WEBVTT 1 00:00:01.919 --> 00:00:05.799 Welcome everyone to another episode of Adventures and Devlops. Joining 2 00:00:05.799 --> 00:00:08.560 the in studio today, Warren Broad Warren, how. 3 00:00:08.519 --> 00:00:11.240 Are you thanks for having me back. You know, I 4 00:00:11.240 --> 00:00:14.119 actually have a good fact for today that I thought 5 00:00:14.279 --> 00:00:17.079 was really interesting to share. There's a malware out there 6 00:00:17.120 --> 00:00:21.719 called potter Cookie, and I know the economy or engineers 7 00:00:21.879 --> 00:00:24.879 is not so great at the moment. However, a lot 8 00:00:24.920 --> 00:00:28.160 of advertisements out there for a job recks, maybe from 9 00:00:28.199 --> 00:00:31.559 malicious attackers who are trying to get you to run 10 00:00:32.359 --> 00:00:35.280 GitHub repos or download packages from the internet to pass 11 00:00:35.320 --> 00:00:38.520 the interview, and those things will get installed on your 12 00:00:38.520 --> 00:00:41.679 machine and either try to steal your local crypto wallets 13 00:00:41.759 --> 00:00:45.759 or worse, be used for attacking whichever company you do 14 00:00:45.799 --> 00:00:49.000 get hired by. So I know it's a real struggle 15 00:00:49.039 --> 00:00:52.039 that you want to complete whatever take home assignment or 16 00:00:52.079 --> 00:00:53.759 you know, get the next job. But you really got 17 00:00:53.799 --> 00:00:56.840 to be careful in today's economy because this tech is 18 00:00:56.880 --> 00:00:59.920 out there that's just waiting to capitalize on a supple mistake. 19 00:01:01.200 --> 00:01:05.319 That's just nuts, Like let's just kick someone while they're 20 00:01:05.359 --> 00:01:09.439 down right, Yeah, Like all of that is nuts. 21 00:01:09.519 --> 00:01:12.840 I think getting homework from an interview is nuts. I 22 00:01:12.879 --> 00:01:17.319 think you know, potentially installing something on your computer that's 23 00:01:17.359 --> 00:01:19.879 going to make your computer go wild. It's not like 24 00:01:20.040 --> 00:01:22.400 it's just it's multiple levels of crazy. 25 00:01:23.920 --> 00:01:25.920 Speaking which, Hi, Jillian, welcome to the show. 26 00:01:27.560 --> 00:01:28.640 Thanks for having me back. 27 00:01:29.760 --> 00:01:31.719 You guys are making me feel guilty. Thanks for having 28 00:01:31.719 --> 00:01:34.680 me back. Like it's conditional with this point, just show up. 29 00:01:36.239 --> 00:01:37.719 Oh I don't know. I'm not sure that it is 30 00:01:37.760 --> 00:01:39.560 for me, but no, thank you. 31 00:01:40.120 --> 00:01:42.359 I'm still appreciative for being here. I guess that is 32 00:01:42.400 --> 00:01:43.040 what I'll say. 33 00:01:43.359 --> 00:01:45.359 Well, I'm happy to have you both here. You guys 34 00:01:45.359 --> 00:01:48.519 make my job a lot more fun and entertaining. And 35 00:01:48.560 --> 00:01:52.120 speaking of fun and entertainment, I'm looking forward to this episode. 36 00:01:52.560 --> 00:01:55.560 We have Alex Kernes joining this in a studio today, 37 00:01:56.200 --> 00:02:00.000 principal solution architect from dude, you just told me how 38 00:02:00.079 --> 00:02:02.280 to say this in my mind already went below. 39 00:02:04.719 --> 00:02:08.400 I've heard every every possible permutation of how to pronounce 40 00:02:08.400 --> 00:02:11.639 it myname correctly, which a lot of people don't do. 41 00:02:13.319 --> 00:02:15.639 Welcome to the show, man, I'm happy to have you here. 42 00:02:16.159 --> 00:02:16.800 Great to be here. 43 00:02:16.800 --> 00:02:17.120 Thank you. 44 00:02:17.560 --> 00:02:19.479 Cool. So give us a little bit about your background 45 00:02:19.680 --> 00:02:23.039 now that we know how to pronounce for your work. 46 00:02:23.919 --> 00:02:28.960 Yeah, great, I mean, I come from a software engineering background, 47 00:02:28.960 --> 00:02:31.479 if you kind of go way back to the post 48 00:02:31.599 --> 00:02:37.039 university career and then made the move into intercloud both 49 00:02:37.319 --> 00:02:42.639 kind of internal consultancy and of platform type work and 50 00:02:42.680 --> 00:02:49.560 then consultancy in terms of the traditional customer external facing consultancy. 51 00:02:49.599 --> 00:02:54.520 But I'm still very technically driven. I like my hands dirty. 52 00:02:54.840 --> 00:02:59.599 That's that for me. Is is what's the most exciting part. 53 00:02:59.680 --> 00:03:05.680 It's building things, breaking things, learning from it. Yeah, payper 54 00:03:05.759 --> 00:03:09.919 architecture is is not my not my phone. 55 00:03:10.719 --> 00:03:13.520 I hear you there. There's like a certain I think 56 00:03:13.560 --> 00:03:16.800 for people who succeed in this industry for a long time, 57 00:03:16.800 --> 00:03:19.759 there's a certain amount of entertainment value that you get 58 00:03:19.840 --> 00:03:20.639 from your job. 59 00:03:22.240 --> 00:03:27.439 I'm definitely doing it wrong then, I mean I I 60 00:03:27.800 --> 00:03:31.000 my new belief is that when I retire, I'm just 61 00:03:31.039 --> 00:03:34.560 gonna go back into drawing boxes and lines. Like that's 62 00:03:34.639 --> 00:03:36.280 just like the best part of my job. Like when 63 00:03:36.280 --> 00:03:38.639 I can get on a piece of paper or whiteboard 64 00:03:38.960 --> 00:03:42.159 and boxes and lines, you know, not even any words necessarily, 65 00:03:42.240 --> 00:03:45.159 Like that's prime enjoyment right there. 66 00:03:45.360 --> 00:03:47.199 I'm gonna get my typewriter and I'm gonna go to 67 00:03:47.199 --> 00:03:49.400 my cabin in Montana. Screw all you guys. 68 00:03:51.120 --> 00:03:52.960 So so speaking of just like I'm going to do 69 00:03:53.000 --> 00:03:55.240 something really simple and turn my brain off, I bought 70 00:03:55.280 --> 00:03:57.680 like a paint by Numbers kit and that has been. 71 00:03:58.120 --> 00:03:59.919 It just reminded me a lot of what you said, 72 00:04:00.039 --> 00:04:01.879 because I just sit there and I just paint in 73 00:04:01.919 --> 00:04:05.199 the numbers and I don't care that I'm nearly forty 74 00:04:05.240 --> 00:04:08.719 and doing an adult like an activity for children. It's great, 75 00:04:08.800 --> 00:04:11.000 and it's just so you just turn your brain. It's great. 76 00:04:11.039 --> 00:04:11.400 It's great. 77 00:04:11.439 --> 00:04:14.960 Anyways, I've actually heard quite a few people comment how 78 00:04:15.120 --> 00:04:17.360 like therapeutic and relaxing that is. 79 00:04:17.879 --> 00:04:20.519 They really are. They're very relaxing. It's great. 80 00:04:21.680 --> 00:04:26.079 I think so many things. It's the tech industry. Brains 81 00:04:26.079 --> 00:04:29.399 are so switched on all the time. There has to 82 00:04:29.399 --> 00:04:34.160 be a way to switch off otherwise work life balances 83 00:04:34.800 --> 00:04:35.600 it's pretty nil. 84 00:04:36.120 --> 00:04:38.600 Well, it's interesting you bring that up, because actually, very 85 00:04:38.639 --> 00:04:42.680 commonly where you keep being in a always production mode, 86 00:04:42.720 --> 00:04:48.160 like everything we do is happening at a critical level 87 00:04:48.199 --> 00:04:50.600 and we have to pass that test. Like whatever work 88 00:04:50.600 --> 00:04:53.879 we're doing, there's no practice involved. It's always run time 89 00:04:53.920 --> 00:04:56.600 for us. And there's a lot that there's a bunch 90 00:04:56.600 --> 00:04:58.439 of research out there that says like we have to 91 00:04:58.519 --> 00:05:01.879 go into practice mode where mistakes can be made, failures 92 00:05:01.879 --> 00:05:04.680 can be had, and we can actually learn from it intentionally, 93 00:05:05.160 --> 00:05:07.160 and without that we will like that's actually one of 94 00:05:07.160 --> 00:05:09.720 the biggest causes of burnout. So you know, if it's 95 00:05:09.759 --> 00:05:13.079 going home and doing watercolor, water painting, you know, whatever 96 00:05:13.120 --> 00:05:16.680 it takes there. If that somehow helps you recharge realistically, 97 00:05:17.000 --> 00:05:17.759 definitely do it. 98 00:05:18.319 --> 00:05:21.920 There's also a lot of evidence showing that taking on 99 00:05:21.959 --> 00:05:25.480 those kind of activities, while you're not consciously thinking about 100 00:05:25.519 --> 00:05:28.839 the problem, your subconscious is continuing to work on it, 101 00:05:28.839 --> 00:05:31.480 and that's when some of the big insights and big 102 00:05:31.519 --> 00:05:34.360 breakthroughs for you occur. Like I know, there's a really 103 00:05:34.360 --> 00:05:36.560 common antidote of like when you're in the shower and 104 00:05:36.600 --> 00:05:40.000 you have this great idea that's like a really fixed 105 00:05:40.040 --> 00:05:42.040 example that whole process in action. 106 00:05:44.759 --> 00:05:48.759 True, do two important jobs anymore, Like I'm just I'm 107 00:05:48.800 --> 00:05:52.120 just not doing them, but like I mean, like they matter, 108 00:05:52.199 --> 00:05:54.639 but not like not on like a huge I'm always 109 00:05:54.680 --> 00:05:58.480 in production. Everything has to be perfect, Like it's fine, 110 00:05:58.600 --> 00:06:00.600 it'll it'll be fine if it's it's not a couple 111 00:06:00.600 --> 00:06:02.879 of days later. I used to do important stuff though, 112 00:06:03.079 --> 00:06:06.839 and I don't want to anymore. So that's I suppose 113 00:06:06.879 --> 00:06:08.639 that's your lesson is this is where I'm at doing 114 00:06:08.639 --> 00:06:10.720 my paint. Fine numbers and things don't really matter. 115 00:06:13.639 --> 00:06:15.439 Well, one of the things we were talking about before 116 00:06:15.519 --> 00:06:22.000 we started recording the episode was leveraging jen AI and 117 00:06:22.040 --> 00:06:24.759 Alex you've got some experience with that, specifically some experience 118 00:06:24.800 --> 00:06:29.600 of like real world examples where you've done that, and 119 00:06:29.639 --> 00:06:32.439 I think that's one of the big I think that's 120 00:06:32.439 --> 00:06:34.519 one of the cool things about AI. You know, it 121 00:06:34.560 --> 00:06:38.240 goes through this this buzz cycle, but people who are 122 00:06:38.240 --> 00:06:40.959 actually putting it to real world use. I'm interested to 123 00:06:41.000 --> 00:06:42.360 hear your take on that. 124 00:06:43.600 --> 00:06:45.959 Yeah, I think it's a it's a really it's a 125 00:06:46.000 --> 00:06:50.439 really interesting topic. It's it's something where you go back 126 00:06:51.600 --> 00:06:58.399 eighteen months, maybe chat GPT was kind of just about 127 00:06:58.439 --> 00:07:03.879 starting to be established as almost household name. People aren't 128 00:07:03.920 --> 00:07:09.120 necessarily using it actively, but tools like that are becoming 129 00:07:09.680 --> 00:07:14.399 more and more common. And then I think with with 130 00:07:14.519 --> 00:07:18.319 any technology, it's it's when it gets kind of democratized, 131 00:07:18.360 --> 00:07:21.959 when it gets put in the hands of people that 132 00:07:22.000 --> 00:07:27.839 aren't having to spend millions on hardware and and do 133 00:07:27.920 --> 00:07:31.360 those kind of things that it actually really starts to 134 00:07:31.399 --> 00:07:36.160 become an awful lot more prevalent. So I think as 135 00:07:36.560 --> 00:07:40.560 we saw with any technology, So you think back to 136 00:07:42.160 --> 00:07:46.040 kind of mid twenty tens, I suppose, where things like 137 00:07:46.079 --> 00:07:50.639 AWS Lander came out, so kind of serveralus technologies, and 138 00:07:50.680 --> 00:07:55.920 then the years after that where every SaaS company that 139 00:07:56.120 --> 00:07:59.600 existed was going for a we now have a server 140 00:07:59.639 --> 00:08:03.839 off ring, and it's like, what is it serverless? Is 141 00:08:03.879 --> 00:08:06.399 it just managed service? 142 00:08:06.519 --> 00:08:07.839 Is it? 143 00:08:07.879 --> 00:08:11.000 What is your what is your definition of servers? 144 00:08:11.319 --> 00:08:11.560 Right? 145 00:08:12.959 --> 00:08:15.519 So you see the buzz around that. You see buzz 146 00:08:15.639 --> 00:08:21.079 around even Cloud, which is I mean cloud is public 147 00:08:21.079 --> 00:08:23.720 Cloud twenty years old if you if you go back 148 00:08:23.720 --> 00:08:28.079 to Aws's first service, so it's not it's not new, 149 00:08:28.160 --> 00:08:32.879 it's not shiny anymore. And AI, I think is going 150 00:08:32.879 --> 00:08:35.919 through the same thing, but just at a much much 151 00:08:35.960 --> 00:08:36.600 faster pace. 152 00:08:37.120 --> 00:08:40.759 So well's a really interesting comparison though, Like I just 153 00:08:40.840 --> 00:08:42.440 I want to stop here for a second there because 154 00:08:43.879 --> 00:08:46.639 I feel like there's a sort of a weird duality 155 00:08:46.679 --> 00:08:50.080 where serverleists made it easier for people to get into 156 00:08:50.480 --> 00:08:53.799 building stuff and releasing applications because it didn't require you 157 00:08:53.879 --> 00:08:57.559 to purchase or allocate huge data center capacity in order 158 00:08:57.559 --> 00:09:00.360 to make that happen. I feel like the where where 159 00:09:00.399 --> 00:09:04.000 the point is AI is currently at is it actually 160 00:09:04.000 --> 00:09:09.080 does require only the most expensive access to hardware or 161 00:09:09.240 --> 00:09:11.120 service providers to be able to get that. So I 162 00:09:11.120 --> 00:09:14.000 don't think like I don't know if it's been democratized yet. 163 00:09:14.320 --> 00:09:15.960 I mean, there's a lot of services out there that 164 00:09:16.240 --> 00:09:20.039 claim to get you access to some facet of AI, 165 00:09:20.120 --> 00:09:23.159 and I know there's like chtgbts and lms out there 166 00:09:23.200 --> 00:09:26.919 that questionable how much value they were turning to you. 167 00:09:27.480 --> 00:09:29.799 But I think that like the real core aspect of 168 00:09:30.080 --> 00:09:33.600 being able to provide the underlying resources or technology to people, 169 00:09:33.639 --> 00:09:35.799 I think it's still much too far away. 170 00:09:36.639 --> 00:09:38.399 I think the way you described it is great. It's 171 00:09:39.279 --> 00:09:44.360 giving people access to a facet of AI. I mean, 172 00:09:44.399 --> 00:09:49.440 if we think about AI as a general topic, artificial 173 00:09:49.480 --> 00:09:55.200 intelligence more broadly has been around for decades. It's only 174 00:09:55.279 --> 00:09:58.960 really that when you sort of stop breaking it further 175 00:09:59.039 --> 00:10:04.120 down into machine learning and deep learning and now generative 176 00:10:04.120 --> 00:10:08.000 AI is a kind of subset of that that the 177 00:10:08.360 --> 00:10:13.720 generative AI and the AI terminologies are now almost interchangeable. 178 00:10:14.759 --> 00:10:19.320 Certainly from an industry perspective, I think it very much 179 00:10:19.360 --> 00:10:22.279 depends on what what people are wanting to do with 180 00:10:22.399 --> 00:10:28.000 AI and how how specific their use case is. So 181 00:10:28.919 --> 00:10:34.360 if we think about things like like chat GPT, that's 182 00:10:34.360 --> 00:10:37.679 obviously a very specific use case. It gives you very 183 00:10:37.960 --> 00:10:42.480 generic responses to things. It hasn't got access to your 184 00:10:43.919 --> 00:10:49.559 your specific business data. But it's free and not with 185 00:10:49.600 --> 00:10:52.879 any any free product you are you are normally the product. 186 00:10:54.799 --> 00:10:56.840 Of course you can you can opt out of things, 187 00:10:56.879 --> 00:11:01.120 but by default it's it's collecting that chat history to 188 00:11:01.200 --> 00:11:05.480 improve the service for everyone. You've then got things like 189 00:11:07.120 --> 00:11:14.279 Amazon Bedrock, so Bedrock with aws's generative AI offering that 190 00:11:14.320 --> 00:11:20.440 came out at their conference twenty twenty three that was announced. 191 00:11:20.960 --> 00:11:26.039 So Bedrock offers kind of two different modalities I suppose 192 00:11:26.240 --> 00:11:28.720 in how you can use it. One is on demand 193 00:11:30.240 --> 00:11:34.480 where you pay per thousand tokens. So that's the way 194 00:11:34.519 --> 00:11:37.480 where you can you can go and build something. Again, 195 00:11:37.519 --> 00:11:42.039 it's similar to chat GPT in its sense of its 196 00:11:42.240 --> 00:11:45.679 generic knowledge. It's whatever the large language model has been 197 00:11:45.679 --> 00:11:50.679 trained on. But because as with any kind of cloud 198 00:11:50.720 --> 00:11:55.039 hosted managed service, they can take advantage of economies of 199 00:11:55.039 --> 00:11:59.000 scale and give you pay as you go pricing the 200 00:11:59.000 --> 00:12:02.559 moment that you want to fine tune that model or 201 00:12:03.200 --> 00:12:06.480 train a different model with your specific data. You're going 202 00:12:06.480 --> 00:12:09.480 from paying kind of fractions of a cent per thousand 203 00:12:09.559 --> 00:12:14.360 tokens to having to commit to thirty thousand for three 204 00:12:14.360 --> 00:12:17.480 months because you are now the one that's bearing the 205 00:12:17.480 --> 00:12:21.000 cost of all of that hosting rather than to AWS 206 00:12:21.080 --> 00:12:25.240 making it available. And of course there are ways that 207 00:12:25.279 --> 00:12:29.200 you can augment the use of large language models with 208 00:12:29.240 --> 00:12:33.120 your own data without going to that extent. So even 209 00:12:33.240 --> 00:12:38.080 just including examples of your specific data in a prompt 210 00:12:39.159 --> 00:12:44.399 or of retrieval augmented generation where you can load your 211 00:12:44.440 --> 00:12:48.840 own documents into a vector database and have it retrieve 212 00:12:48.879 --> 00:12:52.919 that data from there, there's lots of ways you can 213 00:12:53.000 --> 00:12:57.279 kind of get quite a long way without spending huge 214 00:12:57.279 --> 00:13:01.080 amounts of money. At the moment you want to get 215 00:13:01.120 --> 00:13:05.399 to the I have complete control of my model, I 216 00:13:05.480 --> 00:13:10.159 train it with my specific data, then you'd hope that 217 00:13:10.159 --> 00:13:12.919 that's when you start getting to the the type of 218 00:13:12.960 --> 00:13:16.399 customers who can afford to spend that kind of money. 219 00:13:16.919 --> 00:13:20.080 So, in your capacity at your current job, where you're 220 00:13:20.120 --> 00:13:23.320 interfacing with clients and whatnot, do you find there is 221 00:13:23.360 --> 00:13:26.440 one particular provider or one set of tools that you're 222 00:13:26.440 --> 00:13:30.840 constantly going to or whole breadth of set, but for 223 00:13:30.960 --> 00:13:34.720 different types of tasks to help assesdo yes. 224 00:13:34.840 --> 00:13:39.320 I think we are an AWS consultancy, so everything tends 225 00:13:39.320 --> 00:13:43.960 to center around Amazon tools. But of course as you 226 00:13:44.120 --> 00:13:47.399 or in Google both have have AI offerings. 227 00:13:47.080 --> 00:13:48.000 Now as well. 228 00:13:49.639 --> 00:13:54.440 Or the Microsoft with their investment into open ai, are 229 00:13:54.519 --> 00:13:57.679 the only provider that the offer the open AI models 230 00:13:58.039 --> 00:14:00.919 on the public cloud, and I think that will stay 231 00:14:00.960 --> 00:14:04.960 the same for for a number of years. In terms 232 00:14:05.039 --> 00:14:08.440 of the tools that we reach to, there's definitely a 233 00:14:08.440 --> 00:14:13.559 combination of kind of vendor specific but also open source tools. 234 00:14:13.919 --> 00:14:19.600 So in terms of hosting large language models, the kind 235 00:14:19.600 --> 00:14:23.039 of the most frictionless way to access them is through 236 00:14:23.240 --> 00:14:29.200 Amazon Bedrock, really really straightforward API, easy to write scripts 237 00:14:29.240 --> 00:14:35.200 to interact with that either synchronous asynchronous chat however you 238 00:14:35.240 --> 00:14:38.159 need to. But then you can start to bring in 239 00:14:38.639 --> 00:14:42.799 open source tools, so things like lang chain is a 240 00:14:42.840 --> 00:14:49.320 really popular open source framework where you can use Bedrock, 241 00:14:49.399 --> 00:14:54.679 you can use Microsoft's hosted models, Google open Ai. However 242 00:14:54.720 --> 00:14:58.360 you need to interact with your your large language models 243 00:14:58.720 --> 00:15:02.919 and then bring in those other parts like retrieve augmented 244 00:15:03.360 --> 00:15:07.039 generation where you can say I've got a database full 245 00:15:07.080 --> 00:15:10.200 of full of documents these are my business documents, my 246 00:15:10.360 --> 00:15:15.360 sales reports, my financial reports, whatever they need to be. 247 00:15:15.919 --> 00:15:20.200 And then when your large language model takes your prompt, 248 00:15:20.559 --> 00:15:24.080 it can then use that data that you've provided without 249 00:15:24.080 --> 00:15:28.080 having to specifically train the model to augment the generation 250 00:15:28.120 --> 00:15:32.120 of its response. And there's lots of open source tools 251 00:15:32.159 --> 00:15:36.679 that can do things like that. I think what will 252 00:15:36.720 --> 00:15:41.039 be really interesting as these kind of I want to 253 00:15:41.080 --> 00:15:43.759 say years, but I think it's going to be months 254 00:15:43.440 --> 00:15:46.240 with how things are going at the moment. As the 255 00:15:46.279 --> 00:15:50.799 next few months go by, so many open source packages 256 00:15:50.799 --> 00:15:55.039 are popping up, but it's how these open source packages 257 00:15:55.840 --> 00:15:57.320 stay around long term. 258 00:15:57.480 --> 00:15:58.799 So it's. 259 00:16:00.120 --> 00:16:04.600 Unless they are backed by by a big business what 260 00:16:04.679 --> 00:16:12.519 makes them commercially sustainable. I think we've seen seen frameworks 261 00:16:12.639 --> 00:16:17.240 like crew ai, which is a framework for building AI 262 00:16:17.639 --> 00:16:21.759 agents and multiple agents and kind of orchestrating like what 263 00:16:21.919 --> 00:16:24.840 agent would get called for a particular type of task. 264 00:16:25.720 --> 00:16:30.039 They've now introduced to a commercial model where they can 265 00:16:30.080 --> 00:16:35.600 take on some of the management and observability around those agents, 266 00:16:35.720 --> 00:16:38.240 or you can just use the framework open source. 267 00:16:39.039 --> 00:16:42.159 So I feel like I want to ask about that. 268 00:16:42.440 --> 00:16:46.480 Do you you're supporting your customers in utilizing AI within 269 00:16:46.519 --> 00:16:50.720 their businesses. Have you seen significant change? Say over I mean, 270 00:16:50.960 --> 00:16:54.519 I think things are changing very frequently in a month period, 271 00:16:54.600 --> 00:16:58.360 so you know, compared to you know, early twenty twenty 272 00:16:58.360 --> 00:17:02.960 three to now, Like what's the next thing, Like what 273 00:17:03.000 --> 00:17:07.200 are customers now most interested in utilizing? Is it one 274 00:17:07.200 --> 00:17:10.119 of the particular providers more so than others. Is it 275 00:17:10.240 --> 00:17:13.720 just a smattering of everything or do you really see 276 00:17:14.000 --> 00:17:17.359 something taking off specifically in the businesses that you're working with. 277 00:17:18.160 --> 00:17:21.160 So I think it's it's worth worth making it kind 278 00:17:21.160 --> 00:17:24.680 of provide a agnostic and thinking more use case and 279 00:17:24.839 --> 00:17:30.279 drivers for use of AI. So thinking back twelve months, 280 00:17:31.440 --> 00:17:37.279 even twenty four months, there was a lot I think 281 00:17:37.319 --> 00:17:42.480 there was a lot more of the people wanting to 282 00:17:42.599 --> 00:17:47.119 use AI for the sake of using AI. So we've 283 00:17:47.119 --> 00:17:51.960 had conversations with customers who have said, my board member 284 00:17:52.319 --> 00:17:54.680 has said, as a business, we've got to be using 285 00:17:54.680 --> 00:17:58.160 AI because investors wanted to hear, public needs to see it. 286 00:17:59.279 --> 00:18:02.039 Can you help as USI It's like, well, of course, 287 00:18:02.160 --> 00:18:05.160 but let's let's take that step back. Let's try and 288 00:18:05.160 --> 00:18:08.519 work out where there is a genuine use case. I 289 00:18:08.519 --> 00:18:12.880 think we've been kind of getting through. If we use 290 00:18:12.920 --> 00:18:17.200 the gardener hype cycle as a framework, I suppose here 291 00:18:17.319 --> 00:18:22.119 where I think we've we've definitely passed that peak of 292 00:18:23.839 --> 00:18:27.319 inflated expectations, that kind of top of the top of 293 00:18:27.319 --> 00:18:31.519 the hype hype cycle where everyone is using AI for 294 00:18:31.559 --> 00:18:34.880 the sake of using AI. People want to do way 295 00:18:34.920 --> 00:18:40.720 more with it than is really feasible and ethical, sustainable everything, 296 00:18:41.799 --> 00:18:46.279 And we now I think we're starting to quite rapidly 297 00:18:46.319 --> 00:18:50.119 get into that point of what the hype cycle terms 298 00:18:50.119 --> 00:18:56.119 as the trough of disillusionment, where people are thinking, I'm 299 00:18:56.160 --> 00:19:02.240 seeing AI so much every service, every tool, every news article. 300 00:19:02.440 --> 00:19:06.000 I mean even in the in the UK today our 301 00:19:06.039 --> 00:19:08.880 Prime Minister came out and announced a big plan for 302 00:19:08.880 --> 00:19:11.880 for rolling out AI and growth programs across the country. 303 00:19:12.440 --> 00:19:16.079 So it's it's gone from just the those big tech 304 00:19:16.160 --> 00:19:22.119 providers to to government politics to everything. It's dominates everywhere, 305 00:19:24.279 --> 00:19:26.640 and I think it's almost sort of starting to see 306 00:19:26.640 --> 00:19:29.839 a little bit of fatigue in companies where it's a 307 00:19:31.440 --> 00:19:34.519 it is a case of every vendor is talking to 308 00:19:34.559 --> 00:19:40.480 us about AI or their latest AI powered offering, and 309 00:19:40.599 --> 00:19:44.000 the that next stage, which I don't think we're far 310 00:19:44.039 --> 00:19:49.960 away from now, is working out and really being visible 311 00:19:50.400 --> 00:19:54.720 the use cases for AI that are here to stick around. 312 00:19:55.039 --> 00:19:58.279 So the one yeah, no, no, I totally get it. 313 00:19:58.400 --> 00:20:01.599 I mean it is quite in the it's everywhere, as 314 00:20:01.599 --> 00:20:03.839 you said, and I'm I sort of want to ask 315 00:20:03.839 --> 00:20:08.279 our resident mL expert here, you know, what she's seen, 316 00:20:08.480 --> 00:20:11.039 you know, comparative to with what you brought up. I 317 00:20:11.079 --> 00:20:12.319 know she loves to talk about it. 318 00:20:13.079 --> 00:20:16.039 I love AI. I think I think AI is very cool. 319 00:20:16.079 --> 00:20:19.680 I'm still really seeing people on the upward side of 320 00:20:19.720 --> 00:20:21.960 the hype cycle. Like I have a small AI service 321 00:20:21.960 --> 00:20:24.200 that I offer. I had to stop offering it like 322 00:20:24.279 --> 00:20:26.960 publicly because people were just coming in with these very 323 00:20:27.000 --> 00:20:29.599 outsized expectations. And now I'm like, okay, we have to 324 00:20:29.880 --> 00:20:31.759 we have to schedule like a ten to fifteen minute 325 00:20:31.759 --> 00:20:34.559 talk first so that I can like, you know, adjust 326 00:20:34.599 --> 00:20:39.000 some of these expectations and things. But besides that, I 327 00:20:39.000 --> 00:20:42.160 think it's very It's great if you're using it kind 328 00:20:42.200 --> 00:20:45.240 of for what it's good at, and then it's terrible 329 00:20:45.440 --> 00:20:48.480 if you're not. Like you know, I think probably a 330 00:20:48.519 --> 00:20:50.640 lot of the public policy stuff might be a little 331 00:20:50.640 --> 00:20:54.119 bit maybe a little bit like outsized in terms of 332 00:20:54.119 --> 00:20:55.920 what it can do. But maybe you know, but maybe 333 00:20:55.960 --> 00:20:58.119 people making these kind of requests and just being like, well, 334 00:20:58.160 --> 00:21:00.319 shouldn't it be doing this? That's probably what's going to 335 00:21:00.359 --> 00:21:03.119 drive innovation forward. So I have kind of I have 336 00:21:03.240 --> 00:21:04.599 kind of like mixed feelings about it. 337 00:21:04.599 --> 00:21:10.200 I guess, well, I think that's there's like I want 338 00:21:10.200 --> 00:21:12.200 to drill in on that for a second, like you're 339 00:21:12.240 --> 00:21:15.680 having the right expectations for it. Alex and Jillian and 340 00:21:15.720 --> 00:21:20.200 y'are both brought that up. What are some of the 341 00:21:20.240 --> 00:21:26.200 good use cases where you've seen AI really make a difference, Alex. 342 00:21:27.799 --> 00:21:30.319 So I can talk about one one kind of specific 343 00:21:30.359 --> 00:21:36.160 customer use case where as part of a migration, there 344 00:21:36.279 --> 00:21:43.039 was we had two hundred and fifty PHP crown jobs 345 00:21:43.119 --> 00:21:45.440 the rebunning on a non premises server. 346 00:21:46.240 --> 00:21:50.519 Now, these were some of these were. 347 00:21:52.920 --> 00:21:57.519 Been years old, kind of dating back to of PHP 348 00:21:58.240 --> 00:22:02.039 early PHP five and PHP four with some of them 349 00:22:03.000 --> 00:22:06.559 and some of them were they were little scripts. Some 350 00:22:06.599 --> 00:22:09.240 of them were fifty lines, some of them were four 351 00:22:09.279 --> 00:22:11.920 or five hundred lines. And then you have the ones 352 00:22:11.960 --> 00:22:17.079 that import from different files. And we kind of took 353 00:22:17.119 --> 00:22:20.000 the view of, actually, is there a way we can 354 00:22:20.400 --> 00:22:23.359 can do something here to speed up the analysis of 355 00:22:24.039 --> 00:22:26.759 these scripts. There are a few things that we need 356 00:22:26.799 --> 00:22:30.000 to know, we need to know what databases does this 357 00:22:30.039 --> 00:22:34.400 script talk to? Does it interact with any other services 358 00:22:34.480 --> 00:22:40.680 so APIs, does it interact with things like in the 359 00:22:40.720 --> 00:22:43.759 case of the on premises servers, things like the send 360 00:22:43.799 --> 00:22:50.279 mail binary on a server, because this particular customer for 361 00:22:50.319 --> 00:22:54.480 the rest of their business used a managed SMTP service 362 00:22:54.519 --> 00:22:59.119 now and not just sending emails from on premises servers. 363 00:23:00.920 --> 00:23:03.400 So we did a bit of a proof of concept 364 00:23:03.440 --> 00:23:07.640 around well, primarily just the way to speed up our 365 00:23:07.680 --> 00:23:13.240 own analysis, because yeah, I mean, nobody wants to read through. 366 00:23:14.920 --> 00:23:15.720 Line by line. 367 00:23:16.359 --> 00:23:18.119 No, say, it's not so. 368 00:23:18.279 --> 00:23:20.480 Well, I mean, if you're looking at just as an example, 369 00:23:20.559 --> 00:23:23.680 you know, you said accessing particular database, right, Like if 370 00:23:23.720 --> 00:23:26.880 you have calls out to some on prem or third 371 00:23:26.880 --> 00:23:30.440 party provided data provider and you're in they're migrating to AWS, 372 00:23:30.519 --> 00:23:32.839 they may be going to either a no SQL option 373 00:23:33.000 --> 00:23:35.440 or or RDS, and so you want to make sure 374 00:23:35.480 --> 00:23:38.240 that those get converted, assuming you just port the scripts, 375 00:23:38.319 --> 00:23:41.119 you know, lift and shift into easy tubes. So I 376 00:23:41.160 --> 00:23:43.960 mean a question that I might have is where do 377 00:23:44.000 --> 00:23:47.440 you find yourself on the risk scale, Like what happens 378 00:23:47.480 --> 00:23:49.640 if the LM which is not going to be perfect? 379 00:23:49.960 --> 00:23:52.759 I like, I mean, obviously it makes up tables and 380 00:23:52.759 --> 00:23:56.400 whatnot that aren't being actually used. It's that's fine, but 381 00:23:56.519 --> 00:23:59.039 I think the false negatives would be more of a problem, 382 00:23:59.119 --> 00:24:01.559 Like what happens if its the table? Would that have 383 00:24:01.599 --> 00:24:04.079 caused an issue during the migration And how did you 384 00:24:04.200 --> 00:24:07.559 potentially think about mitigating those sorts of risks. 385 00:24:08.160 --> 00:24:12.119 Yes, so what we did was rather than kind of 386 00:24:12.160 --> 00:24:16.200 invest the time upfront to build a fully working solution, 387 00:24:16.279 --> 00:24:20.160 it was let's do a test on a few scripts first. 388 00:24:20.200 --> 00:24:23.160 Let's let's try over three or four scripts, scripts that 389 00:24:23.200 --> 00:24:25.640 we have done the analysis by hand on so we 390 00:24:25.720 --> 00:24:29.720 know we've got a golden answer that there is a 391 00:24:30.720 --> 00:24:36.039 ground truth to talk we're comparing against. When we did 392 00:24:36.160 --> 00:24:40.519 run it across the full data set, then we did 393 00:24:40.519 --> 00:24:45.920 dip sampling to make sure that a reasonable portion of 394 00:24:45.960 --> 00:24:49.960 them were accurate. The other thing that we did with 395 00:24:50.039 --> 00:24:55.480 these was when migrating those scripts made you of different environments. 396 00:24:55.680 --> 00:25:00.880 So these were going to cumuneties cluster in as and 397 00:25:01.680 --> 00:25:04.880 because we had a dead of in a staging and environment, 398 00:25:04.880 --> 00:25:08.039 we knew that we could run these scripts in a 399 00:25:08.480 --> 00:25:12.920 sandbox pre production environment. If they failed kind of so 400 00:25:13.039 --> 00:25:16.240 be it. It's not going to bring the business down. 401 00:25:16.279 --> 00:25:19.680