound right. Right. It's it's it's sometimes very confidently incorrect. Neil Twa (04:25.829) that's true. That's a great way of saying it. Neil Twa (04:30.219) Convincing. Very confidently incorrect. Good way of saying that. Russell Halper (04:33.678) So so here's an example. And this is this is a true story. and this is gonna get back to your comment about numbers. So my wife, my wife, maybe two months ago had to go drop something off at the the po post office and had to go drop something off at FedEx. So she went to chat GPT and said, Hey, can you can you give me a route of of where to go? And I need to do these couple errands, send me the most efficient route. Chat GPT, here you go. Here's the addresses, go here, go there, goes to the car, puts in GPS, goes to the first route. Gets to the first stop. Turns out to a it is not a one of those two destinations, it's an adult boutique that it led her to. Neil Twa (05:15.821) Now she has some explaining to do on her chat history. Russell Halper (05:16.858) So Right. So she says, Well, you why'd you do this? And of course it says, yeah, that's terrible. I'm so sorry about it. I'll definitely not do that again. Right. And so Neil Twa (05:29.412) yeah, you're right. I'm sorry, I was wrong. I I can't tell you how many times I've seen the system say that. Russell Halper (05:33.24) Yeah. So like the EQ on these things is relatively high, right? They're tuned in a way where Neil Twa (05:37.089) Yeah. Yep. Russell Halper (05:42.053) They're they're they're meant to be conversational, right? If it sounds weird, that's not what the goal is when you're building the technology. But it's sometimes hard to know if it's giving you a good answer up front. And while the models get better and hallucinations get lower, having that critical thinking is still important. And it goes down to how do you tell with a model what to do, how do you use it in the right way. That's not a skill that's gonna disappear. In fact, if anything, that's gonna a skill that's gonna become more valuable. Neil Twa (05:56.716) Yeah. Neil Twa (06:09.997) That's right. That is absolutely right. Yeah. Until the machines, you know, they they're intellectually smart and socially inept in many ways. And that's because they miss the nuances of the human psyche. That's just not built into it. But I believe that, you know, with my experience in machines and human, you know, machine language learning systems that are now large language models that are really the precursor to AGI. or general to general intelligence is that they are going to continue to to grasp that intelligence until we see the quantum effect of those computers, which is in essence a basically a a wet brain of information, starting to to close that gap on people. So it really is those who, in my world and experience, my two senses, they're gonna be the the very finite amount of people who actually understand enough to be dangerous about how it's working to be able to try to control it or utilize it in in their lives. And there are gonna be a lot of people who are kind of left trying to keep up with it because it's generally smarter. And there's Russell Halper (07:06.99) You know, when when Google was released, right, learning how to search on the internet in itself is a skill that people had to learn at at and mass. And I think over time the way people have searched has gotten more and more sophisticated. There's kind of like socially the skill, right, that we all have to learn and get better at on how to use these types of tools because they're You know, they're out like they're out of the box. They're not going anywhere. And and they're very happy to tell ya the wrong address to drive to sometimes. Neil Twa (07:37.899) Yeah, that Pandora's is op that box is open. Neil Twa (07:46.07) Yep. Russell Halper (07:50.265) you know, when they gotta go somewhere. So it's it's like the and then you know another example you're talking about numbers and like forecasting, right? you know, one of our one of our clients is a very large, well known Silicon Valley company that's got a huge AI business, AI related business. And you know, I was talking to one of the people there senior person who runs the the forecast and at some point in and said, Hey, are you using AI for forecasting? And he said, I mean, look, it it's good for understanding the forecast, digesting what's there, asking questions about what the forecast is, but we're not using it to create the number. and you know, at least where a lot of these large language models and frontier models are today, is they're really good with digesting and generating text. They're good with digesting and generating images. They're oftentimes very good with Neil Twa (08:29.249) Mm-hmm. Russell Halper (08:44.28) Digesting existing numbers and allowing you to understand, like, how do I connect different data sources? Like, what does this mean? Tell me the insights about how my sales have been performing, what's going well, what are the what are some of the trends? They're less good when it comes to making complex decisions, especially when those decisions are numerical. so, for example, if you wanted to say, predict how many sales I'm gonna have of particular SKU. Neil Twa (09:05.056) Mm-hmm. Russell Halper (09:14.67) you know, by month for the next six months. they will probably come back and give you an answer. It's not clear to me that that's going to be a really good answer. And in fact, when we work with a lot of our customers and we do this type of work for large companies, you know, companies that are multi-billion dollar companies, and they really need to answer these questions if they answer them wrong, it's it's very expensive. Neil Twa (09:24.631) Yeah. Neil Twa (09:29.089) Mm, mm-hmm. Neil Twa (09:42.433) Yeah, yeah. Russell Halper (09:45.455) That's not what how they use it. Like they'll again, they'll use it to say, help me find places where my plan has changed, or you know, help me describe this part of my plan, or you know, roll it up and give me a report. But the actual generation of that number, which is what you're running the business on, is where they can definitely play a role in helping you get there, stage the data, the look at it, writing code for a numerical model that you could then run in Python. Pulling data from third party sources and getting it collated with your existing data so you have a broader harmonized data set. But there's that that fine line where it's like you tell it to create a number for you. And that's when it starts having more challenges. But it'll do it. And that's again where where it gets a little dangerous sometimes, which is are you relying on it for the things that it's good at? Are you relying on it for the things it'll tell you that it's good at? Neil Twa (10:42.477) Mm. Russell Halper (10:43.831) and that's those are two very different things. Neil Twa (10:46.701) They are, because one of them depends on your ability back to the experience conversation to actually understand that you got a you got an answer that may look correct, but it may not actually be correct. Not not at the level or the the situation or the complexity which you thought it was. that's the thing you have to be able to confidently incorrect. I love that. But what you're also talking about is up what you mentioned a minute ago is is operational AI versus just straight up AI for the use of AI. Russell Halper (11:02.874) Yeah, they're they could be confidently incorrect, right? Neil Twa (11:14.413) Isn't that what you're referring to is how operationally it can disseminate or be used or propagated across an organization correctly so that it can create efficiencies and not problems. Because I could see back in my mind from my days in the corporate world how if that complexity, the silent information across the large organization like the one you're referring to, each has departments and heads and VPs and stuff, and they're trying to figure out how to incorporate data used to be spreadsheets and meetings and databases and corporations, and that literally can't, you know, doesn't even need to be a meeting anymore. it's just hey, build an MCP and we'll talk to your system and they'll talk to ours. How how are you seeing that play out with these companies? Russell Halper (11:52.549) So when we talk about operational AI, it's about for us, and again, we're working with with you know these organizations that have lots of different stakeholders and complex decisions, it's about how do you build in AI in a way that actually makes sense for how do you run a supply chain, run your manufacturing, run your pricing solutions, or revenue growth management department departments, those types of domains. And when you think about doing that, it's there's a lot of very pragmatic constraints you have to build in based on how that particular business runs. And you could have two businesses that are selling directly competing products that would, you know, sit next to each other on a store shelf. and the way their supply chains can be set up, the way they bring the product to market can be entirely different. And so when we think about how do you effectively leverage AI in those situations, it's not necessarily you build it once and you scale it. Neil Twa (12:34.625) Mm-hmm. Russell Halper (12:50.82) to make it really effective, you need to think about how those companies operate. And to your point about meetings, there's some meetings you might not need anymore, but there's others that become still critically important, especially when you there's a concept with AI of like do you have human in the loop or human not in the loop. For a lot of business decisions, especially ones that involve lots of money, lots of product, right? It's it's hard to not to want have not to want to have Neil Twa (13:06.005) Mm. Russell Halper (13:20.132) people in that loop. Yeah. Neil Twa (13:21.625) I think you have to have people in that loop. I I I am not a pure like just AI, like I make decisions, run everything automation. It can't automate and analyze things and create analytics, but a human should be over the final decision making to you know, avoid that kind of thing we talked about earlier, which is garbage out. Russell Halper (13:38.363) I was talking at a a a large apparel retailer about a year ago and they came in and asked they had a few folks in to give a talk about AI and they had McKinsey in there and they had Deloitte and Accenture and and me. big five and some redheaded guy. and and is there and and one of the things we're talking about is organizationally, what's the accountability for decision processes that have to happen? Neil Twa (13:50.851) the big five, right? One poor guy. Neil Twa (14:05.729) Yeah. Russell Halper (14:06.13) And when you think about, let's say something goes wrong, right, or something goes really wrong. You know, then you get the CEO talking to, you know, the COO or the CSEO. That person then wants to go talk to the VPs and talking to a director. And at some point, somebody's accountable for a decision. so you know, that that fundamental desire around having somebody accountable for a decision affects the way you think about bringing technology into play. Because it's hard to say you're accountable for a decision, but we're automating all of it and you have no control over what that's gonna happen, right? That's an organizational challenge. and so that then turns into a different ways you may think about leveraging some of these systems, the way you might think about building KPIs and organizations to be like to measure performance. Right? Are you measuring people's performance? Are you measuring the AI's performance? how do you think about feedback loops for these systems? so as AI gets better, your business continues to get better. And and I think that that those types of human fundamental truths aren't gonna change, right? People still are accountable, wanna hold other people accountable. They still wanna have some level of control. And it doesn't mean you can't automate things, but it does change the way you want to interact with it. Neil Twa (15:24.375) Well, yeah, that's a very good point because what we're just as an example of what we're doing, we're not automating strategic decisions that have to do with finances, where money's deployed, or how product will be moved and when it's moved per se. But we are using it tactically to control expected outcomes, U cases, SOPs that we've defined that are normal processes of operation in which they can move autonomously as operators through those decisions without a specific oversight from a stakeholder. And those we see being operated very quickly. Those SOPs are being taken over very fast. And quite honestly, that's, you know, half of the business or more just in automating SOPs. Right. we're getting down to about, you know, 30% of the business will be strategy, fiscal management, and oversight based on analytics and experience to make the decisions as the systems are automating the analytics, updating the reports, and tracking the business, the ads, the management, the inventory, and everything that moves around it logistically. That's where I see maybe the companies you're dealing with really getting a major boost. It's also changing competitiveness, isn't it? I mean, it's allowing smaller companies to move at a rate, a speed, that some of these larger companies are moving. And it's always been that way. The small boat can move faster than the Titanic, but our ability to compete head to head has changed dramatically. Russell Halper (16:47.074) You have a you have a I'm a I'm a math guy at heart, so I don't love this term because I think about it very, very numerically. But you have what people call a exponential technology, in terms of the impact and the rate of change. And you have an adoption curve that is dramatically faster in small and mid-sized businesses than enterprises. both because of some of the risk profile around it. the simplicity versus complexity of the operations. And it used to be that you would run into challenges of in mid-sized businesses about doing stuff because they didn't have the data platforms or the data in place to be able to do some of these things. And this effectively can also act as a band-aid for some of those things as well. So Neil Twa (17:31.381) Or the hiring capacity that some of the larger companies had to get some of the people in board, as opposed to big agents. Russell Halper (17:35.704) Yeah. And so it's it's this it's incredible like like force multiplier that's out there right now that you know you you and it's I think it's a real opportunity for small and mid sized businesses right now. Neil Twa (17:39.745) Force multiplier. Yeah. Mm-hmm. Neil Twa (17:46.53) Well, it is. Those who've got the risk and the chops to get out there and start to, you know, I'm gonna say imperfectly adopt it as they try to figure out how to adapt it, because we're still in the very early adoption phase in in my mind, and what I see it's talked about a lot, but honestly, if you were to go out and ask a hundred people on the street what they know about AI, ninety-nine of them aren't gonna have a clue yet. So we are we are still on the very cusp of what this could actually look like. In my world, anyways. I don't know if you see it that way, but I I see us still in the very early stages of what will become a mass adoption. It's just you know, how fast it becomes, I believe you can exponential will be much greater than it took the internet to become a sensation. watching that come online, it took about 20 years for it to really hit mainstream. I think we're gonna do it in about five, just because of how fast it can actually move and all the systems are in place and the internet's in place and. Technology is everywhere and mobile phones are everywhere and all the basis by which it can move is everywhere. So that creates, you know, opportunities, I think, for early adopters, working with folks like you and businesses at the scale they're working at right now to figure out how to get ahead of it before their competition catches up. which gets into the conversation about, you know, how do these companies use it to actually scale across their organization and take over market share? Because that's honestly why you're doing this, isn't it, with the companies you're working with? is to not only just approve their bottom line, but to improve their, you know, awareness, top of funnel, marketing, everything, top down. Russell Halper (19:14.968) All the our companies we work with are using it for all those things. I will be trying to I I don't work too much on the marketing side, but we we do a lot of work on pricing and and you know other areas of operations. One of the things that I do think is important is thinking about how do you bring it into play. And so there's the you know, things that are fairly standard, SOP's economy. Fantastic. There's things I can get to that maybe I couldn't get to otherwise, right? Here's a set of work. Neil Twa (19:27.244) Uh-huh. Russell Halper (19:44.43) that needs to get done or be valuable for me if it gets done. And I just won't be able to do it because of whatever reason. Time constraints, ability to access the technology, but I can have AI help me get get me something that even if it's a eighty percent solution is better than the zero percent solution I'd have otherwise. the other way I I I think is an incredibly effective way to use it is Neil Twa (20:03.467) Right. Russell Halper (20:11.972) Helping you find the places that need your attention. Right. Tell me about the parts of my business that look off, that look weird, where I'm not performing, where you know, I scraped a bunch of competitor data. You know, what's happening that I should look at that I am not looking at before? here's my latest thoughts on what I'd like to do here. Tell me where you can poke holes in this line of reasoning and where I can make these thoughts better. Neil Twa (20:20.642) Yeah. Russell Halper (20:41.978) Right. These are all areas that are really good opportunities to go and leverage it as is today. And as AI gets better, right, what you build will continue should should continue to get better. and and so having those different mental frameworks about how to use it, I think is incredibly important. you know, repeatable processes that don't need a lot of human decision or judgment. you know, the the work you can't get to otherwise, and even if it's at 80% or 90%, and it might be better than that. I don't know, right? But getting it done gives you some value, or being able to use it as you know, exception management, alerting guardrails to help you understand when there's something in your business that needs attention that you might otherwise miss or not find as quickly. Neil Twa (21:40.589) Do you believe that as some say, AGI is around the corner, twenty twenty seven? And if yes or no, do you feel that that's gonna change or impact the model what you're doing in the businesses you're Russell Halper (21:53.335) I Russell Halper (21:59.397) I don't know. So I think there's a difference between being learned and being smart. If you think about, I imagine somebody's got an MBA, they're coming off, they're coming out of a top school, they've had, you know, Ivy League education their whole life. they're definitely smart, they're well educated, right? And they they they might have a great career ahead of them. And again, this is all hypothetical. but you Neil Twa (22:18.518) Mm-hmm. Russell Halper (22:29.634) You also have a situation where that person is still going to go into a company generally at a relatively low-level job because they don't have experience. And there's a lot of things that you just learn from doing. And they're not things that are ever written down in a book. They're not things that are online in a case study. there's not data that always represents those things. And a lot of those things are very organizational specific. So when we talk about AGI, Neil Twa (22:41.299) Mm-hmm. Russell Halper (22:58.682) I I still wonder about the difference between being smart and being alerted. and it might be very, very intelligent, but it still needs to learn your business. I also think about somebody who's asking a question of like, Well, are operating systems needed anymore? Can you just have AI do everything for you from the beginning? And you know, it kind of becomes this question of like, okay. Neil Twa (23:06.016) Mm. Uh-huh. Russell Halper (23:28.718) How smart is AGI? Like, is it an IQ of 150? Is it infinitely smart? Is it just that's not the way you measure it? It's a whole different way of measuring it. But it's hard for me to believe that it's this, it would get to a point where it's this infinitely smart, omnipotent technology. I still think there will be constraints, even if we achieve whatever measures of AGI that are there. and at the same time, Neil Twa (23:38.657) Right. Russell Halper (24:00.516) At least right now it's not gonna tell you, you know, if you could smell something burning on the stove from the other room. Right. There's still limitations based on yeah. Neil Twa (24:08.07) No, it's gonna have some limitations. Yeah. it it is, you know, like we talked about earlier, book smart, and let's call it Street Smarts, for for simplistic terms. But the the end result is it's going to be an as an artificial general intelligence has described, a a common group of skills that are now, you know, ca it's capable of doing, as any common human could do, basically in simple terms. it now has all that knowledge combined within itself. Russell Halper (24:17.474) I like that. Yeah. Neil Twa (24:35.136) However, that doesn't mean it can tell jokes and understand nuances and social aspects or, you know, host a party or have fun or you know be a good conversational partner. but it's it's a step to some kind of super intelligence. And I know it's gonna it's gonna sound really weird, maybe even a little naive, but you know, going back to 1999 and watching The Matrix, I'm I'm almost wondering if we're not watching a documentary playing out, like or somehow you know. programming the machines that will replace us. Planned obsolescence without even realizing we're part of the plan to be obsolete in some capacity. I'm not saying it's gonna be so smart it does everything, but then again, nobody truly understands what quantum computing is and its capabilities and applying AI to that level of technology, I don't think most people comprehend. I barely even get it. And I was working with the Watson Super Blue computer at IBM and I barely understood what these guys were dealing with. at the level they were dealing with it twenty five years ago. And I I know we've come a long way since then. And I just saw recently, you know, administration is pushing quantitative you know investments and initiatives to really push that out and try to make America become the leader because I honestly think that the country that owns the the quantitative aspects of that technology across it possibly AGI, even sentient, I don't know how far this thing can go, but you know, it's it's intelligence on a level I don't think Amer we can comprehend quite yet. That's fascinating to see where it's going. Doesn't mean we're not out of you know out of a job. I don't want to be a what's the word? fatalist about opportunity at all, because I see opportunity at every level and I see it, you know, at the level you're doing. I see it at the level we're doing it at. And I think those who are listening to this are just mostly trying to figure out the balance between the two and how they fit in. and I constantly remind people, and you might agree, that the best thing you can do is Learn. what it does, learn how to use it, utilize it, apply it. Your opportunity to maybe stay in the workforce or enhance your business or start a business is gonna be, you know, somewhat determined, I think, in the next six to eight months, on just the speed at which these technologies are now implementing, you're going to have to keep up with some level of it, to find out where you're relevant in your job, especially in the, you know, coding and software development space or the knowledge working space or Neil Twa (27:00.394) Just areas where I know that AGI has a capability that, you know, as long as it's not faking the numbers and someone is there to catch it, it's it's a pretty powerful vehicle. It's actually a pretty large opportunity when you think about it. Russell Halper (27:12.537) Yeah. I mean quant quantum is a whole nother whole nother thing, right? And and it's interesting to see some of the developments in terms of like the number of qubits that we can do computation on right now. right, all the security problems that come with it. But I mean you also hear all the news about mythos, right, right now and and table five and and the challenges that are there. And and again, you hear about the the Neil Twa (27:16.374) Yep. Neil Twa (27:23.68) Yeah. Security problems, man. Neil Twa (27:32.199) Mm. Table five, yep. Neil Twa (27:36.552) Uh-huh. Russell Halper (27:40.474) The the the next generation past mythos, the successor to mythos is already under development. and so these things feel like they're only gonna scale. There's a lot of questions then about what does that mean for infrastructure and how organizations are approaching applications. and so there's there's definitely challenges, right, that are coming down the pipeline from from these things. And there might be there might be some convergence of the two. You know, NVIDIA recently released Neil Twa (27:45.142) Mm-hmm, mm-hmm. Neil Twa (27:58.369) Yeah. Yeah. Russell Halper (28:10.615) some offerings around and and some announcements around the integration of of like their technology and quantum. not as one product, but how the the the the the the the products could work together and then you know continue to evolve. Neil Twa (28:24.352) Yeah, the boxes that kind of distributed computing by people placing it in their homes and and you know, basically becoming a what I would refer to as the old school SETI grid, that uses distributed computing. Russell Halper (28:34.285) Yeah, or even even just having within the confines of a data center being able to have, you know, GPU based systems and quantity based systems working together on something, right? but it's my my sense is quantum is a little further behind than AI right now. but in itself will be a pretty important technology when it gets there. And who knows, I mean, maybe it's just one. Neil Twa (28:42.602) Yeah. It's fascinating. Russell Halper (29:02.457) Excuse pun, but like quantum leap and then and then suddenly it's relevant. You know, they release released chat GPT in what was it, twenty twenty two and suddenly the whole world's changed. Neil Twa (29:04.828) Yeah. I love that show. Neil Twa (29:14.058) It really has changed relatively fast, even the idea of MCP since 2024 and just how quickly the adoption of that is coming at the at the business level before it hits the you know everyday mainstream. We see it, I think, in Main Street because of the job losses. I mean, that's obvious these corporations are now shedding jobs, shedding people because they're optimizing those more lower level SOPs that we mentioned earlier, and that don't necessarily require humans or as many as they had before. you know, the one guy can replace four, right? so they're you know definitely seeing that come. But I think that the overarching story here is, you know, big companies are adopting it quickly. Small and mid-sized companies should be looking at how they play that game too, because there's definitely a competitive advantage for smaller companies getting involved in streamlining the processes. And you know, I've got three operators who do the work of about 15 people. and you that was not something that four years ago we could even do at that level. So we are able to create forced multipliers in within our own business that I see projecting as quite a bit farther forward than I even anticipated five years ago, what where we could get and where we can go and you know how it's gonna be managed and how we're gonna get there. It's becoming very clear how how we're gonna get there, to me anyways, which is fascinating. So I see that everybody has an opportunity to to be a part of that. if they just get out there and start playing with it. Even just understanding the terminology, if you haven't understood it today, I would encourage you to Go look it up. if you're somewhere around it and want to know more about it, obviously go do that. If you're one of the larger businesses listening to this and AI is something you're trying to figure out how to implement, you definitely want to talk with Russell and his team about how operational AI can impact your bottom line, your supply chain, and your business. is there anything else that maybe we didn't talk about today, Russell? You wanna run over with me? Russell Halper (31:06.081) it's funny because we could keep going down the path we're going down for a long time. I've lots of thoughts of things we can we can go. But I think just the the the one thing I would leave you with, and because you were just hitting on this point, is thinking about data and context. so even if you're and th this is true for for very large organizations and it's true for very small organizations, but as you're thinking about how do you build Neil Twa (31:10.304) Mm-hmm. Russell Halper (31:35.394) And if you want to really be able to leverage AI, one of the things is it needs information about what things are, it needs that context. And so as a smaller business, right, if you want to be able to leverage AI to really accelerate what you're doing, I would encourage to start with building that data and that context as part of it. because if you have all the information in one spot, if you don't tell it what it means, it's not helpful. But the most important thing I for me, I would just recommend is just people just get started. you know, play with it, try to use it for things that's evolving so fast what's out there in a couple of years may look very different than it is today. And, you know, a lot of the course material from two years ago is gonna be dated. So i it's it's you know, the right thing to do is just get after it and Neil Twa (32:11.883) Learn. Neil Twa (32:16.972) Mm. Mm-hmm, mm-hmm. Neil Twa (32:24.854) Yeah. Yeah, that's true. Russell Halper (32:31.671) you know, figure some things out on the way. And the great thing is if you get stuck on something, go ask your AI how to get past that point. It's normally pretty good at helping you troubleshoot on itself. Neil Twa (32:40.82) Just be careful what you location you ask for. You might end up at the wrong place and you know, wearing some lingerie you didn't you didn't know you were gonna end up ordering. You just never know. And that'd be the fun path. Like take it down. And it is true. I I would echo everything you said, and just remind people that, you know, to to the degree that we still have the capability, knowledge is power, and it's your opportunity to go learn, to not become a victim of change. And I see a lot of people right now, in some ways, putting their heads in the sand saying, Well, this can never impact my business and you're Russell Halper (32:50.104) Yeah. Neil Twa (33:10.678) To some degree. unless you are highly skilled in a, you know, diamond cutting. I don't even know if that can maybe be replaced with AI, but maybe it can. I know there are trades and stuff that are going to be less impacted in the shorter term, but I know that most businesses, most processes, most physical products, digital services, software as a service, or just about any kind of service that has an internet capability is impacted, will be impacted. And it's your opportunity to figure out how it is going to become your opportunity. So that you can empower yourself to do something with it and not become a victim of change when you have an opportunity to learn. And that's what you should be doing. Every day I'm learning. I don't know about you, Russell, but every I feel like, my gosh, I didn't know that. I gotta go figure that out. Or this is something I need to go do. I didn't know how to do this. Or I'm just gonna go and try this and see if I could figure it out. and I feel like I'm on a never-ending knowledge path right now that sometimes keeps me up to like 2 a.m. Cause I'm going and got running and I'm like, dang, this is fun. It's fascinating. It's an opportunity. And I would encourage everybody to do what Russell said today. Get out there and learn. Russell Halper (34:09.721) And I I would go one step further, which is when you think about hiring people, understand their aptitude to learn and their desire to learn. as you want to build your team, I I think it's important that you have a team that's able to evolve with the technology right now. And that's one of those skills you can't teach, right? You can't teach curiosity, can't teach desire to learn, can't teach ethics. Neil Twa (34:18.44) More impactful, yes. Neil Twa (34:32.156) Hundred percent tenacity. Mm-hmm. Russell Halper (34:37.975) there's certain things that are just important in the current era, and that is I think a critical one. Neil Twa (34:43.658) Yeah. Very well. I would put it as simply in my words, take a B player who's willing, tenacious, and wants to learn how to do something. I take them all day and every day, but twice on Sunday over the A player who thinks they know it all and isn't willing to learn. take that B player and train them up. So good words today, my friend. Appreciate you coming on. Some fun. All right, everybody, as always, like, comment, share, leave snipe remarks, help us with big tech, push it out there, share this with somebody who might be questioning AI and it's Russell Halper (35:05.379) For having me. Appreciate it. Neil Twa (35:15.636) implications in your business, big or small, and where it might be able to take you. as Russell has shared with us today, the knowledge and opportunity to learn is yours. Go out and get it, put it into your business, figure out your operational A AI, and remember to always question the directions. I think that's what I got out of today's call. So you guys have a great rest of your day. We'll catch you on the next episode.