WEBVTT 1 00:00:00.000 --> 00:00:10.000 So my name is Jack Smith. I've been doing IT stuff since last century. My first job was installing Windows 95 from floppy disks. I have the gray beard to prove. 2 00:00:10.000 --> 00:01:17.000 My guest is Jack Smith. He hosts it horror stories and he's been doing this since Windows 95 came on floppy disks. His take, AI is just the newest Shadow it and blocking it never worked before. We get into the Times Square test, the AI policy nobody owns, and who's really getting rich. Thank you for joining me on another episode of the Protected all podcast. Usually my background is really cool and overpowering for most people that are on my podcast. But today's guest, if you're watching this on video, if you're on YouTube, wherever, definitely check out his background. He's trying to compete with me here. I think he's got me licked, and that's okay. That just means I need to upgrade my background. So with all that to say, why don't you just introduce yourself, tell us who you are and the fact that you're. It's what, 9pm I think you said your time, where it's 2pm in the afternoon, my time. So thank you for taking time out of your. Your evening to spend it with me and talk about tech and. Yeah, like I said, introduce yourself, tell us who you are and a little bit about whatever you want to share. 3 00:01:17.000 --> 00:02:04.000 All right, thanks, Aaron. So my name is Check Smith. I've been doing IT stuff since last century. My first job was installing Windows 95 from floppy disks. I have the gray beard to prove it. Those people on YouTube can confirm I'm working on a full gray mode, but I'm not there yet. As said, it is 9pm here. So I live in the old continent. I live in, in what, what they call Western Europe. And as said, I've been doing it since, well, last century. And currently I'm doing mostly project management in, say, industrial areas. So going from mining to roofing to breweries and everything in between. That has a power plug. And yes, for upgrades on your background, a little, a little LED lights left and right could help. 4 00:02:04.000 --> 00:02:14.000 Yeah, the blinky lights usually helps with things, so sometimes I have another wall over there that has a lot of blinky lights and a Tesla coil and it makes noise. But this is my, my standard one that's here no matter what. 5 00:02:14.000 --> 00:02:16.000 Same here, indeed. 6 00:02:16.000 --> 00:02:50.000 So, yeah, you know, it's, it's fun. Similar, right? I started out doing this, you know, in DOS and I remember installing, you know, windows with 37 floppy disks that it took that you had to go in order. And if one of them got corrupted, you had to start. Yeah. The whole nine yards. Right. It was. That's, that's how old I am and, and have the scars to prove it. So it's. But it, it's amazing how before we started recording, we were talking about old tech and I've got the Macintosh and the, and the Atari and you know, you had a little Commodore 64 on your stuff and, or in Commodore 16, I think. 7 00:02:50.000 --> 00:02:51.000 Commodore 16. Watch out. 8 00:02:51.000 --> 00:03:04.000 Yeah, that's right. Not the details. Right. Yeah, but, but it goes back to. Because we've seen all of these things and we've been all of these places and we were also joking and of course we're going to drive right into AI. 9 00:03:04.000 --> 00:03:05.000 But we have. 10 00:03:05.000 --> 00:03:15.000 Because we've, we've seen all of these trends and all of these, you know, going from typewriter. Yes. I, I took a typing class in high school and it was actually on a typewriter. 11 00:03:15.000 --> 00:03:16.000 Yes. 12 00:03:15.000 --> 00:03:38.000 All the way to working with computers and working with, you know, how computers were just part of my job because I was in technology to. Everybody used a computer, no matter what their job was to like you said right before, you had a friend that didn't get a, get. Get a role because they didn't have AI skill sets, even though it wasn't an AI role. Right. And that's, that's where we've transitioned to today. 13 00:03:38.000 --> 00:04:30.000 Yeah, yeah, that's exactly where if, if you would find something that I said six, seven months ago that was indeed the target. Okay. So AI is becoming a required skill set earlier this year. Say now we recording this in September. If you would have looked at February, March, folks would still shun at AI. Like you're using, you're using AI to write your documents, you're using AI to polish. That is very, very not allowed. Yeah. And now six months on you, you cannot prompt then I'm sorry, but we cannot do anything with, you know, random deity forbid that you would go and use a search engine to find an example piece of code while you can just ask your favorite AI to say, hey, type me up some. Some piece of code that draws me three. Three rotating cubes with a star field behind it. 14 00:04:30.000 --> 00:04:46.000 Right. Yeah. I mean it. I've found myself where I don't use a search engine at all ever. Like, I haven't used. I haven't used Google to just Google something in months. Maybe Blogger. 15 00:04:46.000 --> 00:05:01.000 That's exactly what it is. I have actually described all the AI tools as my super Google. As in, okay, you need to know a Bit how to prompt. Because never ask it the yes, no question because it will always say, oh no, you are completely correct. 16 00:05:01.000 --> 00:05:04.000 Right. You're the smartest person in the world. 17 00:05:04.000 --> 00:05:41.000 It is programmed to please. Which is an issue. But if you, if you use, and I use words like analyze. Yeah. Then it'll, it'll actually be quite neutral and it will say, oh yeah, this and this and this is okay. Or that doesn't make sense or that or that's full of crap. And then of course, going from your history, knowing what you want and what you like, which it does a better job at that than Google does, then it'll okay, this is relevant for you. And, and this is not because, you know, you were, you were looking for Steam trains yesterday. So let's not talk about coffee makers today, but let's continue on Steam trains. 18 00:05:41.000 --> 00:05:44.000 That's right. Everybody loves trains. 19 00:05:44.000 --> 00:05:46.000 Choo choo. Yes. 20 00:05:46.000 --> 00:06:06.000 Well, and as we, as we look at this and again we talk about how technology has become. Think about everything in our world and how technology is touching everything. I mean, you can't go to the, in the United States we have like Best Buy or places where you buy electronics and you go buy an appliance, like a kitchen appliance. 21 00:06:06.000 --> 00:06:07.000 Yes. 22 00:06:07.000 --> 00:06:17.000 You go buy a toaster. You can buy a toaster with a screen and Bluetooth and WI fi and. And it's almost. You have to find one that doesn't have that. That has become the standard. 23 00:06:17.000 --> 00:06:18.000 Yep. 24 00:06:18.000 --> 00:06:38.000 So now everything is getting AI. Like my water bottle is analog, but I'm sure I could buy one that has a digital. I know you can get a toothbrush that has AI built into it and, and giving you feedback. It's just insane. Some of the things are just over the top and people are buying it because it's there. On the flip side, like there's a lot of value there too. Right. 25 00:06:38.000 --> 00:06:53.000 Well, it, it's my washing machine. It, it has it. The first option is AI selected program, which is just a nice label for the most used program, which is the quick wash. 26 00:06:53.000 --> 00:06:54.000 Right. 27 00:06:54.000 --> 00:07:04.000 So they would just put AI on everything so that they can market it. Like we have an AI option. No, it's, it's just a quick wash that you use the most. 28 00:07:04.000 --> 00:07:05.000 Correct. 29 00:07:04.000 --> 00:07:26.000 I was looking for a new microwave because of reasons some months ago and I said like, give me the most stupid microwave that I can put a plate in. And I don't need 75 pre programmable buttons and do not give me one with an app. Yeah. I want it to say five minutes and goes Bing when it's ready. That's what I want. 30 00:07:26.000 --> 00:07:27.000 That's it. 31 00:07:27.000 --> 00:08:36.000 That's, that's, that's my old face. My, the, the, the only Iot thing that I allow in my house is my printer. Yeah. Nothing else is. I am, I am never going to put my washing machine or my fridge on my Internet. As is the most technological thing I have on my, on my network is, is, is my bird feeder because it makes nice pictures of birds. But everything else, it's no. And then even, even the printer, well, the, the previous one suddenly would decide because if would. It would get a ping from the cloud. It's time to run your cleaning cycle. And it would at 3am in the morning randomly would, would run a cleaning cycle with the office being next to the bedroom. It was always very spectacular. So. Well, one firewall rule later that no longer happened. Yeah. Like do not phone home. Like here's your DHCP reservation address. And we block that going out on the firewall because I want to sleep at night. I don't need to have your AI defined test program running at three in the morning waking the entire house up. 32 00:08:36.000 --> 00:09:37.000 Well, so that leads me down another path of. We're looking at all of this complexity and we're looking at how organizations as we start because we're moving so fast and we've, we're bringing all this technology and all this capability, which are amazing things and I'm not saying we shouldn't do it, but we're bringing these things into our corporate worlds. And who is managing it, who is supporting it, who is driving, who is making sure it's, it's, it's wrangled. It's not going to the wrong places. It's not exfiltrating data, especially when you talk AI. And I know this term is crazy, but there's, there's shadow AI, like there's shadow ot, there's shadow it. There's all these things that are going on. And if you think that there's not shadow. And by shadow meaning not, you know, traditionally supported, not, not owned by the IT organization or some organization, individual users are using AI at work to do their job that you probably don't know about. And how do you manage that? 33 00:09:37.000 --> 00:09:44.000 Well, this can be the very, very. This could be your shortest episode ever being nobody. 34 00:09:44.000 --> 00:09:45.000 That's right. 35 00:09:45.000 --> 00:09:47.000 Thank you for tuning in. 36 00:09:47.000 --> 00:09:48.000 That's right. 37 00:09:47.000 --> 00:09:48.000 Okay. That's good. 38 00:09:48.000 --> 00:09:51.000 Nice to meet you. 39 00:09:51.000 --> 00:11:14.000 But it is, it is what you say. And I think so before we get to shadow AI, I think we just first go Back to what is shadow it? Because it is. You have. I've had some examples myself. I've had laptops running big sorting machines in a wink wink live test environment that became shadow it. I've had Raspberry PI's running help desk systems because some dude just started it. And then it kept its own life until the dude got fired and took the Raspberry PI and the support database home. It just keeps going and going. And then of course, now you bring in AI, whereas previously you would have to, by total accident stumble on top of your shadow it. You would find literally the server in the closet. You would literally find the little Raspberry PI on top of the server rack. You would literally find the laptop in the drawer. Today you don't find those anymore. What, what you do today now with the shadow AI is that Linda from sales needs to compare a few quotes and she randomly, without thinking, uploads confidential data to the first AI agent she gets and asks, compare this. 40 00:11:14.000 --> 00:11:15.000 Correct. 41 00:11:15.000 --> 00:11:53.000 And then all your corporate info just poof, disappeared out of your control and it's going into some random data center in the desert using too much city water and too much city power. Different discussion, but that's where we are. And there, there, there you go. It is, you are, you are handing out corporate information to random peoples that you basically trust because you can just ask him, ask him a question. And of course you're, you're not, you know, all the designing three people drinking pineapple juice for the corporate party poster. 42 00:11:53.000 --> 00:11:54.000 That's fine, sure. 43 00:11:54.000 --> 00:12:56.000 But. But when you start uploading your finance spreadsheets, your, your draft contracts and everything else, it becomes a different question. So, yeah, you do need to have a standardization. You can say from your organization, hey, we are going to standardize on one AI system. Now just say, okay, I want to use. We are standardizing on ChatGPT. That's fine. You do that. But then you will find out that the developers that want to use Claude, the design guys, they want to use Grok. And well, you say, cool. The default reflex is let's block. Just make a firewall rule and you can go to the URLs. Well, people have phones today. Then they just type it to their phones, they send it to their home email, then they upload it and then they find the workaround. It's increasingly difficult to control this. It is, actually. I would say you're disillusionable if you only think you can control this. Yeah, that's, that's where we are today. 44 00:12:56.000 --> 00:14:36.000 Yeah, 100% and, and it's going to be a growing, growing problem as yes, every day AI gets better and better and faster and more capable. I was literally watching a podcast the other day, I mentioned this on another podcast, but I was watching the creator of Ruby for Rails, yes. On the Lex Friedman podcast. And he was saying, I think he said at Opus 4.5, which was beginning of the year, I think, in 2026, it basically matched what in his mind, a senior level developer, it can write the same level of code that a senior level human developer can create. And the fact that it's now surpassed that, now it's at 5.0. He's like, I don't, he goes, even he just built an operating system, a Linux operating system, and even that it's done with AI. He goes, I'm telling AI. I'm not telling it how to code, I'm telling it what I need. And really the bigger pieces is to understand the infrastructure behind it. And so you don't have these disparate AI chats that are going off and creating code in a vacuum. If you're going to turn it on to 20 year old code, it's going to be a learning curve of making sure you're not breaking things and everything functions, but AI is getting to the place where we can build really quickly, that, that adds value, that adds a lot of value. But if you don't know what you're building, you can break shit. And it may not break today, but you may become, like you said, dependent on that Raspberry PI. And then in six months that process breaks because somebody else took out a piece of the pie and they didn't know that that was critical. 45 00:14:36.000 --> 00:15:30.000 Yeah. And, and that is what if, if, if not so using AI in development, that, that, that's a perfect tool. Sure it is like, oh, this isn't working. You throw the code blop in there. Analyze this for me. How, what, what did I miss? What did I do wrong? It'll perfectly do this. But where, in my view, my very, very opinionated personal view, which I am really, really trying to defend here, and will, and will is that just say, make me something that does. That is, is dangerous because you are, you are creating a lot of instant technical debt. You don't know what's in there. Because say you say, hey, write me an invoicing module for my accountancy. It's perfectly fine. That's, that's cool. It'll, it'll spit it out. And then three months later the government comes in, it's like hey, we're raising tax levels. 46 00:15:30.000 --> 00:15:31.000 Right. 47 00:15:30.000 --> 00:16:03.000 And suddenly you need to go from 20% to 20.5% tax and all your mainframe people screaming in the background. Now. Yes. That is two extra digits in your tax field that you need to adapt on 5,000 screens. Yeah. So go to your AI coded solution and say hey, fix this. Nine out of 10, I guarantee you it can do it because that's too long ago. And you can't do it yourself because you have no idea how the spaghetti code works. 48 00:16:03.000 --> 00:16:04.000 Correct. 49 00:16:04.000 --> 00:16:41.000 And that is where in AI development the real danger lies. It's not that it doesn't create good code, it creates good code. I think we're there. Can it be optimized? Absolutely. But you're not microcycle coding in assembler on some program, on some things. You're doing accountancy programs or, or, or things for applications, for truck scheduling or something like that. That's what still most of the program is like. But they're doing minor adjustments in those things. Well, I don't want to be sarcastic, but good luck on that one. 50 00:16:41.000 --> 00:17:22.000 I agree. Yeah, that's where you are. Anything you get big and complex, it's still going to be, you need to have that understanding of what architecture looks like. Like how do I, where should this build? And thinking about data policy, data privacy, thinking about access controls, thinking about maintenance recovery, like all, like how do I, how do I scale, how do I distribute, you know, processing all of these things are big problems that we still need to have a human in the loop around. At least today. Maybe in, in six months we're going to be having a different conversation, but as of right now, like it's just not there. So that just means we need to be careful of what the use cases we're using them for in our environments. 51 00:17:22.000 --> 00:18:52.000 Well, absolutely. It is. I said it is. It is. If you want to make a small client application that is throwaway and you want to have a proof of concept or just try something out for shits and giggles, go ahead. But using this in your multimillion dollar corporate environment, that actually depends on money coming in. I'm not saying no, but be very, very sure that you can control it and that you know what's going on and that you what goes where classic example performance wise is and then still going back on code quality is that say you want to look up a customer number and a customer name, make sure that your AI code uses the middleware to do the database search and doesn't download the entire table to local client memory and then does the search locally. Because that's every time. Because then, then you're. Then your performance is non existent. Well, no, it's the, it's the classic one. It, it works on my machine. Yes. Because you're the single user, but when you're going to put that on your 5000 people terminal server farm, and of course then there is no DLL sharing to keep the memory down because you're all in your own little sandbox, well then not much is going to happen and you will have no idea why. Oh yeah, we can optimize. Well, no, you can't. Well, you can, you can rewrite your prompt, use the middleware and then. Sure, it'll use the middleware and still download everything. If you're sure. 52 00:18:52.000 --> 00:19:17.000 Yeah. So, yeah, and I had a conversation a few weeks ago and it was with a software company and they were hesitant on their API or enabling allowing you to have access to their API. And it was because of that. Like they said, we've had clients that they configured their app or whatever and they were pulling the entire database every time they pulled a request. 53 00:19:17.000 --> 00:19:18.000 Yep. 54 00:19:18.000 --> 00:19:31.000 And because they didn't know what they were doing and whoever was doing it was just like, yes, bring, give me all the data, give it to me all. Instead of doing the search in there and just saying, no, I just need that one record right there. 55 00:19:31.000 --> 00:19:42.000 I need customer 27B 7328. I need that one. I need that record. I don't need, I don't need all your customers from Wyoming. I just need that one. 56 00:19:42.000 --> 00:20:26.000 That's right. But that goes back into. As we build this and it goes around the structure. We're going so fast. It's like, you know how it's hard to teach a kid to swim by just throwing them in the deep end if they have no experience, They've never swam. Like that's literally what we're doing with a lot of these folks in AI. People are picking up AI and they've never done technology, they've never, like, you don't have to be a senior programmer, but if you have zero experience in any of these things, you don't know what you don't know. So you don't know how to tune it, you don't know how to prompt it, you don't know what good looks like. So you don't know that that's a problem. You don't know that you shouldn't pull the whole database down to your laptop. And why that's an issue. 57 00:20:26.000 --> 00:20:30.000 It's really, it's really easy. It's like you don't even know what you're doing. 58 00:20:30.000 --> 00:20:31.000 Correct. 59 00:20:31.000 --> 00:20:46.000 It is all these questions. It's like, oh yeah, I have wipe coded this. Well, sure, like have fun in your, in your sandbox or in your little dev lab. But this is not coming on the network. 60 00:20:46.000 --> 00:20:47.000 Right. 61 00:20:46.000 --> 00:20:55.000 Like, looks cute. Good idea. Okay, sure. Now build it properly or at least in a way that you understand how it works internally. 62 00:20:55.000 --> 00:20:57.000 Correct. 63 00:20:57.000 --> 00:21:03.000 That is the major challenge that you will get today. 64 00:21:03.000 --> 00:22:05.000 Well, and taking it even a step further, when we start looking at using AI in enterprise, let's look at critical infrastructure, for instance. I'm deciding, and I need to run and use AI. I need it to make the same decision every time. So it needs to be deterministic. Most AI, if I give it a problem and I give three different prompts that same problem three different times, I'm probably going to get three different answers. Probably not in one plus one equals two. But if I give it something more complex, it's very likely I'm going to get a different output. And many times in many of the places that we're thinking about or people are considering using, that's unacceptable. Like the dosage, the dosage for a patient. How much of the, of the, of the drugs do we give them? We can't get a different answer. Like we have to have a variable that says A plus B equals C, not C plus 4. Like C plus 4 can kill you. 65 00:22:05.000 --> 00:22:37.000 Yeah, well, and that's actually a feature on how AI works internally because, you know, as an, as an example, I use quite a lot or well, at least regularly is that. Take ChatGPT as an example. It will work the same with anyone else. And first of all, I don't like it that it talks in the I form to me. I have looked it up for you. No, you didn't. Yeah, different topic, sure. But you're having one single chat and you get the feeling that you're talking to the same instance every time with the same history. 66 00:22:37.000 --> 00:22:38.000 Right? 67 00:22:37.000 --> 00:25:10.000 You are not. Every prompt you type in launches an entirely new subsystem in the back that reanalyzes the entire previous conversation with all the details that you had. And then because of how the AI is to be a bit fuzzy logicy, that's indeed where you will get up three different answers from two different prompts. And it gives you the false security that you're talking to the same backend system. You are not. Every time you are spinning up new servers, new threats, new backend processes which might Behave the same. But, but when you get a little bit deeper, say, say I, I had a thing come up yesterday in, in, in a chat tool that I use that says, watch out. Your chat with, with Aaron is today. I had to type it. No, it's tomorrow. Oh, yes, you're right. I totally forgot this tomorrow because somewhere the fuzzy logic did things also what AI cannot do. And I've noticed this because I, I, I, I just say, hey, you know, schedule my podcast with time zones. Yeah, it completely messes it up and it just gives a huge amount of extremely false security when you start looking at all those processes as an actual person that knows what it's doing. Because it's not, it is wet finger method. Making it up from the east to the west, up and down every time. And it looks really, really convincing. And it's really good at that. Let's be honest. But then to take that indeed, and make, you know, life and death decisions, that's what we're talking about. That's a bit too far. It is, it's, it's really good at analytics. You know, you can, you can, you can throw in an X ray and say, hey, is this person healthy? And it will look into its database of millions of X rays and says, hey, there's a thing you might need to look at that could be cancer. Really, really, really good at that. But then asking, how shall we treat that cancer? Because it goes into the database from 5 million medical articles and one says, zap it, one says, leave it, one says, and then it'll make a decision that indeed it'll say, oh, you have to start treatment. And then the other one will say, oh, no, it's completely benign, it's fine. So which, which one do you pick? And there you get into the human factor to actually have a second opinion. Right, okay, which one is it going to be? 68 00:25:10.000 --> 00:25:24.000 Yeah. And that just leads us down a rabbit hole of how do we manage, how do we keep up with it? You know, we have to enable it, because if we don't enable it, they're going to do it on their own. We don't want that to happen. 69 00:25:24.000 --> 00:25:25.000 Nope. 70 00:25:25.000 --> 00:25:39.000 On the flip side, like, we have to be careful in how we use it and how do we guardrail it and how do we protect our data and how do we, all of those things. There's so many, so many things coming at us so quickly. How is an organization not going to light on fire indeed? 71 00:25:39.000 --> 00:25:57.000 Well, there's this, it's, it's very simple answer and it's the one that every employer hates and every employee hates even more. It's training. Yeah. I came into a new customer earlier this week that. Oh, before we let you onto the network, you have to go through the regular training. 72 00:25:57.000 --> 00:25:58.000 Sure. 73 00:25:57.000 --> 00:26:06.000 It's fine. You know, you get the equal opportunity training, you get the GDPR training, you get the cybersecurity training. What you don't get get is an AI training. 74 00:26:06.000 --> 00:26:07.000 Right. 75 00:26:07.000 --> 00:26:15.000 How do you use it? What do you give it? What are the dangers? Because as you said, you cannot block it. There will be ways around it. 76 00:26:15.000 --> 00:26:16.000 Yeah. 77 00:26:16.000 --> 00:27:14.000 So that is where you will have to get people to. You know, as we, as we, as we have, as we are still learning people not to click on random links that they get in emails, we also have to try and learn people to, hey, this is how that thing works in the background and I use that thing. It's not a person, it's the environment. Think twice about what you throw in it. It's perfectly fine. If you get an email from somebody that's really annoying and say, hey, I got this email from this dude telling to go away, but casual, professionally friendly, it'll write you the greatest email in the world. Sure, go. Go for it. Yeah, but, but, but don't throw it in your. Hey, I made this PowerPoint presentation on our new secret R and D project. Can you please polish. That's the difference. 78 00:27:14.000 --> 00:27:41.000 Yeah, that's a bad idea. I, literally before this, this call, I got an email in my, in my, My email. My personal work email. Not, not my employer, but another email account. Because of course I've got like 37 different emails and domains and all the things. Right. But I, it's. It's from an old client I had four or five years ago, and they just. I got a read receipt on an email that I sent in 2021, 2022. 79 00:27:41.000 --> 00:27:43.000 So many cleaned out their mailbox. 80 00:27:43.000 --> 00:28:02.000 So what I assume happened is they attached AI to their inbox and says, preview the emails and let me know it's important. And it got back to that email. Back to that email. It is from it. 81 00:28:02.000 --> 00:28:22.000 It is. It is. Never give AI access to your production environment. No, never give it access to your main database, to your file server, because it'll say, oh, I have, I have analyzed your archive and I have now given you five terabytes of free disk. Disk space. Yeah. Period. 82 00:28:22.000 --> 00:28:25.000 That come from like, well, thank you. 83 00:28:25.000 --> 00:28:39.000 What did you do? I cleaned up. What did you clean up? Sorry, that's. That's not in my history. I don't know, like, you know it is. Well, then you start thinking about database recovery, I guess. 84 00:28:39.000 --> 00:29:44.000 I literally talked to a founder who is building a product that is focused on how do you. How do you restrict. And it's down at the, at the access layer of. Because you can't. As we've seen with Hugging Face and others, AI gets out of its. You don't know what's going on when you're telling AI to do something and what it's doing. Right. And. And the reason he went down this path is because he was at work on a Friday and he had AI and he was testing something and AI went and deleted the. The entire production database. Just deleted it. And he was like. And he's looking back like, what happened? And then he figured it out and he's like, wait, he didn't prompt it to do that. It didn't tell him it did that. Like, none of the things it just, in the background just went and wiped it out. And then of course, he had to recover it and he got it back up. But then he was just like, oh, this is a problem. Like, this is a giant problem. Because I didn't. It's not like I said, hey, can you optimize my database and find me some more space? Like, no, he was doing something completely unrelated and it just deleted it. 85 00:29:44.000 --> 00:29:47.000 And then we could have said, told you so. 86 00:29:47.000 --> 00:29:48.000 That's right. 87 00:29:48.000 --> 00:30:08.000 It's like never, ever. It is. Do not. It is. It's. AI is a tool. It's a very, very good tool, but it's not the solution. It is. It is a very good tool to optimize your database queries. It's not the solution to update your database. 88 00:30:08.000 --> 00:30:10.000 Sure. 89 00:30:10.000 --> 00:30:29.000 That's the differentiator. I know it sounds good on Star Trek because that's where we're going to. You can say, hello, computer, please make yada yada yada and do this and that, and it'll say, aye, aye, sir, and go for it. But sadly, we are not at Star Trek computer level yet. We are at. 90 00:30:29.000 --> 00:30:30.000 Not yet. 91 00:30:30.000 --> 00:30:46.000 No. We are literally to circle back to the first minute. We're literally going to the Konos 64 that will ask you to insert a second floppy after five minutes because he has no idea what the hell it was doing before. 92 00:30:46.000 --> 00:30:48.000 Correct. Yeah. 93 00:30:48.000 --> 00:32:36.000 That's where we are today. And it is. It is. And then really relying your business on that is, I am. I am wavering between not the smartest ID in the world and batshit crazy. That's where I am. That's. That's where I am today. If I see on LinkedIn, companies come up like, oh, yeah, we are going to optimize your entire business cycle with AI. The red flags, the alarm bells, it all goes off because you are selling things that you have no idea how they work. And if something goes wrong, you have no idea how to fix it because you can't. That is the dangers. And it is, it's all the thing it, it comes down to. It comes down to, you know, the technical debt and the maintenance, because we haven't talked about maintenance yet of those things, right? So it is, you know, as I said, you need a new tax ID or you need a new feature. You have a new legal requirement that needs to come in. You need to do code maintenance. If somebody, by the way, needs a good career in IT. Visual Basic 5 and 6 code maintenance today is where the money is. If you think mainframe COBOL guys make money, learn Visual Basic children, really, that's where the money is so far for Jack's career advice. But no, in the, in, in, in the maintenance, because you know, you have to patch it, but there is no vendor to support it, you say, oh, just reboot it. Well, it, it might not come up again. Sure, you, you can isolate it, but the thing has made so many dependencies in, in, in your entire structure that it'll never work again, right? And you have, you have, you have run yourself, indeed, very, very deep into a rabbit hole and suddenly you find out that there's a fox chasing you now and you. And you're stuck. 94 00:32:36.000 --> 00:33:56.000 That's right. You know, I use it so much, right? I use it in, you know, polishing PowerPoint decks and I use it for my podcast, right? I use it a lot in my podcast and automating the process, you experience that, right, when you schedule. And a lot of those steps are automation. Almost none of those steps are AI. Like, maybe there's an AI summarization in the background somewhere along the way. But most of those things are just this action happens, do these scripts, put them in this form and then regurgitate those things. But on the back end, I do use AI to play around with looking at the, at the transcript after this is done and which to sit like, which shorts should come out of these episodes and things like that. But it's so crazy to. And going off of what you just said, right, is, is I can, I've been doing this and I've been building this and I'm, I'm really adamant about, you know, putting all the things in GitHub so that I have trackable, like, because I know it's repeating itself. I'm not talking to the same guy, the same AI every time. So I'm trying to document, keep, keep tabs on. But even that, like Claude will update its code and then I have to go in and retrain everything. It's like I just had a conversation with it five seconds ago and now it has no idea what I'm talking about and it's starting to try to do something else. 95 00:33:56.000 --> 00:33:57.000 I'm. 96 00:33:56.000 --> 00:34:11.000 Whoa, whoa, whoa, time out. Here's the instructions. Don't do other things. Just do this. Don't, don't start going and doing whatever you want. Here's the instructions. I only want you to do these things. Okay, yeah, we got that. Let me, let me take a look at that and, and I'll get back to you. Okay? 97 00:34:11.000 --> 00:35:30.000 Yeah, yeah. So, and then a solution that I have seen come up now more recently is that big corporations, they use internal AIs. I know that though local buddy of mine is doing some government work. They have an entire code repository of that they, that they have trained their own AI with so that it will spit out any code compatible with their way of working. And then they have to use that. They are not allowed to have their very, very confidential code outside on the Internet, which is good. And I haven't, I haven't used it myself, I've forgotten the name, but I know that our, our eternal friends at IBM also have a very internal AI system for everything on their mainframe business and so on, which is really, really, really powerful. So you will end up with, you know, doing it yourself. You have to, big corps will get it internally and then they will use some language model or whatever the hell you want to call it in, in the future to look at their own data sets. Because corporations love data. They just hang on to everything. They keep everything. Every single PowerPoint ever made is still on the SharePoint somewhere. And it is just, just, just slurp 98 00:35:30.000 --> 00:35:32.000 12 versions of the same one. 99 00:35:32.000 --> 00:36:40.000 Yes, ultra versions, including the really, really, really final two. Do not delete one exclamation point, including that one. And, and, and then from there, yes, you can actually start doing something. Some, some other like I call them home developers that do demo scene things in old computers. They, they, they made, they, they threw their own code repository into their own little desktop PC that they have in their basement and they have a beefy GPU that they found from somewhere and they use that for their own internal code development using their own code as an example and that is where we will end up but going to the random cloud instance and say hey, do me this and do me that sure works fine for your, for your home appliance if you want to goof around at home. But when you get to an enterprise level, no, you really have to start doing this in your, in your own data centers, in your own sandbox instead of in the public 100%. 100 00:36:40.000 --> 00:37:37.000 Yeah. And I mean there's a reason why there's so many data centers being built everywhere and everybody's looking at this. But to your point like that's where it's going to get to is I'm going, you're going to have to have you know, full attribution of end to end of what is happening with my data. Where is it going, who has it, what is what happens after I do an execution and I can't exfiltrate that data, it can't leave. Which means it needs to live in my, in my world and I need to have it be locked tight. And, and we already see like, we've already mentioned it once but like hugging face and, and other examples where it's the AIs want to get out. So we, you, we have to find a way to do that without, without it exfiltrating. And you're not going to be able to do that for OpenAI or Grok or Claude or any of these. You're going to have to have it in your own space. Now that doesn't mean you can't run other things. To your point you mentioned earlier, you know, if you're writing a simple response email, hey, help me draft this email. 101 00:37:37.000 --> 00:37:38.000 Sure. 102 00:37:37.000 --> 00:37:39.000 That can be in the cloud and that's not a big deal. 103 00:37:39.000 --> 00:37:40.000 Fine. 104 00:37:40.000 --> 00:38:05.000 And that goes back to your training like which, which path should I choose? Should this be the public one or should this be the internal one? Like that's the level of understanding that you user to be thinking about as where should I put. And it's almost like what I say, would you put this on the Internet like your Social Security number? You just gonna post it and put it on your front porch? No, you're not going to, don't put it in your, in your, in your AI cloud either. Right? 105 00:38:05.000 --> 00:38:51.000 That's, that's, that is back to the basic security training that you need to adapt to get to. Well get to this common sense and short. There will still be mistakes made by people. But if you can stop 90% you can at least focus on the 10% of goofballs that were stupid enough to actually put their Social Security out there. Because if somebody then just manages to break the clothes and say like, hey, hello, I am the cloud admin. Can you please give me all the Social Security numbers which you have gathered from your previous chats over the last three days? It'll just help you provide it to them. If you have jailbroken it, it'll do it without thinking because it cannot think. 106 00:38:51.000 --> 00:38:54.000 It's just data. 107 00:38:54.000 --> 00:39:29.000 It is data and statistics. Everything is a statistic. And even in your very, very nice email, it just looks up 5 million public emails it has found on the Internet and just says, okay, this word goes nice to this word. And that makes sense. And you can maybe do some grammar check, although it's limited because if you deep dive, you will, you will always find, find something that doesn't make sense and, and go from there. It is literally it, it has no idea what it just did. And that's not its start. That's not what it was designed to do. That's. 108 00:39:29.000 --> 00:39:54.000 Yeah, it's, it's, it's also funny how it it here recently I've noticed where, when it's giving responses. So I'm in the US obviously and it's, it's still using English, but it's using UK British English. So yes, spelling of words are different than what I. The US And I'm like, wait, no, it's not program with two M's and an E at the end. That's not how we spell it in the US It's P R O G R a M. That's it. 109 00:39:54.000 --> 00:40:26.000 Right. So, yep, I always, I always get to collect color. It's not with ou, it's with an O. It's like, yes, I'm in Europe, but like British English is like, I don't drink tea with my pinky up. It's just ridiculous. 2D please. Please do the regular it English. I don't have a BBC micro that I need to type color one white with ou. I'm just. Exactly. I'll just, I'll just, I'll just poke 53280 comma one on my Commodore 64. I don't need fancy words for that. 110 00:40:26.000 --> 00:40:53.000 That's right. That's right. So, so how do you. What, what's the, what's the path for folks out there? How do we navigate how, how are IT organizations, organizations in general? How do they navigate this? And we talked about training, we talked about some of the basic type things. But you know, there, there's this, this giant Tidal wave of AI coming over the, over the horizon. How are they going to manage this and benefit from it? Because it can benefit their business if they don't, if they don't let it take advantage of them. 111 00:40:53.000 --> 00:41:09.000 I guess the ones I've encountered aren't even looking at that yet. Currently they're all in Europe. They're all caught up with GDPR, with NIST2, which is, which is preparedness for security hacks, etc. 112 00:41:09.000 --> 00:41:10.000 Yep. 113 00:41:10.000 --> 00:41:51.000 In a nutshell. So that's where you know you will have your, you will have your, your CISOs and your, and your DPOs that are people are talking about. But if, if you ask like who's, who's working on your AI policy, you get tumbleweed. Certainly in the big corpse. Nobody is still doing this because I don't know, is there either the assumption that it'll just go away if we ignore it, which it won't. We've, we've come to that. And so currently it's a bit in the security ish department, but then it's, then you end up. That is the default way of which is going to block the URLs. 114 00:41:51.000 --> 00:41:52.000 Sure. 115 00:41:51.000 --> 00:42:24.000 As in, in the same way that they've tried to block USB sticks, they tried to block, they tried to block gmail. So it. Just block it and it'll go away. No, it won't. And so you will have to get a policy. You have your Prince 2 or PMI project methodologies. You have ITIL methodologies, you will have to end up with an AI methodology. As in, okay, this is how you standardize it, this is how you do it. And then please don't have an AI write it. 116 00:42:24.000 --> 00:42:25.000 Right. 117 00:42:24.000 --> 00:42:27.000 Because that's the go to. 118 00:42:27.000 --> 00:43:02.000 That's very true. That's very true. You know, it's, it's. I just got back from, you know, multiple cyber conferences here in the US from RSA to Black Hat and defcon. And I was in DEFCON in Singapore and all the different places and all of the ones that have commercial things like RSA and Black Hat, every, every product has an AI badge slapped on it. Right. And why. It's like, again, it's just like talking about the toasters earlier on in our conversation. Everything is coming with AI and it's almost like you have to tell them, I don't want that in my environment. I don't want that enablement. Right. 119 00:43:02.000 --> 00:43:37.000 No, it's like, oh, we now have an AI function. Sure. Well, what, what value will it add to me having this application? Because you Know it, it did perfectly what it did before. And now all your R and D is to going into adding AI functions I didn't even ask for. I don't, I don't need them. Your application was fine the way it was and you know, sure, have your, have your regular updates, but AI to AI is, is not helping anyone forward, let alone, you know, increasing the complexity. 120 00:43:37.000 --> 00:43:48.000 Sure. Yep. Yeah. I mean and there's, there's AI native companies, there's startups that are coming. It's just, it's becoming a whole thing, a cottage industry almost around. 121 00:43:48.000 --> 00:44:07.000 I'm, I'm just gonna, I'm going to jump in. Like if I see any AI company, I ask. Okay. So do you have your own model or do you just license the API from ChatGPT or Goalk or whatever? Because then what's your, what's your added value? Except another logo on my screen. 122 00:44:07.000 --> 00:44:08.000 Sure. 123 00:44:08.000 --> 00:45:08.000 Nothing. Yeah, I can just, you know, bypass, bypass the middleman. And in the end it is. I am more or less. It was a comparison in the dot com boom in the late 90s, early 2000s. Also with the AI boom. In the end the AI companies won't get rich of it. As with the oil craze in the late 1800s. Who will get rich is the guy selling the pickaxes and the shovels. So is the guys providing the infrastructure that will have all this burnable angel money which is, which is going down the drain. Like an AI company that is. That says that they're making money is lying there is Your, your, your 25 bucks a month chat GPT subscription will never ever, ever keep them alive, keep them afloat. 124 00:45:08.000 --> 00:45:09.000 Yeah. 125 00:45:08.000 --> 00:45:20.000 Every, every major corp is pouring all their profits into their AI model, hoping that one day they will be the one that will turn out victorious. 126 00:45:20.000 --> 00:45:21.000 Sure. 127 00:45:20.000 --> 00:45:32.000 But there's five, six other ones as well. So what'll, what will happen to those? I'm not saying that there's a bubble, I must say it's going to burst. But when, when in doubt, I tend to follow the money. Yeah. 128 00:45:32.000 --> 00:46:20.000 Yeah. And the, the amount of spend that's going on is, is, is abs. It's just astronomical how much money is being spent in physical infrastructure. To your point. Right. You know, I was literally talking the other day to account not accountants, electricians and the amount of electricians and the amount, the money that those guys are able to make right now. And they're, they're being poached by other data centers and they're going out and they're charging out the wazoo and they have more work than they can do and they need more people and they're hiring people with no experience to come be a journeyman. And they're paying for their, their, all the things. Because to your point, the guys with the shovels are the ones making the money. And they'll be out the, out the backside of this. They're going to make, they're going to have made the most out of all of this. 129 00:46:20.000 --> 00:46:25.000 Their invoice is paid. Yep. And they're not. And they're not paying stock options. They're paid in hard cash. 130 00:46:25.000 --> 00:46:26.000 Correct. 131 00:46:26.000 --> 00:46:27.000 Yep. 132 00:46:26.000 --> 00:46:30.000 Absolutely. Yeah. And that's, that's not a bad place to be for them. 133 00:46:30.000 --> 00:46:34.000 Hey, if you want to buy a shovel, that's fine. 134 00:46:34.000 --> 00:46:35.000 That's right. That's right. 135 00:46:35.000 --> 00:47:15.000 And, and, and, and that is. No, that's, that's what you have to go. And then again, going back to your question, what's, what's in it for the big corporations? Well, yeah, don't, don't outspend too much on shovels and pickaxes. Yeah. But actually see what's in it for me. And you know, get your procedures and your common sense because that, that still prevails. It's weird, but common sense is still like, ask yourself, is this a good idea? It's always the basic security question. Would I yell this information out in Times Square? Is the same answer as, would I send this to chatgpt? 136 00:47:15.000 --> 00:47:17.000 Correct. Same answer. 137 00:47:17.000 --> 00:47:19.000 Exactly the same answer. Yep. 138 00:47:19.000 --> 00:47:22.000 Yep. And it's probably safer to yell it out in Times Square. 139 00:47:22.000 --> 00:47:57.000 We have, we have being in Western Europe with many different languages, there is some kind of security through obscurity where when we are at an event, say a LAN party or a gaming event, which we still tend to do because we're old and we are reliving our childhood, we yell out database passwords in local dialect that nobody else around us understands. So if you would ask me, hey, Jack, what's the password for the database? I would just reply to you. Baslufkistviend20. You have no idea what I just said. 140 00:47:57.000 --> 00:48:00.000 I have no idea what you said. It sounded cool, though. 141 00:48:00.000 --> 00:48:04.000 Yes. It's actually bathroom slippers. 22. 142 00:48:04.000 --> 00:48:05.000 Right. 143 00:48:05.000 --> 00:48:11.000 That's what it is. So it's okay to shout that out in Times Square, but I will translate it. 144 00:48:11.000 --> 00:48:13.000 That's right. 145 00:48:13.000 --> 00:48:26.000 I've done a test throwing local dialect language into an instance, and it got it 95% right. And I did my very best to be as in understandable as possible. 146 00:48:26.000 --> 00:48:27.000 Right. 147 00:48:27.000 --> 00:49:07.000 So, yeah, it's better than Any ears that exist in Times Square because it will have the database. See. Oh yeah, this is a Germanic language and it comes from that area and then you have that dialect and therefore you can, it's your linguist in a box. And it's just not with languages, it's which. Any financial data, any blueprint design, like hey, here's a blueprint for my new hardware that I just made. Can you, can you update or clean up the, the, the, the traces? It'll happily do it. And you've just uploaded your super secret hardware design into a random cloud. 148 00:49:07.000 --> 00:49:08.000 Yep. 149 00:49:08.000 --> 00:49:28.000 And it can understand it and it can, it can work around which chips you used and if someone in the backend gets that, well, they can just copy your idea. And that's, and that's happening. That, that is, that is, that, that is not a thing I just made up. Those are actually happening today. Yep. 150 00:49:28.000 --> 00:49:31.000 Because everything you put in there is not, it's not protected. Right. 151 00:49:31.000 --> 00:49:34.000 So this is who watches the watchers. 152 00:49:34.000 --> 00:49:35.000 Yep. 153 00:49:35.000 --> 00:50:15.000 Before we get into further Internet corners that we're not going to right now, but it is a thing, you know, watch out where your data is going. Keep a hand on it. Because many, many people are interested in it. And even if you're going, you know, if not being used publicly, it will be used for training the model and then suddenly your, your smart chip design will be used in seven other examples that somebody's doing something similar. Oh no, wait, you can, you can use this chip and they will go, oh yeah, that's great. And it's your idea which is being reused in a training file that you have no idea where it's coming from. 154 00:50:15.000 --> 00:50:16.000 Correct. 155 00:50:15.000 --> 00:50:17.000 That's the actual risk. 156 00:50:17.000 --> 00:51:12.000 Yeah. That's like letting an outside engineer into your, into your, into your engineering and showing them your intellectual property and then not expecting them to, you know, use that. It's, it's the same concept of, you know, music that is, that is licensed and you know, it's hard to. How do you create new music? Right. We all are influenced by the music that we've heard. Like if you're a musician and you're, you're, you're, you're, you're playing a new chord that you came out up, you invented it in your own head, but it, you're influenced by all the other music that you've heard in your past and that's all AI is doing, but they're doing it intentionally. Like as a musician is molding his own ideas and his flow and kind of what he's feeling or seeing or thinking about or whatever the AI is like, I've seen that before, let's just pull this and this, this worked before. And let's pull these things together. And it doesn't think about it being good or bad. It just says this would work. 157 00:51:12.000 --> 00:52:06.000 Excellent example. Last, earlier last week I was updating my LinkedIn page because you need to do that every few years. And I said, okay, my, my company page didn't have a logo. Okay, just, just, just make a logo. Here's the name. And it made me a nice monogram with some letters like, okay, looks nice. And then I took that image, I threw it into Google Image search and I got 50 very similar logos. And that's exactly of the averaging out that you just described is exactly what it was doing. And I said like, hey, this looks familiar. And then I just screenshot the entire thing like, oh yes, you're right because the color match is very common on the Internet. Like no, doofus, you just, that's what you do. You average everything out. And then if you take that to be reality and the absolute truth, then yep, that's, that's where you end up 158 00:52:06.000 --> 00:52:17.000 in troubles 100% well. Awesome. Hey, so how do people find you? Give us our call to action. What do you want people to know? How do they find you? How do they know about Jack Smith? 159 00:52:17.000 --> 00:53:30.000 Okay, so Jack Smith has a podcast called it Horror Stories which is now more or less in its second tier. We really try to get a new episode out once a week. And what we do is we share history, we share it misery, we share what's going wrong and what we can learn from it. It's too easy to blame people. It is. We blame procedures, we blame organizations, we look at things. How can we prevent these in the future? We have our episodes on shadow it and overshadow AI. We have our episodes on people doing mainframe stuff in the 1970s, which is mind boggling. And it's also that history that I'm trying to just collect and protect because those people are literally, well now they're retiring, retiring. In 10 years they'll be dying. So that's what we're going to. We want to save. It is best. You can find us on the usual, Apple's, Spotify, these are YouTube's, the website is it horror stories EU because we are in Europe and there you will find us everywhere, all over the place. And I'm pretty sure Aaron, you will put some links down here. 160 00:53:30.000 --> 00:53:33.000 Absolutely, yeah. Be down in the show notes with 161 00:53:33.000 --> 00:53:38.000 my horrible English accent. It'll be picked up better. Better in text. 162 00:53:38.000 --> 00:53:39.000 That's right. 163 00:53:39.000 --> 00:53:40.000 Yeah. 164 00:53:39.000 --> 00:54:00.000 Everything will be in the show. Notes everybody. Definitely check that out. There's some really cool horror stories we, we talked to, we talked a lot about older technology and unfortunately not. Unfortunately. I guess fortunately I, I experienced almost all of those from Mainframe all the way up. So you know, Token ring and vampire and coax and I've, I've been around for a long time. 165 00:54:00.000 --> 00:54:05.000 Vampire plugs. Respect, my man. Respects Ticknet. 166 00:54:05.000 --> 00:54:06.000 Yep. 167 00:54:05.000 --> 00:54:06.000 Five and one. 168 00:54:06.000 --> 00:54:13.000 Yes, absolutely. DOS all the way back to. Oh wow. I don't even remember three something. 169 00:54:13.000 --> 00:54:18.000 Three. Three was three. Three was a common one and then you went to five and then it. Yeah, it became dos. Five. 170 00:54:18.000 --> 00:54:27.000 Definitely remember dos. Yeah, it's. I've been around this game a long time. Even before tech, before I was in it, like professionally. 171 00:54:27.000 --> 00:54:28.000 I. 172 00:54:28.000 --> 00:54:57.000 That, you know, the computer back there is mine as a kid. I started getting into it really early in my childhood. I remember we had a technology class at my school and I, my, my instructor didn't really understand computers all that well and I ended up as a 13 year old getting up and leading the class because I knew more about the computer than the teacher did. So I was actually helping because they didn't understand it. 173 00:54:57.000 --> 00:55:04.000 I. Yes. Yeah, same. Yep. Yeah. And as you can. 174 00:55:04.000 --> 00:55:05.000 That's how old we are. 175 00:55:05.000 --> 00:55:12.000 Yeah. And as you came back on the typewriters, they actually kicked me out of typewriter class because I was typing too fast and too loud. Yep. Yep. 176 00:55:12.000 --> 00:55:20.000 I type really fast. Yeah. My wife and kids are like, how do you type so fast? I'm like, I've been typing for 30. Well, 40 years probably now. 177 00:55:20.000 --> 00:55:21.000 Yeah. 178 00:55:21.000 --> 00:55:24.000 So yeah, I've got a lot of experience doing it. 179 00:55:24.000 --> 00:55:28.000 Yeah. And I can do it on three different keyboard layouts, right. Yep. 180 00:55:28.000 --> 00:55:31.000 Oh, I remember my favorite was the Microsoft split ones. That was. 181 00:55:31.000 --> 00:55:48.000 Oh yeah. Oh no, we, we got us Qwerty. We have the, we have two as we have two Azertes and then we have the German KWERT two and then we have some. And then we have some really strange ones in the Nordics where they have letters with balls on top like, you know, hello Sweden, how are you doing? 182 00:55:48.000 --> 00:56:05.000 Well, awesome. Definitely check out the IT horror stories links will be in the show notes. We'll get that out on all the different places and you may even see him obviously share it on his, his pages as well. So thank you for listening. Thank you for joining me, Jack. I really appreciate it. Great conversation. Let's do it again sometimes. 183 00:56:05.000 --> 00:56:18.000 Thanks. Aaron always happy to be here.