1 00:00:00,000 --> 00:00:02,004 Generative AI can create code as well. 2 00:00:02,004 --> 00:00:05,784 So it's not just a case that this is gonna say, here's your, 3 00:00:05,845 --> 00:00:06,774 this is what you should do. 4 00:00:06,774 --> 00:00:09,085 It's gonna be, and I, this is something I firmly believe will 5 00:00:09,085 --> 00:00:10,764 happen in the next two, three years. 6 00:00:11,394 --> 00:00:15,614 You already have generative AI code in, Microsoft and Amazon, like Amazon 7 00:00:15,614 --> 00:00:20,664 Whisper, if you're a coder, but, um, you're gonna have, literally a free, 8 00:00:20,694 --> 00:00:22,375 free text like, build me a website. 9 00:00:22,434 --> 00:00:26,110 This is my product, this, as you said, and it will just build the website because 10 00:00:26,110 --> 00:00:32,510 it will be smart enough to do the code as well as understand all the, permutations 11 00:00:32,530 --> 00:00:34,000 on back you know, as you said. 12 00:00:39,140 --> 00:00:43,070 Welcome to the e-Commerce podcast with me, your host, Matt Edmundson. 13 00:00:43,460 --> 00:00:48,020 The E-Commerce podcast is all about helping you deliver e-commerce. 14 00:00:48,080 --> 00:00:48,740 Wow. 15 00:00:48,800 --> 00:00:54,980 And to help us do just that today I am chatting with the very amazingly 16 00:00:54,980 --> 00:01:00,890 talented Max Sinclair from e-content, Ecomtent, uh, about exploring the 17 00:01:00,890 --> 00:01:03,635 possibilities of Generative AI. 18 00:01:03,935 --> 00:01:07,655 But before Max and I jump into that, let me suggest a few other 19 00:01:07,685 --> 00:01:11,075 e-commerce podcast episodes that I think you'll enjoy listening to. 20 00:01:11,465 --> 00:01:15,905 How to Bring the Magic of Disney to Your Customer Service with Vance Morris. 21 00:01:15,905 --> 00:01:19,355 What a legend, uh, Vance was doing that podcast episode. 22 00:01:19,355 --> 00:01:20,165 Definitely check it out. 23 00:01:20,170 --> 00:01:24,215 And in fact, if you're part of e-commerce cohort, uh, Vance has also 24 00:01:24,220 --> 00:01:27,035 got a training in there, which you can look at in one of the sprints, 25 00:01:27,035 --> 00:01:29,760 which is brilliant, let me tell you, 26 00:01:30,120 --> 00:01:34,350 uh, also check out how to develop a creative strategy for your brands 27 00:01:34,900 --> 00:01:38,100 advertising with Colby Flood, the legend from North Carolina. 28 00:01:38,100 --> 00:01:39,540 What a great guy Colby is. 29 00:01:39,870 --> 00:01:41,400 Uh, do check that out. 30 00:01:41,405 --> 00:01:45,390 You can find these and our entire archive of episodes on our website 31 00:01:45,390 --> 00:01:48,470 for free at ecommercepodcast.net. 32 00:01:49,280 --> 00:01:53,240 On our website, you can also sign up for our newsletter, and each 33 00:01:53,240 --> 00:01:57,200 week we will email you these links along with the moats and the moats? 34 00:01:57,470 --> 00:01:59,330 Uh, the notes and the links. 35 00:01:59,570 --> 00:02:03,170 Uh, from today's conversation with Max, uh, they go direct your inbox 36 00:02:03,170 --> 00:02:06,345 totally free, which is amazing. 37 00:02:06,615 --> 00:02:11,685 Now, uh, Max, I'm sure like you, I love to help people take their 38 00:02:11,715 --> 00:02:13,815 e-commerce businesses to the next level. 39 00:02:13,815 --> 00:02:19,305 You know what, I've been working in studying e-commerce for years, since 2002, 40 00:02:19,635 --> 00:02:23,505 and during that time, I've developed, well, let's just call it a unique 41 00:02:23,505 --> 00:02:25,785 methodology called e-Commerce Cycles. 42 00:02:25,995 --> 00:02:29,675 Uh, it's a system that helps businesses grow their revenue by identifying 43 00:02:29,675 --> 00:02:31,250 and exploiting opportunities. 44 00:02:31,610 --> 00:02:35,510 I use e-commerce cycles on my own e-commerce sites, uh, and 45 00:02:35,510 --> 00:02:38,330 I also use it with my clients. 46 00:02:38,330 --> 00:02:41,540 And we have seen incredible growth generating over a hundred 47 00:02:41,540 --> 00:02:44,030 million in online revenue. 48 00:02:44,030 --> 00:02:47,735 So if you wanna achieve the same kind of success? 49 00:02:47,735 --> 00:02:49,355 No, I'm not gonna give you that kind of guarantee. 50 00:02:49,535 --> 00:02:53,855 But what I am gonna say is, uh, we have got some free training, uh, that 51 00:02:53,855 --> 00:02:57,995 will take you through the e-commerce cycle methodology and show you 52 00:02:57,995 --> 00:03:00,725 exactly how we use it in our business. 53 00:03:00,730 --> 00:03:04,805 So if you are in e-commerce, uh, in , I'm a bit tongue-tied today. 54 00:03:04,895 --> 00:03:06,635 If you're in e-commerce, check it out. 55 00:03:06,635 --> 00:03:10,550 Head to ecommercecycles.com for more information. 56 00:03:10,790 --> 00:03:11,570 Do check that out. 57 00:03:11,570 --> 00:03:12,290 It is a free training. 58 00:03:12,290 --> 00:03:13,760 Hopefully you'll enjoy it. 59 00:03:14,300 --> 00:03:16,220 So let's talk about Max now. 60 00:03:16,220 --> 00:03:21,680 Max is the CEO and founder of Ecomtent who are revolutionizing how e-commerce 61 00:03:21,680 --> 00:03:25,190 sellers create content with generative AI. 62 00:03:25,220 --> 00:03:26,990 Sounds all very posh, doesn't it? 63 00:03:27,230 --> 00:03:31,760 Now, prior to founding Ecomtent, uh, which is the, it's a word you get when 64 00:03:32,020 --> 00:03:34,750 you merge ecommerce and content together. 65 00:03:35,080 --> 00:03:37,960 Uh, Max has spent six years at Amazon. 66 00:03:37,960 --> 00:03:42,850 Yes, he has where he worked on the launch of Amazon here in the uk, uh, Amazon 67 00:03:42,855 --> 00:03:44,170 business here in the uk, should I say. 68 00:03:44,530 --> 00:03:49,065 Uh, the country launch of amazon.sg, which if you're not in the know, 69 00:03:49,205 --> 00:03:53,470 is in Singapore, uh, and the launch of Amazon Grocery across the uk. 70 00:03:53,470 --> 00:03:57,720 The man's done it all it seems with Amazon, uh, and throughout his time he has 71 00:03:57,720 --> 00:04:01,990 worked directly with hundreds of sellers of all sizes, across many categories. 72 00:04:02,290 --> 00:04:04,090 And he saw the pain. 73 00:04:04,510 --> 00:04:05,110 Oh yes. 74 00:04:05,230 --> 00:04:06,670 Which we can all identify with. 75 00:04:06,970 --> 00:04:11,320 The pain of creating content, uh, for e-commerce firsthand. 76 00:04:11,320 --> 00:04:16,370 So hence, I'm guessing Max, the reason you started Ecomtent. 77 00:04:16,390 --> 00:04:17,860 Uh, welcome to the show. 78 00:04:17,860 --> 00:04:18,490 Great to have you. 79 00:04:18,490 --> 00:04:19,120 Great that you are here. 80 00:04:19,120 --> 00:04:19,780 How are we doing? 81 00:04:20,550 --> 00:04:21,230 Thank you. 82 00:04:21,260 --> 00:04:22,190 No, very excited. 83 00:04:22,190 --> 00:04:23,000 Great to be here. 84 00:04:23,000 --> 00:04:26,750 As I said, um, fan of the podcast, so it's really cool to be on it and yeah. 85 00:04:26,750 --> 00:04:28,880 Excited to get going and talk about generative ai. 86 00:04:29,720 --> 00:04:30,280 Oh, absolutely. 87 00:04:30,280 --> 00:04:31,040 Its great to have you. 88 00:04:31,040 --> 00:04:33,590 Now you have, uh, a decidedly British accent. 89 00:04:33,595 --> 00:04:37,570 Are you calling from the good old islands of the UK or are you wanting to sort of 90 00:04:37,590 --> 00:04:41,785 less exciting than few other guests. 91 00:04:41,785 --> 00:04:43,145 I'm here in London. 92 00:04:44,855 --> 00:04:45,545 No, not at all. 93 00:04:45,785 --> 00:04:48,125 I, you just want, you, you know what you can't do when you talk to 94 00:04:48,130 --> 00:04:51,425 guests, assume from their accent where they're from, because everyone now 95 00:04:51,425 --> 00:04:53,255 has a digital nomad visa, don't they? 96 00:04:53,255 --> 00:04:57,215 And they're all working in very posh climates, uh, and 97 00:04:57,215 --> 00:04:58,895 um, and very tropical places. 98 00:04:58,895 --> 00:04:59,825 So I just wanted to clarify. 99 00:04:59,825 --> 00:05:03,030 You are in fact dialing in from the great city of London itself. 100 00:05:03,310 --> 00:05:03,980 There we go. 101 00:05:04,310 --> 00:05:05,030 Exactly. 102 00:05:05,120 --> 00:05:07,920 West Hamstead, to be even more specific.. 103 00:05:10,580 --> 00:05:11,450 Brilliant, brilliant. 104 00:05:11,450 --> 00:05:15,110 Why, did you, um, when you worked at Amazon, did you work at, uh, Amazon's 105 00:05:15,110 --> 00:05:17,000 HQ In, in the center of London then? 106 00:05:17,030 --> 00:05:17,420 Yep. 107 00:05:17,510 --> 00:05:21,530 I, in Shoreditch, um, also have worked in, as you said in the 108 00:05:21,530 --> 00:05:23,810 intro, in the Singaporean office. 109 00:05:23,840 --> 00:05:27,920 I've been to the Milan office, I've been to Luxembourg. 110 00:05:28,590 --> 00:05:33,670 Uh, Bratislava, uh, I at one point helped manage the sales team in Bratislava. 111 00:05:33,690 --> 00:05:36,420 So yeah, being, being global with Amazon. 112 00:05:36,600 --> 00:05:38,370 Um, being global, yeah. 113 00:05:39,030 --> 00:05:42,690 Well, there is a chance we may have crossed paths down at Amazon HQ in London 114 00:05:42,695 --> 00:05:44,190 as I've been to that building many times, 115 00:05:44,640 --> 00:05:46,770 Um, it's just easily Yeah, yeah, yeah. 116 00:05:46,775 --> 00:05:48,780 It's be, it would be ironic if we had, uh, you. 117 00:05:49,430 --> 00:05:51,875 So you worked at Amazon. 118 00:05:51,875 --> 00:05:52,865 Let's jump into this, right? 119 00:05:52,865 --> 00:05:52,955 Yes. 120 00:05:52,955 --> 00:05:56,645 So you worked in Amazon, um, and we said in the bio that you saw 121 00:05:56,645 --> 00:06:00,395 the sort of the pain of creating e-commerce content sort of firsthand. 122 00:06:00,605 --> 00:06:00,695 Mm-hmm. 123 00:06:00,940 --> 00:06:03,965 . Was that what led you down this journey of generative ai? 124 00:06:03,965 --> 00:06:04,325 Was there. 125 00:06:05,435 --> 00:06:07,505 I'm just curious because that it's a heck of a leap. 126 00:06:07,505 --> 00:06:07,925 Sure. 127 00:06:07,985 --> 00:06:10,025 You know, from Amazon to, to new content. 128 00:06:10,025 --> 00:06:10,325 Yeah. 129 00:06:10,405 --> 00:06:10,605 Yeah. 130 00:06:10,805 --> 00:06:15,845 So I guess what really was the, um, was the inspiration was this kind 131 00:06:15,845 --> 00:06:17,945 of onset of generative AI itself. 132 00:06:17,975 --> 00:06:23,885 And I think I, um, I kind of saw the, saw the potential. 133 00:06:24,515 --> 00:06:28,745 And, uh, with my co-founder, we kind of launched a business which was, um, 134 00:06:28,985 --> 00:06:30,515 within both of our understandings. 135 00:06:30,515 --> 00:06:35,405 So, yes, like I understand the pain that, um, sellers have, uh, when 136 00:06:35,410 --> 00:06:38,015 they're creating content, but more, I mean, if I'm being honest, more 137 00:06:38,015 --> 00:06:41,705 generally, like I think the power of generative AI is transformative. 138 00:06:42,035 --> 00:06:45,785 I think it's gonna transform pretty much every industry that we. 139 00:06:46,895 --> 00:06:48,065 You know, every industry. 140 00:06:48,545 --> 00:06:51,935 Um, I mean, I can go into a bit more details about what generative AI 141 00:06:51,935 --> 00:06:55,375 specifically is, because I think a lot of listeners will be saying, oh, 142 00:06:55,375 --> 00:06:57,305 a AI's been around for ages, right? 143 00:06:57,305 --> 00:07:00,095 Like, what do you, you know, everyone's heard of ai we're kind 144 00:07:00,095 --> 00:07:05,525 of very used to ai, um, artificial intelligence in Google ads and, you 145 00:07:05,525 --> 00:07:06,725 know, and all sorts of stuff, right? 146 00:07:06,725 --> 00:07:10,835 But this is really a, a big technological shift that has happened. 147 00:07:11,615 --> 00:07:15,665 in the last few, in the last few years and has been commercialized really 148 00:07:15,665 --> 00:07:16,865 in the last few months, you know? 149 00:07:17,055 --> 00:07:17,335 Mm-hmm. 150 00:07:17,420 --> 00:07:21,485 . And, um, that's, um, that's kind of what, um, what really excites me and 151 00:07:21,485 --> 00:07:24,725 yeah, I'm, I'm, I'm looking to bring this to an industry that I know well, 152 00:07:24,845 --> 00:07:26,555 and that's kind of what motivated me. 153 00:07:27,695 --> 00:07:28,055 Okay. 154 00:07:28,055 --> 00:07:31,865 So, um, Well let, let's deal with that straight away then. 155 00:07:31,865 --> 00:07:34,475 You, so we used this phrase, generative AI in the bio. 156 00:07:34,475 --> 00:07:36,035 You just kinda mentioned it there. 157 00:07:36,455 --> 00:07:39,455 And like you say, AI has been around for a while, but generative 158 00:07:39,460 --> 00:07:40,655 AI is quite a new thing. 159 00:07:40,655 --> 00:07:42,065 So what do you mean? 160 00:07:42,215 --> 00:07:43,625 Let's, let's differentiate terms. 161 00:07:43,625 --> 00:07:44,465 What do you mean by this? 162 00:07:44,465 --> 00:07:44,765 Definitely. 163 00:07:45,065 --> 00:07:50,585 So I'm gonna, I'm gonna label kind of AI as everybody knows it as deterministic 164 00:07:50,585 --> 00:07:55,985 ai, uh, and this deterministic AI was kind of conceived in, in the 1950s and 165 00:07:55,985 --> 00:07:57,965 kind of commercialized in the 1980s. 166 00:07:58,235 --> 00:08:02,135 And what you have here is a computer, uh, able to mimic the problem 167 00:08:02,135 --> 00:08:06,650 solving capabilities of a human to complete analysis on big data 168 00:08:06,650 --> 00:08:11,480 sets and broadly either do data classifications or regressions. 169 00:08:11,510 --> 00:08:11,600 Mm-hmm. 170 00:08:11,840 --> 00:08:14,780 . So what you do is you, you feed in a large dataset and the machine will 171 00:08:14,780 --> 00:08:18,980 tell you, this is type A or type B, or this is a regression and how 172 00:08:18,980 --> 00:08:20,300 these numbers relate to each other. 173 00:08:20,900 --> 00:08:25,100 And it's used, um, you know, in, um, in medicine it's used, uh, you 174 00:08:25,100 --> 00:08:29,240 know, we use it at Amazon to kind of, uh, identify like, are these ASINs 175 00:08:29,600 --> 00:08:31,160 parent and child asins attributes? 176 00:08:31,160 --> 00:08:31,970 Should we merge them? 177 00:08:32,060 --> 00:08:36,455 Um, this kind of question, uh, what's happened very recently, 178 00:08:36,575 --> 00:08:40,325 um, really commercializing kind of the back end of 2022. 179 00:08:41,015 --> 00:08:43,955 Is a second wave of ai, which is called generative ai. 180 00:08:44,405 --> 00:08:48,845 And this is where a computer is able to produce entirely new content. 181 00:08:48,875 --> 00:08:51,425 So new images, new video, new texts, new code. 182 00:08:51,995 --> 00:08:58,360 And what happens here is you train a, um, train a a machine on the patterns 183 00:08:58,360 --> 00:09:02,470 and characteristics of the input data, and then you use it to kind 184 00:09:02,470 --> 00:09:04,240 of generate new and similar content. 185 00:09:04,630 --> 00:09:09,520 And, um, this is, this is a, a big step because in deterministic ai, 186 00:09:09,520 --> 00:09:11,050 the answer was really in the data. 187 00:09:11,055 --> 00:09:14,050 So you are kind of the, you know, you're looking at large pieces of 188 00:09:14,050 --> 00:09:17,160 the data and saying, this is, I, you know, your, your answer for what your 189 00:09:17,160 --> 00:09:19,470 question be it, how is these relate? 190 00:09:19,470 --> 00:09:21,780 Or who, how should I target this person with this ad? 191 00:09:22,080 --> 00:09:23,130 Your answer's in there. 192 00:09:23,190 --> 00:09:23,250 Yeah. 193 00:09:23,460 --> 00:09:27,780 With generative ai, you are kind of, you, you are creating new things. 194 00:09:27,780 --> 00:09:31,020 So you're kind of saying, this is a data now make it look like 195 00:09:31,020 --> 00:09:32,220 this, or do this or do that. 196 00:09:32,220 --> 00:09:34,200 And you're creating new content. 197 00:09:34,560 --> 00:09:38,940 Um, and this is gonna, I mean, I can go, I can go a level more deeper if 198 00:09:38,940 --> 00:09:42,720 we want to, but like this is kind of the, um, this is a step change. 199 00:09:42,960 --> 00:09:47,220 Um, and I think it's gonna have really, really broad consequences everywhere. 200 00:09:47,700 --> 00:09:49,890 And, um, it's very exciting. 201 00:09:49,890 --> 00:09:53,940 So, yeah, I think, um, when I, when I, what kind of being saying 202 00:09:53,940 --> 00:09:57,630 on top of this and bringing it to e-commerce and tailoring it to, um, 203 00:09:57,690 --> 00:09:59,460 sellers to help them is, uh, yeah. 204 00:09:59,460 --> 00:10:02,220 Is what is kind of what, what is driving me in, you know, the 205 00:10:02,220 --> 00:10:03,540 reason why I started ecomtent. 206 00:10:03,560 --> 00:10:04,080 Yeah, yeah, yeah. 207 00:10:04,380 --> 00:10:04,950 So the. 208 00:10:05,565 --> 00:10:06,285 So generative. 209 00:10:06,345 --> 00:10:09,315 I mean, it all sounds very exciting, but it all sounds a bit Terminator. 210 00:10:09,525 --> 00:10:12,465 Uh, Do you know what I mean that, um, Skynet's gonna take over 211 00:10:12,465 --> 00:10:13,545 the world and destroy us all. 212 00:10:14,175 --> 00:10:17,535 Um, it was interesting, uh, the time of recording this. 213 00:10:17,565 --> 00:10:21,285 Uh, as you may or may not know, dear listener, when we record the e-Commerce 214 00:10:21,455 --> 00:10:24,765 podcast and when it goes live, there's usually a quite a significant 215 00:10:24,765 --> 00:10:26,335 time difference between the two. 216 00:10:27,085 --> 00:10:28,070 Or at least there is at the moment. 217 00:10:28,070 --> 00:10:29,540 We're, we're making steps to change that. 218 00:10:29,540 --> 00:10:37,130 But, um, at the time of recording, uh, last week I did a LinkedIn 219 00:10:37,135 --> 00:10:41,090 live, um, on, I do this thing called on a Monday lunchtime, the 220 00:10:41,090 --> 00:10:42,590 LinkedIn live chat show type thing. 221 00:10:42,590 --> 00:10:44,450 So if you're on LinkedIn on a Monday, come join in. 222 00:10:44,900 --> 00:10:45,860 Um, connect with me on LinkedIn. 223 00:10:45,860 --> 00:10:46,360 You'll see its there. 224 00:10:46,550 --> 00:10:51,380 And last week, um, I was on chat, GPT, which is probably the most, 225 00:10:51,500 --> 00:10:56,750 um, in the news Generative AI system that I can think of at the moment. 226 00:10:56,750 --> 00:10:56,990 Right. 227 00:10:57,470 --> 00:11:00,740 And I just, I just put into that chat GPT thing. 228 00:11:00,740 --> 00:11:04,730 What are the top five trends that I need to be aware of in e-commerce for 2023? 229 00:11:05,600 --> 00:11:07,860 And it was just really interesting, some of the answers that it mm-hmm. 230 00:11:07,945 --> 00:11:08,420 came up with. 231 00:11:09,110 --> 00:11:13,430 So what you are saying is the answers that, um, open Chat came up with were 232 00:11:13,430 --> 00:11:18,245 all completely, um, , uh, generated. 233 00:11:18,245 --> 00:11:19,835 It's all new fresh content. 234 00:11:19,835 --> 00:11:19,925 Yes. 235 00:11:19,925 --> 00:11:23,135 Wasn't like it just went through a blog post, copied and pasted mm-hmm. 236 00:11:23,375 --> 00:11:26,195 , how did it, how did it come up with those five answers then? 237 00:11:26,765 --> 00:11:27,215 Okay. 238 00:11:27,485 --> 00:11:31,895 So in, I'll go very quickly into the technical, technical 239 00:11:31,895 --> 00:11:34,925 explanation and apologies, but I think it's quite interesting just 240 00:11:34,930 --> 00:11:36,245 to understand like what's happening. 241 00:11:36,575 --> 00:11:36,725 Yeah. 242 00:11:36,755 --> 00:11:42,065 Um, generative AI uses, uh, something called Generative Advisoral Network, and 243 00:11:42,065 --> 00:11:44,315 basically you have two networks in here. 244 00:11:45,005 --> 00:11:48,155 you have the first network is a generator and you give it a load 245 00:11:48,155 --> 00:11:50,255 of input data in chat GPT's case. 246 00:11:50,255 --> 00:11:54,185 This is all the text on the internet up to 2021, which is important. 247 00:11:54,185 --> 00:11:56,015 And I'll, I'll come back to why that's important. 248 00:11:56,495 --> 00:11:56,555 Yeah. 249 00:11:56,555 --> 00:12:01,325 But you give it all the data up to 2021, the machine and the generator 250 00:12:01,325 --> 00:12:05,465 learns the patterns and characteristics of that content and then tries to 251 00:12:05,465 --> 00:12:06,905 create new content similar to that. 252 00:12:07,295 --> 00:12:10,475 And then you have a second one called the discriminator which 253 00:12:10,475 --> 00:12:14,315 is trained to distinguish real content from generator content. 254 00:12:14,525 --> 00:12:17,495 So it's presented with the original content, it sees the 255 00:12:17,495 --> 00:12:20,285 content from the generator, and it basically goes yes and no. 256 00:12:20,285 --> 00:12:22,955 Yes, no, you know, in, in microseconds. 257 00:12:23,255 --> 00:12:23,345 Mm-hmm. 258 00:12:23,585 --> 00:12:25,925 of does this look like the original content or not? 259 00:12:26,135 --> 00:12:29,105 And this will happen, you know, trillions of times a second. 260 00:12:29,525 --> 00:12:34,070 And then once he, um, You know, once a generator fools the discriminator 261 00:12:34,070 --> 00:12:35,630 enough, then it publishes the content. 262 00:12:35,780 --> 00:12:37,490 So it looks like the original content. 263 00:12:37,850 --> 00:12:39,260 So this is kind of what's happening. 264 00:12:39,740 --> 00:12:42,950 Um, you know, when we talk about generative ai, which is, which as 265 00:12:42,950 --> 00:12:47,270 I say, it's, it's a new, it's a big technological shift on how we think 266 00:12:47,275 --> 00:12:48,710 about like machine intelligence. 267 00:12:49,220 --> 00:12:53,710 Um, oh, just, just to return to the point that you said on chat, GPT, I. 268 00:12:54,280 --> 00:12:59,410 Just explain a point here, which is chat GPT and, and I, I think we'll go into, 269 00:12:59,410 --> 00:13:03,190 you know, some, some ways that I, I would recommend using it because mm-hmm. 270 00:13:03,640 --> 00:13:04,750 At the moment it's free. 271 00:13:04,755 --> 00:13:07,900 I dunno, when this been published, it might cost $42 a month. 272 00:13:07,960 --> 00:13:08,410 I dunno. 273 00:13:08,710 --> 00:13:11,230 But at the moment it's free tool to use as we are recording. 274 00:13:11,235 --> 00:13:11,310 Yeah. 275 00:13:11,710 --> 00:13:16,895 Um, But what it is is a language model, and it is the point of a language 276 00:13:16,895 --> 00:13:20,945 model is to gen, as I said, it's to generate patterns and text similar 277 00:13:20,945 --> 00:13:22,295 to the data that it's trained on. 278 00:13:22,745 --> 00:13:24,215 This is not accurate data. 279 00:13:24,665 --> 00:13:25,055 This is. 280 00:13:25,490 --> 00:13:28,550 This is, is trained to sound like a human talking and it's trained 281 00:13:28,550 --> 00:13:31,760 to, you know, give you an answer that sounds correct, but it's not, 282 00:13:31,760 --> 00:13:33,310 it is not necessarily accurate. 283 00:13:33,590 --> 00:13:37,610 So we should always, like, there's, there's some very useful ways of 284 00:13:37,610 --> 00:13:41,960 using chat GPT, which we'll probably go into later, later in the podcast. 285 00:13:41,965 --> 00:13:42,110 Mm-hmm. 286 00:13:42,190 --> 00:13:45,350 . But, um, we should, we should always have that in mind that. 287 00:13:46,025 --> 00:13:47,945 What this is doing is creating content. 288 00:13:47,945 --> 00:13:49,295 That sounds correct. 289 00:13:49,325 --> 00:13:51,545 It's not create, it's not giving me accurate content. 290 00:13:51,545 --> 00:13:54,380 And I can give you a, um, an interesting example of this. 291 00:13:54,905 --> 00:14:00,255 Uh, my, my, um, girlfriend's brother is a doctor, and when he puts in chat gpt, 292 00:14:00,280 --> 00:14:05,285 you know, some medical questions, it will then give to him like a medical. 293 00:14:07,400 --> 00:14:10,640 Uh, like a medical, um, kind of article as an answer. 294 00:14:10,700 --> 00:14:10,790 Mm-hmm. 295 00:14:10,790 --> 00:14:11,900 As it is trained to, right? 296 00:14:11,905 --> 00:14:15,050 As you, if you ask it to write in this spirit of Shakespeare, we're doing 297 00:14:15,050 --> 00:14:16,130 it in Shakespeare, blah, blah, blah. 298 00:14:16,490 --> 00:14:19,070 Uh, but then what we'll do is we'll completely make up the references 299 00:14:19,070 --> 00:14:21,860 cause it's seen online that, you know, these come from references 300 00:14:21,860 --> 00:14:24,230 and you should make, you know, you should reference random, like mm-hmm. 301 00:14:24,470 --> 00:14:25,760 professional sounding names. 302 00:14:25,940 --> 00:14:29,420 So it kind of gives you this medical answer with references which don't exist. 303 00:14:29,425 --> 00:14:32,570 So, and that's because that's what it's trained to do, is to, is to kind of 304 00:14:32,570 --> 00:14:34,380 replicate the data that it's trained on. 305 00:14:35,295 --> 00:14:38,115 it is super useful and there's many ways that we should use it, but we 306 00:14:38,115 --> 00:14:41,775 should always be, um, we should always know like fundamentally what the 307 00:14:41,775 --> 00:14:45,165 technology is and, and what like, and what, what it's supposed to be doing. 308 00:14:46,455 --> 00:14:47,445 That's really interesting. 309 00:14:47,445 --> 00:14:50,805 And you, the other thing that you mentioned about Chap GPT is the data 310 00:14:50,810 --> 00:14:53,135 that feeds it only goes up to 2021. 311 00:14:54,290 --> 00:14:54,890 Yes. 312 00:14:54,920 --> 00:14:55,310 Yes. 313 00:14:55,340 --> 00:14:59,300 So this is, um, so I mean, this is another point, right? 314 00:14:59,300 --> 00:15:03,830 That, um, the data is, they say officially it's a 2021. 315 00:15:03,830 --> 00:15:07,040 I think there have been, I mean, I follow this stuff as I'm sure 316 00:15:07,040 --> 00:15:08,720 many listeners do on, on LinkedIn. 317 00:15:08,720 --> 00:15:11,510 So there's been like, it's a live model. 318 00:15:11,515 --> 00:15:12,620 They're training it, right? 319 00:15:12,625 --> 00:15:13,650 And, um, I think. 320 00:15:14,180 --> 00:15:18,890 It they are, might be updating parts of it just to kind of experiment, but officially 321 00:15:18,890 --> 00:15:21,080 it's only been trained until 2021. 322 00:15:21,620 --> 00:15:26,585 Um, so it is not the most up to date, but, um, I, I'm not sure 323 00:15:26,585 --> 00:15:30,635 if you're aware, but they're kind of working on GPT four right now. 324 00:15:31,145 --> 00:15:36,005 Um, and this is going to increase, um, the number of parameters. 325 00:15:36,005 --> 00:15:39,215 So the number of data sets that, uh, you know, the machine is trained on 326 00:15:39,215 --> 00:15:43,385 from 175 billion to a hundred trillion. 327 00:15:43,835 --> 00:15:46,535 So what this is to kind of con, these numbers are really hard to 328 00:15:46,535 --> 00:15:50,555 contextualize, but to contextualize it, this is like going from earning 329 00:15:50,560 --> 00:15:55,135 500 K a year to 20, uh, 285 million. 330 00:15:55,855 --> 00:15:57,245 uh, pounds a year. 331 00:15:57,735 --> 00:15:57,905 Yeah. 332 00:15:57,905 --> 00:16:00,680 So it's a, it's an, it's an enormous upgrade. 333 00:16:00,740 --> 00:16:05,210 Like it's an upgrade kind of, it's forget like os iOS updates, what, what 334 00:16:05,210 --> 00:16:06,870 you're gonna be able to do with GPT four. 335 00:16:06,920 --> 00:16:09,230 The, the rumors are, is you're gonna be able to interact 336 00:16:09,230 --> 00:16:11,210 with both text and speech. 337 00:16:11,510 --> 00:16:15,780 So you could talk to it and get your answer and, and kind of at the 338 00:16:15,785 --> 00:16:17,420 moment you use it and it's amazing. 339 00:16:17,420 --> 00:16:21,700 But like with GPT four, you could use it and write a 60,000 340 00:16:21,705 --> 00:16:23,160 page book from a single prompt. 341 00:16:23,160 --> 00:16:27,110 So It's, it, the, um, the speed at which this technology 342 00:16:27,110 --> 00:16:29,360 is, is moving, is incredible. 343 00:16:29,960 --> 00:16:33,920 Um, and yeah, it's just a super, super exciting space, um, in, in my opinion. 344 00:16:35,000 --> 00:16:36,230 It's a really interesting one, isn't it? 345 00:16:36,230 --> 00:16:39,680 Because one of the things that came up in my little LinkedIn conversation 346 00:16:39,680 --> 00:16:43,520 last week, uh, Max, and I'm kind of curious to see where, cuz you are 347 00:16:43,970 --> 00:16:46,250 involved in ai, um, is where you sit. 348 00:16:46,310 --> 00:16:46,520 Yes. 349 00:16:46,570 --> 00:16:51,890 Sit on this because if I can go to chat GPT and tell it with a single prompt 350 00:16:51,890 --> 00:16:53,840 to write a book for 60,000 words. 351 00:16:55,070 --> 00:16:57,710 Yes, that book's not really come from me, has it? 352 00:16:57,710 --> 00:16:58,280 And it's. 353 00:16:59,255 --> 00:17:01,175 There's a sort of this, this fine line, isn't it? 354 00:17:01,175 --> 00:17:02,095 I could go and market. 355 00:17:02,095 --> 00:17:03,785 I mean, there are people on YouTube now. 356 00:17:03,785 --> 00:17:05,585 I've seen the videos, they pop up in my feed. 357 00:17:05,585 --> 00:17:07,115 I've not watched them, I have to be honest with you. 358 00:17:07,115 --> 00:17:12,035 But the, the use chat, GPT to write your first book, um, and publish 359 00:17:12,035 --> 00:17:14,945 on Amazon, you know, turn yourself into a millionaire kind of thing. 360 00:17:15,335 --> 00:17:20,255 And it's, it's a really interesting conundrum, isn't it? 361 00:17:20,255 --> 00:17:22,985 Because all of a sudden, um, creation. 362 00:17:25,250 --> 00:17:27,530 in some way stops being from the creator. 363 00:17:27,530 --> 00:17:29,360 It starts being from the machine. 364 00:17:29,390 --> 00:17:35,030 And I'm just really curious, I dunno if I have an answer, Max, uh, Max, 365 00:17:35,030 --> 00:17:40,610 but I, I, I feel like there's this sort of very big gray area with ai. 366 00:17:40,850 --> 00:17:43,610 Um, and I'm, I'm not quite sure. 367 00:17:43,730 --> 00:17:46,220 I, I get excited by AI and some of the things it can do. 368 00:17:46,220 --> 00:17:46,310 Mm-hmm. 369 00:17:46,660 --> 00:17:48,500 , but some of the other things like being able to write a book. 370 00:17:48,860 --> 00:17:49,820 With a single prompt. 371 00:17:50,690 --> 00:17:53,210 I just, I dunno how I feel about that, if I'm honest with you. 372 00:17:53,240 --> 00:17:57,200 It's, well, I think what I would say is it's terrifying in some 373 00:17:57,200 --> 00:17:59,210 level, but also it's very exciting. 374 00:17:59,390 --> 00:18:03,650 And what I would also say, is that like this is coming and it's 375 00:18:03,655 --> 00:18:05,525 coming for every single industry. 376 00:18:05,805 --> 00:18:11,370 and like I was Out to dinner last night with friends of 377 00:18:11,390 --> 00:18:12,290 mine, one of them is a lawyer. 378 00:18:12,290 --> 00:18:15,440 And I was explaining to them like what you're gonna have in the future is 379 00:18:15,440 --> 00:18:19,130 you're gonna have an a generative AI model trained on all the law in the uk. 380 00:18:19,280 --> 00:18:23,660 You're gonna give it a prompt like, my neighbor has built a fence over 381 00:18:23,690 --> 00:18:26,540 blah, blah, blah, and it's kind of blocking my sunlight and what 382 00:18:26,540 --> 00:18:29,240 you know, and it will just give you the answer because it's like, 383 00:18:29,705 --> 00:18:32,705 Chat GPT can't do this because, and I think this is a difference between like 384 00:18:32,705 --> 00:18:36,065 an open source model, which is like chat GPT and kind of a private model, which 385 00:18:36,065 --> 00:18:39,815 is like what we have at ecomtent and like what you would have where you say, okay, 386 00:18:39,815 --> 00:18:43,385 I'm gonna build like a chat GPT for law and I'm actually gonna make it correct for 387 00:18:43,385 --> 00:18:47,435 law, which is not, you know, it's not chat GPT, you couldn't do this with the law, 388 00:18:47,435 --> 00:18:52,155 but, and you, and I mean, it's gonna come, it's, it's gonna come for every industry. 389 00:18:52,355 --> 00:18:57,215 Um, so I think the genie is out the bottle, right? 390 00:18:57,215 --> 00:18:58,595 Like you. 391 00:18:59,375 --> 00:19:02,315 There's, there's kind of no, there's no going back from this. 392 00:19:02,405 --> 00:19:04,355 Um, but I think it's very exciting. 393 00:19:04,360 --> 00:19:09,635 Like, I, I, you know, I'm a, um, I'm a firm believer that kind of technological 394 00:19:09,635 --> 00:19:11,585 advances are good for humanity. 395 00:19:11,795 --> 00:19:16,595 Um, you know, if you take us all, uh, a thousand years ago, we are all, you 396 00:19:16,600 --> 00:19:22,145 know, 80, 90% of us were working in agriculture as, uh, as pretty miserable. 397 00:19:22,595 --> 00:19:24,300 Um, you know, Terrible. 398 00:19:24,305 --> 00:19:27,140 No education, terrible diet, terrible life expectancy. 399 00:19:27,620 --> 00:19:28,940 And now we are kind of. 400 00:19:30,185 --> 00:19:32,825 You know, every, probably everyone listening to this podcast has got 401 00:19:32,825 --> 00:19:36,245 a job, which is far more exciting than a, um, than working in a field. 402 00:19:36,635 --> 00:19:41,435 And that's based on technological advances and the population, um, in 403 00:19:41,435 --> 00:19:45,335 the UK has grown from one and a half million to, you know, 70 odd million. 404 00:19:45,665 --> 00:19:47,465 And we have, uh, 4% unemployment. 405 00:19:47,585 --> 00:19:51,095 And, you know, pretty much every, you know, they're obviously still some 406 00:19:51,095 --> 00:19:54,635 farmers, but they're doing it at much better scale thanks to technology. 407 00:19:54,635 --> 00:19:54,725 Mm-hmm. 408 00:19:55,175 --> 00:19:58,085 and everyone else is doing jobs that would be completely impossible 409 00:19:58,085 --> 00:20:02,645 to imagine if you a surf in, you know, the middle, the middle ages. 410 00:20:02,645 --> 00:20:08,135 So I don't think that technology is something to be scared of. 411 00:20:08,165 --> 00:20:12,665 And I also don't buy the argument that we are now at the forefront of some 412 00:20:12,665 --> 00:20:15,155 revolution, which is so much better than any other revolution we've seen 413 00:20:15,155 --> 00:20:18,875 because like that's exactly what the Victorians thought when they're 414 00:20:18,875 --> 00:20:20,045 looking at like the steam engine. 415 00:20:20,045 --> 00:20:23,045 And that's exactly what everyone thought that you know, when you are 416 00:20:23,045 --> 00:20:25,995 looking at your period of, your life. 417 00:20:26,015 --> 00:20:28,245 You're like, okay, this is much bigger than ever before. 418 00:20:28,245 --> 00:20:33,705 But if you look at human history, technology has definitely been a benefit. 419 00:20:33,705 --> 00:20:36,065 And there's kind of, you know, there's a lot of progress 420 00:20:36,065 --> 00:20:37,305 that we could make, with it. 421 00:20:37,325 --> 00:20:41,615 So, you know, it is, it is scary in some sense and the world is gonna change, but, 422 00:20:41,675 --> 00:20:42,845 uh, you know, I think it's very exciting. 423 00:20:44,250 --> 00:20:44,700 Yes. 424 00:20:44,700 --> 00:20:46,710 Uh, and like I, I, I totally agree. 425 00:20:46,710 --> 00:20:49,440 I think it's a really interesting turning point, isn't it? 426 00:20:49,470 --> 00:20:50,580 It's, um mm-hmm. 427 00:20:50,820 --> 00:20:54,120 , I'm kind of curious to see what happens to the lawyer when, uh, 428 00:20:54,150 --> 00:20:56,820 you, I, I mean, I actually have a, a barrister friend of mine who is 429 00:20:57,220 --> 00:21:01,350 actively working on the, the legal version of this kind of AI type thing. 430 00:21:01,780 --> 00:21:02,260 there we go. 431 00:21:02,260 --> 00:21:02,940 There we go. 432 00:21:02,945 --> 00:21:05,280 It's really fascinating what he's getting into. 433 00:21:05,280 --> 00:21:05,340 Yeah. 434 00:21:05,400 --> 00:21:08,130 And the amount he has to spend on computers just to do the 435 00:21:08,130 --> 00:21:09,840 processing is just unbelievable. 436 00:21:09,900 --> 00:21:15,410 Anyway, eyewatering amount of money, but, um, . So I, I, I, I, I'm just kind 437 00:21:15,410 --> 00:21:17,690 of curious to see what happens to, yeah. 438 00:21:17,690 --> 00:21:20,870 That lawyer and the transition that has to happen to take place. 439 00:21:21,500 --> 00:21:25,880 Um, I can see, you know, for me, one of the things that I would love, I would 440 00:21:25,880 --> 00:21:30,380 pay money for actually, is you, is if I didn't have to worry about social media, 441 00:21:30,500 --> 00:21:33,710 you know, how do I build an Instagram following, there's this AI system 442 00:21:33,710 --> 00:21:35,130 over there that goes in that mm-hmm. 443 00:21:35,210 --> 00:21:38,060 , you know, sort of examines your niche and it'll create all the content for you. 444 00:21:38,060 --> 00:21:39,110 It'll build your following. 445 00:21:39,110 --> 00:21:40,640 It'll do it all in an organic way. 446 00:21:40,640 --> 00:21:41,900 It'll be totally natural brilliant. 447 00:21:41,960 --> 00:21:42,650 There you go. 448 00:21:43,190 --> 00:21:44,000 Um, and it will, 449 00:21:44,800 --> 00:21:46,090 that's what we're trying to build. 450 00:21:46,450 --> 00:21:47,980 That's what we're trying to do right now. 451 00:21:48,410 --> 00:21:50,215 Well, I'm your first subscriber man. 452 00:21:50,245 --> 00:21:53,905 Let me, here's my Instagram login. 453 00:21:53,935 --> 00:21:54,535 Go for it.. 454 00:21:55,150 --> 00:21:57,385 Um, cuz I can see. 455 00:21:57,775 --> 00:21:59,215 But a again, I mean mm-hmm. 456 00:21:59,905 --> 00:22:03,205 as soon as it starts doing that, in a few years time, people will just stop watching 457 00:22:03,210 --> 00:22:06,035 social media because they're just gonna, well, maybe they will, maybe they won't. 458 00:22:06,035 --> 00:22:06,445 I don't know. 459 00:22:06,445 --> 00:22:07,546 It'll be an interesting one. 460 00:22:08,185 --> 00:22:10,165 So ecomtent then. 461 00:22:10,165 --> 00:22:14,615 So we we're in the midst of this sort of technological revolution. 462 00:22:14,615 --> 00:22:17,405 And it is, it's both exciting and it's scary. 463 00:22:17,465 --> 00:22:21,305 And yes, there are inevitable opportunities for the savvy 464 00:22:21,305 --> 00:22:23,615 e-commerce entrepreneur, right? 465 00:22:23,620 --> 00:22:24,005 Yes. 466 00:22:24,965 --> 00:22:29,165 And so for you, I'm guessing you've looked at that and gone, right? 467 00:22:29,165 --> 00:22:32,385 This is where, this is where we fit in with ecomtent. 468 00:22:32,405 --> 00:22:36,755 We are gonna create some stuff over here which e-commerce guys can use. 469 00:22:36,760 --> 00:22:37,405 Exactly. 470 00:22:37,565 --> 00:22:38,735 That is generative ai. 471 00:22:38,765 --> 00:22:40,175 We're gonna build on that technology. 472 00:22:40,175 --> 00:22:41,735 We're gonna help you guys rock and roll with it. 473 00:22:42,705 --> 00:22:49,790 Um, so what sort of things then are you guys doing and playing with right now? 474 00:22:50,630 --> 00:22:54,740 So I can, I can say what we're doing right now, which is we are creating 475 00:22:54,800 --> 00:22:56,750 product images and lifestyle images. 476 00:22:56,755 --> 00:23:00,470 So what we do is, um, similar to how I explain how the technology work, 477 00:23:00,860 --> 00:23:03,676 we train, uh, uh, a private AI model. 478 00:23:04,096 --> 00:23:08,300 on, um, you know, three to five photos of a product. 479 00:23:08,600 --> 00:23:08,690 Mm-hmm. 480 00:23:08,930 --> 00:23:14,390 , and then we use generative AI to put that product, um, in any scenario, uh, with 481 00:23:14,390 --> 00:23:20,390 someone of any ethnicity, any gender, any, um, you know, any background, 482 00:23:20,720 --> 00:23:22,260 uh, based on the prompt, so mm-hmm. 483 00:23:22,550 --> 00:23:23,480 , you could say. 484 00:23:23,540 --> 00:23:27,380 Um, I mean, on our website for example, we've got the kind of demo video of 485 00:23:27,380 --> 00:23:32,290 this elephant toy and you could say, um, you know, from the website we go like. 486 00:23:32,460 --> 00:23:35,085 put it with a child on a beach and you see this elephant toy being 487 00:23:35,085 --> 00:23:36,825 held by a child on the beach. 488 00:23:36,835 --> 00:23:37,435 Mm-hmm. 489 00:23:37,520 --> 00:23:40,665 . And, um, we, you know, this is kind of step one and we have 490 00:23:40,665 --> 00:23:42,045 like a very exciting roadmap. 491 00:23:42,555 --> 00:23:47,465 Um, You know, the ambition is to, you know, create all content which is 492 00:23:47,465 --> 00:23:49,535 more optimized using generative ai. 493 00:23:49,535 --> 00:23:53,975 So that's descriptions, bullet points, uh, a plus content, everything we wanna do. 494 00:23:54,195 --> 00:23:54,475 Yeah. 495 00:23:54,545 --> 00:23:57,185 But right now we are kind of, we focus on the hardest bit, 496 00:23:57,185 --> 00:23:58,865 which is a lifestyle imagery. 497 00:23:59,405 --> 00:24:01,475 Um, because we know like that's very painful. 498 00:24:01,480 --> 00:24:06,125 It, you know, to kind of organize these shoots is, um, is a pain 499 00:24:06,130 --> 00:24:08,435 and it's costly and it takes ages. 500 00:24:08,495 --> 00:24:12,965 Um, so we, we do that with generative ai and you can kind of create 501 00:24:13,465 --> 00:24:17,030 Thousands of images instantly with our, with our self-service tool. 502 00:24:17,540 --> 00:24:19,550 Um, so yeah, that's, that's what we're doing. 503 00:24:19,550 --> 00:24:21,290 Um, that's what we're focused on right now. 504 00:24:22,220 --> 00:24:25,340 So if I, I'm just kind of curious here. 505 00:24:25,520 --> 00:24:25,640 Yeah. 506 00:24:25,640 --> 00:24:29,660 Max, if I'm honest with you, cuz we, we, our product shots are 3D 507 00:24:29,660 --> 00:24:34,010 renders on our eCommerce websites because, um, they're so realistic now. 508 00:24:34,010 --> 00:24:37,130 You get much better lighting, you get exactly how you want it, right? 509 00:24:37,700 --> 00:24:37,790 Mm-hmm. 510 00:24:38,060 --> 00:24:39,590 . Um, and it's easy to do for our products. 511 00:24:39,590 --> 00:24:42,800 I've gotta admit, um, like with our supplement brand, for 512 00:24:42,800 --> 00:24:46,190 example, it's a cylindrical bottle with a label on the front. 513 00:24:46,190 --> 00:24:49,130 I mean, he can't get really that much easier, uh, you know, 514 00:24:49,130 --> 00:24:50,600 in terms of 3D generation. 515 00:24:50,600 --> 00:24:50,690 Mm-hmm. 516 00:24:52,035 --> 00:24:55,995 . , we still use a photographer to do the lifestyle shots. 517 00:24:55,995 --> 00:25:00,075 Now obviously they are restricted to, um, I don't pay them to go all over 518 00:25:00,075 --> 00:25:01,335 the world and generate these shots. 519 00:25:01,340 --> 00:25:02,715 They, they sort of do them here. 520 00:25:02,715 --> 00:25:05,535 So I'm restricted to what is available to me here in mm-hmm. 521 00:25:05,615 --> 00:25:06,855 Liverpool in those photo shoots. 522 00:25:07,845 --> 00:25:10,455 Um, so I'm intrigued by what you do. 523 00:25:10,455 --> 00:25:12,555 So I'm thinking of the elephant on the beach. 524 00:25:13,455 --> 00:25:13,695 Yeah. 525 00:25:13,995 --> 00:25:19,305 Is your system then taking a picture of a beach and the elephant and just 526 00:25:19,305 --> 00:25:21,105 being really creative with photoshop. 527 00:25:21,470 --> 00:25:24,080 Or is this beach, which it's generating a beach which has 528 00:25:24,080 --> 00:25:25,430 never ever been seen before. 529 00:25:25,430 --> 00:25:25,970 Exactly. 530 00:25:25,970 --> 00:25:26,570 Correct. 531 00:25:26,630 --> 00:25:27,200 Exactly. 532 00:25:27,200 --> 00:25:30,890 So, um, it's tra you know, we, we have private data sets, we 533 00:25:30,895 --> 00:25:34,250 train it on, but basically it's hundreds of millions of images. 534 00:25:34,640 --> 00:25:38,720 Um, and then it will create a net new image based on your prompt. 535 00:25:38,840 --> 00:25:42,020 Um, so if you say a beach, if you say a beach of sunset, it's gonna kind of 536 00:25:42,020 --> 00:25:47,445 understand the, our model will understand what a person's product is, uh, the 537 00:25:47,785 --> 00:25:51,735 customer's product is, and then it will kind of build that product into a scene. 538 00:25:52,155 --> 00:25:57,195 Um, amalgamating all of the, um, you know, photos on the internet of whatever 539 00:25:57,195 --> 00:26:00,225 the background is and whatever the whoever the person is interacting. 540 00:26:00,675 --> 00:26:04,520 And I wanna make an important point, like, , this is not kind of the, 541 00:26:04,520 --> 00:26:07,880 um, kind of background removal, kind of swapping a background, 542 00:26:07,880 --> 00:26:09,140 which you kind of see everywhere. 543 00:26:09,530 --> 00:26:13,280 This is kind of creating a new photo so your, you know, your elephant with 544 00:26:13,280 --> 00:26:17,840 a child in, in natural setting held, you know, held as, and it's kind of 545 00:26:17,840 --> 00:26:21,770 understood like this is the elephant toy, you know, is similar to a teddy bear. 546 00:26:21,800 --> 00:26:23,210 The teddy bears are held by children. 547 00:26:23,210 --> 00:26:26,000 We've got like millions of images of how they hold them and how they interact 548 00:26:26,000 --> 00:26:29,840 with them and the smart, and it kind of will recreate an image similar to that. 549 00:26:30,755 --> 00:26:33,545 Fundamentally a new image that's never been seen before and also would 550 00:26:33,545 --> 00:26:37,325 like, would never be generated again exactly the same if you gave it the 551 00:26:37,325 --> 00:26:39,095 same prompt a million times over. 552 00:26:40,265 --> 00:26:40,685 Wow. 553 00:26:41,225 --> 00:26:43,745 So, you've got some really clever computers. 554 00:26:43,805 --> 00:26:46,015 Uh, At, uh, gchq. 555 00:26:46,015 --> 00:26:51,545 Yeah, I have a fantastic, uh, fantastic, uh, CTO who is a PhD in ai and Is, is, is 556 00:26:51,545 --> 00:26:56,555 super smart and is kind of building these very complex, uh, very complex models. 557 00:26:57,005 --> 00:26:58,175 Yeah, no kidding. 558 00:26:58,355 --> 00:26:58,925 No kidding. 559 00:26:59,465 --> 00:27:02,990 So that's, um, , that's the lifestyle shots then. 560 00:27:02,990 --> 00:27:03,810 And I, I, yes. 561 00:27:03,810 --> 00:27:05,210 I can see how that can work. 562 00:27:05,330 --> 00:27:08,270 Um, I'm just, I'm kind of, I'm gonna go check out the site and, 563 00:27:08,300 --> 00:27:09,590 and play is what I'm gonna do. 564 00:27:10,100 --> 00:27:11,030 I wanna see the elephant thing. 565 00:27:11,510 --> 00:27:15,230 Um, because yesterday I was on, uh, we use a system called, I dunno if 566 00:27:15,235 --> 00:27:16,460 you've heard of it, called Jasper. 567 00:27:16,520 --> 00:27:17,660 jasper.ai helps 568 00:27:17,660 --> 00:27:18,430 us write content. 569 00:27:18,430 --> 00:27:18,660 Yes. 570 00:27:18,660 --> 00:27:19,330 Jasper ai. 571 00:27:19,330 --> 00:27:19,610 Yeah. 572 00:27:19,610 --> 00:27:19,770 Yeah. 573 00:27:20,615 --> 00:27:23,765 Um, it's a mixed reviews from me really. 574 00:27:23,765 --> 00:27:25,055 Some of it's good, some of it's bad. 575 00:27:25,055 --> 00:27:27,425 They've got this image generation feature on there. 576 00:27:28,025 --> 00:27:33,155 Um, and yesterday, uh, I was like, can you generate for me an 577 00:27:33,155 --> 00:27:35,345 image of a trillion dollar bill? 578 00:27:35,735 --> 00:27:39,095 Um, Because e-commerce for the first time has, has gone through the 579 00:27:39,095 --> 00:27:40,445 trillion dollar mark or whatever it is. 580 00:27:40,445 --> 00:27:44,585 So I'm like, I'd, I'd like to talk about this, uh, on, on our, one 581 00:27:44,585 --> 00:27:45,755 of our LinkedIn lunchtime events. 582 00:27:45,755 --> 00:27:48,275 Yeah, I'd like a, I'd like this image, could not do it. 583 00:27:48,485 --> 00:27:51,275 Um, and it may be the prompts that I were putting in or what, 584 00:27:51,725 --> 00:27:52,775 it was just really interesting. 585 00:27:52,775 --> 00:27:56,065 So there are times when it's good and bad and, um mm-hmm. 586 00:27:56,495 --> 00:27:57,105 , I, I. 587 00:27:58,355 --> 00:27:59,915 , I get how this stuff is helpful. 588 00:28:00,055 --> 00:28:04,265 I think it's, I think it's a different, Jasper kind of pretty much use open 589 00:28:04,265 --> 00:28:08,095 source models, which means they're like, they're, it's a bit like chat GPT. 590 00:28:08,110 --> 00:28:08,855 They're generic. 591 00:28:08,860 --> 00:28:13,655 They can basically do anything but not everything well, if that makes sense. 592 00:28:13,775 --> 00:28:14,115 Yeah, yeah. 593 00:28:14,225 --> 00:28:19,325 Whereas if you had, in that case, like a train model on creating like, you 594 00:28:19,325 --> 00:28:24,545 know, artwork on, on, um, you know, bank notes, which is very niche, but 595 00:28:24,550 --> 00:28:28,205 like you could imagine someone, like if there's a huge demand for this 596 00:28:28,205 --> 00:28:31,025 particular request, then you'd, then you'd be able to do it brilliantly. 597 00:28:31,115 --> 00:28:33,815 So it's just a question of like, this model is kind of 598 00:28:33,815 --> 00:28:35,285 a generic open source model. 599 00:28:35,765 --> 00:28:41,080 Um, and therefore, It can do, you know, it can get pretty good at most things. 600 00:28:41,200 --> 00:28:44,980 Um, first is like a private model, which is going to then like be very 601 00:28:44,980 --> 00:28:48,580 specific at like under understanding British law or like understanding 602 00:28:48,790 --> 00:28:49,950 artwork on this, on this. 603 00:28:50,930 --> 00:28:51,320 Whatever. 604 00:28:51,890 --> 00:28:55,550 Yeah, I get the power of that as well because like you say, you know, these 605 00:28:55,550 --> 00:28:59,180 systems work as well as the data you feed it, so if you are creating an AI 606 00:28:59,185 --> 00:29:02,540 system, which generates a plus content and all you are doing is analyze an 607 00:29:02,540 --> 00:29:06,680 a plus content from really successful listings, which you're gonna know from 608 00:29:06,685 --> 00:29:11,060 your days of Amazon, then I can, I, I look at that and go, that's gonna create much 609 00:29:11,060 --> 00:29:13,670 better resources for me than say Jasper. 610 00:29:13,700 --> 00:29:17,510 And so therefore I'm, I'm, I like that that's there because yeah, that's 611 00:29:17,510 --> 00:29:19,025 where it gets really, really, clever. 612 00:29:19,905 --> 00:29:20,385 Exactly. 613 00:29:20,405 --> 00:29:22,835 That's the, that's the, that's the goal of what we are, we're doing. 614 00:29:22,835 --> 00:29:28,055 Like I think, um, we are gonna move to a world where, like you have a lot of 615 00:29:28,055 --> 00:29:31,265 open source models and then a lot of people building like private models for 616 00:29:31,265 --> 00:29:35,435 specific industries or whatever, kind of using the latest technological advances. 617 00:29:35,675 --> 00:29:38,225 But then like using that in a sense, like, okay, let's apply 618 00:29:38,255 --> 00:29:39,665 this to whatever we're doing. 619 00:29:39,665 --> 00:29:43,145 And kind of building that and using specific data sets like as you 620 00:29:43,145 --> 00:29:46,935 say, all the best performing a plus content, um, to kind of generate 621 00:29:47,075 --> 00:29:48,635 the best outcome for our customers. 622 00:29:49,495 --> 00:29:50,425 Really interesting. 623 00:29:50,575 --> 00:29:51,675 Really interesting. 624 00:29:52,015 --> 00:29:55,645 I do wonder Max, whether at some point mm-hmm. 625 00:29:55,885 --> 00:29:59,935 um, you're gonna get clever enough where I don't even have to have a, you know, that 626 00:29:59,935 --> 00:30:01,585 you go to a web design agency, like Right. 627 00:30:01,585 --> 00:30:02,155 Design for me. 628 00:30:02,155 --> 00:30:03,175 An e-commerce website. 629 00:30:03,955 --> 00:30:04,075 Yeah. 630 00:30:04,080 --> 00:30:06,445 And they'll charge you whatever, 30 grand, 40 grand, 50 grand, 631 00:30:06,875 --> 00:30:07,825 whatever the price is these days. 632 00:30:07,885 --> 00:30:08,065 Yeah. 633 00:30:08,065 --> 00:30:08,815 It's a hundred bucks. 634 00:30:08,815 --> 00:30:12,475 If you go to Shopify, it's probably 120, 150 grand if you 635 00:30:12,475 --> 00:30:14,065 go to a big magento agency. 636 00:30:14,305 --> 00:30:17,990 And if you, you know, Somewhere in between or whatever. 637 00:30:18,470 --> 00:30:21,320 So I'm gonna go and spend, say, 30 grand on a website design. 638 00:30:22,190 --> 00:30:25,820 Is it gonna get to the place where actually I go to people like you and 639 00:30:25,820 --> 00:30:27,800 I say, listen, this is my product. 640 00:30:28,220 --> 00:30:28,830 Mm-hmm. 641 00:30:28,915 --> 00:30:32,360 , um, I think these are my target customers who will buy this product. 642 00:30:32,570 --> 00:30:34,310 Um, based on the research that I've done. 643 00:30:34,700 --> 00:30:38,600 I feed those parameters into an engine and it goes right. 644 00:30:38,960 --> 00:30:43,685 We think based on that data, this is gonna be the best, yeah. 645 00:30:43,685 --> 00:30:48,005 The website for you, both in, in your colors, in layout, in branding. 646 00:30:48,215 --> 00:30:50,875 Here's some content we think you should put on there. 647 00:30:50,875 --> 00:30:51,115 Mm-hmm. 648 00:30:51,245 --> 00:30:57,476 . Oh, and by the way, if, um, a, a man in his thirties comes on, we're actually, and 649 00:30:57,481 --> 00:31:00,785 we know that we are gonna tweak it around, so the colors are slightly different. 650 00:31:00,785 --> 00:31:03,485 That, and, and the website almost morphs and changes. 651 00:31:03,485 --> 00:31:05,135 Depends who, who comes on it. 652 00:31:05,795 --> 00:31:07,505 I think I think, you're completely right. 653 00:31:07,505 --> 00:31:10,805 And that's kind of like our, uh, our like vision statement we have here, 654 00:31:10,805 --> 00:31:12,995 which is like unimaginable creativity. 655 00:31:12,995 --> 00:31:15,995 Like we think this unlocks unimaginable creativity for all of 656 00:31:16,000 --> 00:31:18,665 the creative people we work with, all the social media managers, all the 657 00:31:18,665 --> 00:31:19,955 brand owners, et cetera, et cetera. 658 00:31:20,405 --> 00:31:24,620 And also limitless personalization because now, You can just create an 659 00:31:24,620 --> 00:31:26,480 infinite amount of content, right? 660 00:31:26,660 --> 00:31:26,870 Mm-hmm. 661 00:31:27,200 --> 00:31:31,070 , you can personalize it to an inf uh, to an infinite degree, as you said. 662 00:31:31,070 --> 00:31:33,650 So you could have, like the man in his thirties, he sees this, the 663 00:31:33,770 --> 00:31:35,510 woman in her sixties, she sees that. 664 00:31:36,170 --> 00:31:39,965 And I wanted, I wanna add one more thing, which is like, Um, generative 665 00:31:39,965 --> 00:31:41,495 AI can create code as well. 666 00:31:41,495 --> 00:31:45,275 So it's not just a case that this is gonna say, here's your, 667 00:31:45,335 --> 00:31:46,265 this is what you should do. 668 00:31:46,265 --> 00:31:48,575 It's gonna be, and i, this is something I firmly believe will 669 00:31:48,575 --> 00:31:50,255 happen in the next two, three years. 670 00:31:50,885 --> 00:31:55,105 You already have generative AI code in, Microsoft and Amazon, like Amazon 671 00:31:55,105 --> 00:32:00,155 Whisper, if you're a coder, but, um, you're gonna have, literally a free, 672 00:32:00,185 --> 00:32:01,865 free text like, build me a website. 673 00:32:01,925 --> 00:32:05,600 This is my product, this, as you said, and it will just build the website because 674 00:32:05,600 --> 00:32:12,000 it will be smart enough to do the code as well as understand all the, permutations 675 00:32:12,020 --> 00:32:13,490 on back you know, as you said. 676 00:32:13,490 --> 00:32:16,070 So I think, that's definitely coming. 677 00:32:16,370 --> 00:32:18,980 It's not something that we've, like, we're not focused on that particular 678 00:32:18,985 --> 00:32:22,610 thing, but we are gonna see, you know, I would, I'd bet good amount of money 679 00:32:22,610 --> 00:32:27,630 that we're gonna have kind of generative ai, you know where site creations 680 00:32:27,650 --> 00:32:31,670 and app app creations in, in, you know, in the not too far off future. 681 00:32:33,020 --> 00:32:38,090 So everything that you've talked about, um, you know, 682 00:32:38,090 --> 00:32:40,130 code is very two-dimensional. 683 00:32:40,220 --> 00:32:41,780 Uh, text is very two-dimensional. 684 00:32:41,780 --> 00:32:43,130 Images are very two-dimensional. 685 00:32:43,700 --> 00:32:47,990 Um, are, we getting to the place, I mean, I, I see it a little bit with 686 00:32:47,990 --> 00:32:50,840 deep fake videos, but are we getting to the place where actually I'm gonna be 687 00:32:50,840 --> 00:32:56,315 able to create TV shows for want of a better expression using generative ai. 688 00:32:56,585 --> 00:33:00,635 I think like, I, I think we're probably further off from that, but like we are 689 00:33:00,640 --> 00:33:06,065 looking at videos like, we know that videos are more, um, like kind of get 690 00:33:06,065 --> 00:33:09,605 higher engagement on social media and also a like best practice to have like 691 00:33:09,605 --> 00:33:11,375 a mini video on your Amazon listings. 692 00:33:11,375 --> 00:33:11,495 You. 693 00:33:11,845 --> 00:33:12,625 , you probably know well. 694 00:33:12,955 --> 00:33:15,415 Um, so we definitely, like video is on horizon. 695 00:33:15,415 --> 00:33:20,605 I don't think the tech is there, um, now, but, um, I think that's, um, 696 00:33:20,635 --> 00:33:23,065 that's not beyond the realms of reality. 697 00:33:23,485 --> 00:33:25,795 Um, I think this is gonna really shift. 698 00:33:26,915 --> 00:33:29,975 , you know, this is really gonna shift most, most things. 699 00:33:30,305 --> 00:33:33,905 Um, I think it'd be hard to, hard to number the things that generative 700 00:33:33,905 --> 00:33:37,625 AI won't touch, rather than talk about like what, what it will do. 701 00:33:37,630 --> 00:33:40,745 Because like if you are taking back to basics, like you're 702 00:33:40,745 --> 00:33:41,975 just creating new stuff, right? 703 00:33:41,980 --> 00:33:45,365 Which is what most people do in their jobs day to day, like, and if that can 704 00:33:45,365 --> 00:33:49,775 be done more optimized, quicker, faster, better with more data sets, like it's, 705 00:33:50,015 --> 00:33:52,235 it's hard to see where it doesn't touch. 706 00:33:53,900 --> 00:33:57,740 This can be really interesting and I'm, I'm very tempted to go off in 707 00:33:57,740 --> 00:34:01,370 this conversation, which says, will Netflix not actually have any shows? 708 00:34:01,400 --> 00:34:03,350 You're just gonna, it's gonna understand who you are as a 709 00:34:03,350 --> 00:34:04,430 person and show you stuff. 710 00:34:04,490 --> 00:34:04,610 Yeah. 711 00:34:04,610 --> 00:34:06,560 It's gonna make stuff up on the fly that only you see. 712 00:34:06,560 --> 00:34:06,620 Yeah. 713 00:34:07,640 --> 00:34:11,840 Um, but let's, let's, let's bring it back to a bit more, uh, stuff 714 00:34:11,840 --> 00:34:13,130 that we can do today, right? 715 00:34:13,130 --> 00:34:13,190 Yeah. 716 00:34:13,220 --> 00:34:19,520 So, um, if I'm, if I'm sat here right, and I'm listening to you going, you know, Max. 717 00:34:20,405 --> 00:34:25,235 This all sounds very intriguing, but if you were starting an Amazon business 718 00:34:25,235 --> 00:34:28,235 today, or if you were starting, maybe not Amazon, maybe your own online 719 00:34:28,265 --> 00:34:31,895 e-commerce website today, um, or even you've been around for a while, but 720 00:34:31,895 --> 00:34:35,015 you've not really hit the AI thing yet. 721 00:34:35,105 --> 00:34:39,215 Um, I mean, you've heard a few things on it and you've maybe played on chat 722 00:34:39,215 --> 00:34:43,535 GPT, what are some of the things that I should be thinking about, um, as that 723 00:34:43,535 --> 00:34:47,645 kind of person, what, what, what things are gonna really help me in e-commerce? 724 00:34:48,590 --> 00:34:48,980 Sure. 725 00:34:48,980 --> 00:34:53,840 So I'll, I'll start with the chat GPT, um, because at the moment, at time 726 00:34:53,840 --> 00:34:55,220 of recording, as I say, it's free. 727 00:34:55,280 --> 00:34:58,610 And by the way, I think they've done the most incredible marketing campaign ever. 728 00:34:58,610 --> 00:34:59,510 That's been unbelievable 729 00:34:59,630 --> 00:35:04,310 to, to release it for free, get everyone on all these podcasts and LinkedIn to be 730 00:35:04,310 --> 00:35:05,750 like, these are the ways you can use it. 731 00:35:05,755 --> 00:35:07,740 And then just charge two months later. 732 00:35:07,740 --> 00:35:10,740 I've already had the email saying it's gonna be 42 or whatever it is. 733 00:35:11,150 --> 00:35:11,641 Yeah, yeah. 734 00:35:11,646 --> 00:35:14,280 Which I haven't, I mean, I use it almost daily and I 735 00:35:14,280 --> 00:35:15,630 haven't, I haven't done it yet. 736 00:35:15,630 --> 00:35:16,200 I don't. 737 00:35:16,200 --> 00:35:19,110 Anyway, , I think to, to answer your question, so you 738 00:35:19,115 --> 00:35:20,430 are a new e-commerce person. 739 00:35:20,430 --> 00:35:25,685 Number one thing I would do, is to, um, you know, let's say you know that 740 00:35:25,685 --> 00:35:29,885 you want to go into a specific category like toys, cuz we talked about toys. 741 00:35:29,890 --> 00:35:30,125 Mm-hmm. 742 00:35:30,575 --> 00:35:33,575 . So I think the first thing you could do is to use chat GTP to 743 00:35:33,575 --> 00:35:38,585 help you analyze the reviews, um, on competitors and understand what 744 00:35:38,585 --> 00:35:40,895 customers are looking for in terms of. 745 00:35:41,585 --> 00:35:44,675 Um, you know, what they like and what they dislike, and you know, you can, you can 746 00:35:44,675 --> 00:35:48,245 flip it either way, but you can literally say, as long as this, by the way, again, 747 00:35:48,305 --> 00:35:54,035 as long as the ASIN is before 2021, or you kind of copy and paste manually. 748 00:35:54,365 --> 00:35:56,565 You could, you can, if, if the, if it's an old ASIN, you 749 00:35:56,565 --> 00:35:57,635 could just paste in the link. 750 00:35:57,695 --> 00:35:58,485 It's chat GPT. 751 00:35:58,505 --> 00:36:01,905 If it's an, if it's a newer asin, you could just copy in all of the customer 752 00:36:01,905 --> 00:36:04,760 reviews, place that in and then say, analyze reviews on the on this. 753 00:36:05,120 --> 00:36:08,720 But you could say, you know, analyze reviews on this ASIN you're gonna give 754 00:36:08,720 --> 00:36:11,720 me, tell me what the customers love, or give me product recommendations. 755 00:36:11,960 --> 00:36:14,480 And then it will kind of spit out like the key takeaways. 756 00:36:15,320 --> 00:36:19,640 , um, for you to look at, and then that kind of, that will kind of set you off 757 00:36:19,640 --> 00:36:23,720 to say, okay, people love this toy, but, uh, they've complained that it's, 758 00:36:23,725 --> 00:36:27,560 um, got a few defects and the stuffings uneven, or that it's not the size is, 759 00:36:27,590 --> 00:36:30,320 you know, it is too small when they thought it'd be bigger by the photos 760 00:36:30,320 --> 00:36:32,660 or, you know, blah, blah blah, whatever. 761 00:36:32,660 --> 00:36:37,865 So that kind of gives you an idea of, this is my product that I want to launch. 762 00:36:38,465 --> 00:36:40,775 Um, another good way of, 763 00:36:40,775 --> 00:36:41,585 um, sorry. 764 00:36:41,585 --> 00:36:44,345 Before you get into the second one, Max, lemme just clarify something there. 765 00:36:44,735 --> 00:36:49,415 So I could go and grab the URL off an Amazon product listing. 766 00:36:49,445 --> 00:36:49,745 Yes. 767 00:36:50,195 --> 00:36:53,675 Paste that into chat GPT and with a command, which says 768 00:36:53,675 --> 00:36:57,185 something along the lines of, analyze the reviews on this page. 769 00:36:57,795 --> 00:36:58,215 Yes. 770 00:36:58,220 --> 00:37:02,375 And tell me, you know, the things that people love about this product 771 00:37:02,435 --> 00:37:06,215 and the things that people, you know, that, that what are the main 772 00:37:06,215 --> 00:37:07,475 complaints about this product? 773 00:37:07,565 --> 00:37:07,985 Yeah. 774 00:37:07,990 --> 00:37:08,015 Yeah. 775 00:37:08,405 --> 00:37:08,495 Mm-hmm. 776 00:37:08,895 --> 00:37:09,755 hit the enter button. 777 00:37:10,055 --> 00:37:11,825 I'm sorry to be over simplistic. 778 00:37:11,855 --> 00:37:15,935 Uh, I, I need it to be simple for me to understand, um, hit the enter 779 00:37:15,935 --> 00:37:19,115 button and it's gonna come up with a whole bunch of recommendations for me. 780 00:37:19,120 --> 00:37:20,645 Is it as simple as that or am I 781 00:37:20,645 --> 00:37:21,095 It is as simple as that. 782 00:37:21,095 --> 00:37:22,535 It is as simple as that. 783 00:37:22,565 --> 00:37:23,055 Okay. 784 00:37:23,215 --> 00:37:24,635 So I don't need to be more descriptive. 785 00:37:25,055 --> 00:37:25,835 In my commands. 786 00:37:25,895 --> 00:37:29,705 Um, I think there's a very interesting point, which is like, prompt 787 00:37:29,705 --> 00:37:36,455 engineering is gonna become a, a very important, uh, like future job type. 788 00:37:36,575 --> 00:37:36,635 Yeah. 789 00:37:36,695 --> 00:37:39,275 Where like, how, how do you interact with the ai? 790 00:37:39,275 --> 00:37:40,925 Like what are the best prompts to say? 791 00:37:41,315 --> 00:37:45,095 I mean, I've got a few things that I've noticed, which I can share, 792 00:37:45,275 --> 00:37:48,635 which I use when I'm writing blogs on my website or whatever, whatever, 793 00:37:48,845 --> 00:37:49,775 which would been interesting. 794 00:37:50,165 --> 00:37:51,970 Um, but, um, Yes. 795 00:37:51,970 --> 00:37:52,870 Like, it's as simple as that. 796 00:37:52,870 --> 00:37:56,110 You say, what are, you know, what are the gimme five takeaways on like, um, 797 00:37:56,290 --> 00:38:00,160 what people love or gimme five takeaways for product improvement or whatever. 798 00:38:00,160 --> 00:38:01,900 And it will understand and it'll give it to you. 799 00:38:01,900 --> 00:38:04,060 So that's kind of a very good starting point. 800 00:38:04,540 --> 00:38:09,100 Um, if you wanted to start with, um, you know, start, start a 801 00:38:09,100 --> 00:38:12,790 business as you said on like, with a, like a, you know, D2C business. 802 00:38:14,650 --> 00:38:15,400 That's incredible. 803 00:38:15,490 --> 00:38:15,970 Okay. 804 00:38:15,970 --> 00:38:16,570 I've got that. 805 00:38:16,570 --> 00:38:17,350 That was number one. 806 00:38:17,500 --> 00:38:18,430 Uh, what's the number two? 807 00:38:20,465 --> 00:38:24,905 Um, so you've got your product, um, you've done all the hard stuff, which is, uh, 808 00:38:24,935 --> 00:38:27,485 manufacturing and, and everything else. 809 00:38:27,485 --> 00:38:32,465 I guess next, you know, at some point you're gonna have to look at, um, you 810 00:38:32,465 --> 00:38:34,625 know, keywords, seo, that kind of stuff. 811 00:38:35,080 --> 00:38:39,370 So, um, and I've view this, um, in my, in my business, I say, you know, give 812 00:38:39,370 --> 00:38:42,280 me a, you know, literally give me a list of a hundred search phrases for a 813 00:38:42,280 --> 00:38:46,000 startup sell self-service tool to create lifestyle images for generative ai. 814 00:38:46,300 --> 00:38:50,020 And it will just give you a hundred keyword words and it will say, and 815 00:38:50,020 --> 00:38:54,550 what's brilliant is that I'm obviously British and I like will think, you know, 816 00:38:54,550 --> 00:38:57,640 like I phrased, if you read the, the website or whatever, you'll, you'll, 817 00:38:57,640 --> 00:38:59,020 it's obviously very come from me. 818 00:38:59,110 --> 00:39:01,865 Um, and like I have a certain way of talking about my business. 819 00:39:01,885 --> 00:39:05,935 But like what you can do at chat GPT is think about a hundred ways 820 00:39:05,935 --> 00:39:07,405 of like describing the same thing. 821 00:39:07,465 --> 00:39:09,955 And like all of, you know, not all of them are gonna be great, but 822 00:39:09,955 --> 00:39:13,135 it's helpful to see, okay, these are some things that I've missed. 823 00:39:13,135 --> 00:39:15,925 Like these are some keywords which are interesting or whatever, and I can, you 824 00:39:15,925 --> 00:39:21,205 know, I can better optimize, um, your, you know, your Amazon bidding or whatever, 825 00:39:21,205 --> 00:39:23,395 or your, your Google, um, based on that. 826 00:39:23,395 --> 00:39:25,375 So I guess that would be my number two. 827 00:39:28,795 --> 00:39:29,125 Okay. 828 00:39:29,355 --> 00:39:29,705 So. 829 00:39:30,410 --> 00:39:35,870 I'm just trying to, so you are now using chat GPT for keyword research. 830 00:39:35,930 --> 00:39:36,420 Yeah. 831 00:39:36,620 --> 00:39:36,860 Um, 832 00:39:37,370 --> 00:39:39,180 again, it's not, it. 833 00:39:39,695 --> 00:39:44,045 It, I, I like, I know what chat GPT is, which is a natural language 834 00:39:44,045 --> 00:39:47,615 model and therefore like, is very good at doing natural language. 835 00:39:47,615 --> 00:39:49,895 So it will rephrase things very well for me. 836 00:39:50,345 --> 00:39:56,975 Um, so like, you know, it can be a bit of a mouthful explaining what I do, um, cuz 837 00:39:56,975 --> 00:39:59,015 it's a new technology and it's whatever. 838 00:39:59,015 --> 00:40:03,185 But like using that will help me like see 10 different ways I can or 10 different 839 00:40:03,185 --> 00:40:05,435 Google potential Google searches, uh, or. 840 00:40:06,535 --> 00:40:07,555 Or 10 different keywords. 841 00:40:07,555 --> 00:40:08,035 Exactly. 842 00:40:08,405 --> 00:40:08,895 Okay. 843 00:40:08,900 --> 00:40:14,605 Um, so I mean, it's not, it's not based off any, like, it's not based 844 00:40:14,610 --> 00:40:19,075 off any inherent data, but it's just, it's, it's a good reference point to 845 00:40:19,075 --> 00:40:20,935 see different phrases and keywords. 846 00:40:21,655 --> 00:40:22,885 So do you. 847 00:40:23,480 --> 00:40:24,320 I, I'm just trying to think. 848 00:40:24,320 --> 00:40:25,100 I mean, it's great. 849 00:40:25,100 --> 00:40:26,840 You can use it for SEO and keywords. 850 00:40:26,840 --> 00:40:27,800 I, I think mm-hmm. 851 00:40:28,040 --> 00:40:30,800 , um, I extrapolating that out. 852 00:40:30,800 --> 00:40:34,880 I'm thinking, can you use it then, or how would you use it? 853 00:40:34,910 --> 00:40:36,410 Uh, cuz can is a wrong phrase. 854 00:40:36,410 --> 00:40:40,940 How would you use it to generate, say, content idea or content 855 00:40:40,940 --> 00:40:43,130 ideas for your blogs, for example? 856 00:40:43,200 --> 00:40:43,440 Yes. 857 00:40:43,440 --> 00:40:45,440 So this is a, is that, this is a great one. 858 00:40:45,440 --> 00:40:46,130 Oh, that's, yeah. 859 00:40:46,130 --> 00:40:52,595 So you can, like, again, it's, you know, it's very good at understanding language. 860 00:40:52,600 --> 00:40:54,605 So if you say, you know, you gave the example. 861 00:40:55,355 --> 00:40:57,365 Your, um, what, what was your product? 862 00:40:57,365 --> 00:40:58,865 You said the kind of, uh, the cylinder. 863 00:40:58,865 --> 00:40:59,435 The supplement? 864 00:40:59,735 --> 00:41:00,065 Yeah. 865 00:41:00,065 --> 00:41:00,125 Yeah. 866 00:41:00,125 --> 00:41:00,775 The supplement. 867 00:41:00,775 --> 00:41:01,175 Supplement, yeah. 868 00:41:01,175 --> 00:41:05,475 So you could say, write a, write a blog or write an Instagram ad targeting 869 00:41:05,495 --> 00:41:09,185 this supplement at British men in their, you know, early thirties. 870 00:41:09,185 --> 00:41:15,040 And it would write you, um, A, an ad which like is, you know, using British, 871 00:41:15,130 --> 00:41:20,230 um, slang using kind of British calls to action, like the gym, the office, like 872 00:41:20,230 --> 00:41:21,550 it will understand all that very well. 873 00:41:21,550 --> 00:41:25,810 And then you could say, okay, now do it for, you know, fifties to sixties women, 874 00:41:25,810 --> 00:41:30,730 and it'll give you completely different, um, slang and, uh, well phrases and, you 875 00:41:30,730 --> 00:41:32,740 know, like, um, how they would use it. 876 00:41:32,740 --> 00:41:36,310 And I mean, it, it like, this is something that's brilliant that is, is 877 00:41:36,310 --> 00:41:39,400 very good at, so if you kind of put in like, yeah, write an Instagram ad for 878 00:41:39,400 --> 00:41:41,250 my supplement, this is my target market. 879 00:41:41,970 --> 00:41:45,860 you can generate, you know, 50 different texts and then you can swap your 880 00:41:45,860 --> 00:41:49,340 target market and it can kind of show you different, like it will again. 881 00:41:49,345 --> 00:41:52,250 It's a good way to kind of, oh, I didn't think of that because like, 882 00:41:52,250 --> 00:41:56,270 I'm not a woman in my sixties, but now like, you know, I've got this, 883 00:41:56,420 --> 00:41:59,150 I've got this, and it just g it's good for giving you some ideas for sure. 884 00:42:00,230 --> 00:42:00,620 Wow. 885 00:42:00,680 --> 00:42:03,380 And again, you're just literally heading over to chat GPT and typing 886 00:42:03,380 --> 00:42:07,760 that in the Yes, in the, in the, this is, I'm, I'm really intrigued 887 00:42:07,760 --> 00:42:09,290 the more you are talking and. 888 00:42:09,815 --> 00:42:10,425 . Mm-hmm. 889 00:42:10,505 --> 00:42:14,315 . The more I understand what you mean by this idea of prompt engineering, the 890 00:42:14,315 --> 00:42:19,865 people that will, that know what to type into chat GPT then become Yeah. 891 00:42:19,865 --> 00:42:23,285 The ones which are currently gonna be the ones which are highly sought after, right. 892 00:42:23,285 --> 00:42:24,515 Because it's, yeah, definitely. 893 00:42:24,520 --> 00:42:28,385 I, the, the data you get out is all dependent on the prompts that you write 894 00:42:28,385 --> 00:42:29,905 and the information that you give it. 895 00:42:29,905 --> 00:42:30,105 Yes. 896 00:42:30,635 --> 00:42:32,545 So what was some of the Go on, 897 00:42:32,545 --> 00:42:32,705 yeah. 898 00:42:33,515 --> 00:42:36,755 I'll just say you like, it helps to have an understanding of the model, 899 00:42:36,755 --> 00:42:41,765 what's it's trained on, what the, like, what the data sets are because, um, 900 00:42:41,825 --> 00:42:44,765 you know, like different, again, chat GPT is a generic model trained on, 901 00:42:44,885 --> 00:42:47,195 like, we actually, we dunno what it's trained on, but they say it's trained 902 00:42:47,195 --> 00:42:49,355 on data on the internet for 2021. 903 00:42:49,415 --> 00:42:50,025 Um, yeah. 904 00:42:50,195 --> 00:42:52,985 You know, they, they could have trained it on, they could have, I imagine 905 00:42:52,985 --> 00:42:56,735 they did ignore specific websites and, you know, otherwise it'd be saying 906 00:42:57,095 --> 00:43:00,215 horrible things about certain people if they had kind of trained it on the, 907 00:43:00,215 --> 00:43:01,925 or, you know, the entire internet. 908 00:43:02,225 --> 00:43:05,855 So I'm like, so we didn't really know what, but it's trained on, but 909 00:43:05,855 --> 00:43:08,615 like for, you know, for our private models, we know what it's trained on, 910 00:43:08,620 --> 00:43:10,325 we know what, we know what works well. 911 00:43:10,505 --> 00:43:14,455 We can like tailor it to, you know, specific categories, which we do. 912 00:43:15,245 --> 00:43:17,735 Um, and, and that kind of, yeah. 913 00:43:18,035 --> 00:43:21,005 So I guess that's a big thing that we are thinking about is like, how can we 914 00:43:21,005 --> 00:43:23,045 help customers Is prompt engineering. 915 00:43:24,650 --> 00:43:27,680 You know, for example, we do a lot of work in furniture and we like train 916 00:43:27,680 --> 00:43:31,460 it on different rooms, aesthetics, rustic rooms, candid room, modern room. 917 00:43:31,670 --> 00:43:36,740 But it's about like, how do we communicate to the customer, um, like, you know, this 918 00:43:36,745 --> 00:43:39,980 is what it, this is what the model knows and understands it's been trained on. 919 00:43:39,980 --> 00:43:42,560 And like, it understands different furniture types. 920 00:43:42,565 --> 00:43:47,270 You can put your, you know, your armchair in like a classic, uh, you 921 00:43:47,270 --> 00:43:49,880 know, rustic room by a fireplace, and that would be brilliant. 922 00:43:49,880 --> 00:43:51,680 And then you could put it in a, like a modern. 923 00:43:53,135 --> 00:43:53,825 Whatever. 924 00:43:54,035 --> 00:43:57,665 And like it understands different, um, like a French star room 925 00:43:57,665 --> 00:44:02,105 and like a whate like different cult like device kind of suite. 926 00:44:02,105 --> 00:44:02,915 And it's all golden. 927 00:44:02,915 --> 00:44:06,815 So it un like how, how, how do, how do you Yeah, so that's a big thing as well. 928 00:44:06,815 --> 00:44:10,955 The, the, like, we, we think about every day it's kind of, it's um, you know, you 929 00:44:10,955 --> 00:44:14,825 can really do anything, but like, how do we help people to like, understand the 930 00:44:14,830 --> 00:44:20,450 technology and understand like, What works well, um, uh, in your, you know, what 931 00:44:20,450 --> 00:44:23,200 we, what we've designed it to work, what we've designed it to work well, right? 932 00:44:23,200 --> 00:44:23,680 Mm-hmm. 933 00:44:23,765 --> 00:44:26,630 , um, which is like category specific for us. 934 00:44:27,140 --> 00:44:27,320 Um, 935 00:44:29,405 --> 00:44:34,195 so what are some of the, um, what are some of the prompt discoveries 936 00:44:34,200 --> 00:44:37,385 that you've made using chat GPT then, which is just the one, 937 00:44:37,390 --> 00:44:42,725 I'll give you my, my, my personal top tip, which is, and, and I will, you know, 938 00:44:42,815 --> 00:44:44,495 I will not make a, we are both British. 939 00:44:44,495 --> 00:44:46,985 I will not make a comment on Boris Johnson's politics because, 940 00:44:47,255 --> 00:44:48,455 uh, it's not what I'm gonna do. 941 00:44:48,665 --> 00:44:52,895 But the man is undeniably an extremely talented writer. 942 00:44:52,895 --> 00:44:54,485 He was a journalist for. 943 00:44:55,040 --> 00:44:58,460 Um, you know, 40 years or however long he was a journalist for. 944 00:44:58,820 --> 00:45:03,620 And he has a very distinctive, kind of upbeat, charismatic writing style. 945 00:45:04,130 --> 00:45:04,220 Yeah. 946 00:45:04,250 --> 00:45:07,850 Um, that is, that's, you know, quite unique and actually quite engaging to 947 00:45:07,850 --> 00:45:11,150 read again, like I'm, I'm not gonna, you know, I'm not commenting on, on 948 00:45:11,155 --> 00:45:12,620 the, the, him as a political figure. 949 00:45:12,620 --> 00:45:14,600 I'm talking about him as a, as a journalist. 950 00:45:14,660 --> 00:45:14,780 Yeah. 951 00:45:14,780 --> 00:45:20,720 And I have noticed, or I know that, you know, It is the chat GPT seems to 952 00:45:20,720 --> 00:45:24,410 have been trained on a lot of articles, and many of those articles have been 953 00:45:24,415 --> 00:45:28,160 written by Boris Johnson, who's pumped out thousands, you know, nearly as 954 00:45:28,160 --> 00:45:32,030 many articles as children over his, uh, time as, uh, as a journalist, 955 00:45:33,320 --> 00:45:38,120 And, um, and it is, you know, if you write a blog, it's, it can be quite boring. 956 00:45:38,550 --> 00:45:40,380 And like this is the thing. 957 00:45:40,380 --> 00:45:43,470 And then I will say, rewrite this in the style of Boris Johnson. 958 00:45:43,650 --> 00:45:48,030 Make it jovial yet informative, and you'll have this kind of brilliantly 959 00:45:48,090 --> 00:45:54,080 upbeat, um, You know, fun kind of style of, of, of blog writing. 960 00:45:54,080 --> 00:45:57,740 So, um, if you head over to the, the blog on my website, hopefully you'll 961 00:45:57,740 --> 00:45:59,180 see the blogs are quite entertaining. 962 00:45:59,330 --> 00:46:01,990 I mean, they're not like hilarious, but they're kind of They're, 963 00:46:01,990 --> 00:46:03,200 they're, they're more entertaining. 964 00:46:03,200 --> 00:46:07,760 And that's because I've kind of copied, um, you know, I, I've copied as he, you 965 00:46:07,765 --> 00:46:12,170 know, as he probably has done many times, copied his, um, his writing style for 966 00:46:12,175 --> 00:46:12,860 brilliant, uh, brilliant. 967 00:46:13,305 --> 00:46:14,105 Brilliant. 968 00:46:14,115 --> 00:46:14,985 So that's one that I use. 969 00:46:15,615 --> 00:46:16,065 Okay. 970 00:46:16,575 --> 00:46:20,865 So you are using chat GPT to write blogs in the style of Boris Johnson. 971 00:46:20,955 --> 00:46:21,705 Um, yes. 972 00:46:21,765 --> 00:46:26,535 You are using it to find keywords, content, ideas, um, understand products. 973 00:46:26,715 --> 00:46:29,355 Is there anything else that we need to, to think about? 974 00:46:29,755 --> 00:46:30,105 Uh, 975 00:46:30,710 --> 00:46:34,490 Um, the last one, the last one I'll go into is natural language translation. 976 00:46:34,550 --> 00:46:38,270 Again, like remembering what chat GPT is, it's natural language. 977 00:46:38,270 --> 00:46:43,820 So if you do Google trans, like no good seller should ever be doing 978 00:46:43,820 --> 00:46:45,390 Google Translate for their license. 979 00:46:45,390 --> 00:46:47,010 Like, no, no, no, no, no. 980 00:46:47,300 --> 00:46:52,550 But if you say, rewrite this ASIN in German, use German. 981 00:46:52,615 --> 00:46:57,025 You know, phrases, um, and it will kind of re, you know, I'd like, I don't 982 00:46:57,025 --> 00:46:59,935 speak German, but I've tested this in Spanish with my Spanish friends. 983 00:46:59,965 --> 00:47:03,445 You know, rewrite, rewrite this in Spanish, using Spanish phrases and it'll 984 00:47:03,445 --> 00:47:08,515 write it in, you know, invoke here in Spanish using kind of like phrases, 985 00:47:08,755 --> 00:47:10,165 you know, you like different languages. 986 00:47:10,170 --> 00:47:13,685 So we have kind of many metaphors and synonyms, which make, translated, 987 00:47:13,685 --> 00:47:15,385 make zero sense in other languages. 988 00:47:15,715 --> 00:47:15,985 Yeah. 989 00:47:17,825 --> 00:47:21,245 , like, you know, making a mountain out of a mole hill or something like this, 990 00:47:21,245 --> 00:47:24,485 which like if you translate it into Spanish, like would just make zero sense. 991 00:47:24,845 --> 00:47:24,965 Yeah. 992 00:47:24,965 --> 00:47:28,085 And that's kind of one of the challenges of translation is that like a 993 00:47:28,435 --> 00:47:33,185 deterministic AI will translate verbatim word for word and it will sound weird. 994 00:47:33,365 --> 00:47:38,075 Whereas a generative AI understands the key themes and the key concepts 995 00:47:38,080 --> 00:47:41,435 of the listing, and then it'll translate that into the other 996 00:47:41,435 --> 00:47:43,285 language and it'll create like net new 997 00:47:43,635 --> 00:47:46,970 phrases and synonyms and metaphors to replace ones you had. 998 00:47:46,970 --> 00:47:51,350 So that's a, um, you know, that would be kind of, My fifth tip. 999 00:47:51,590 --> 00:47:57,410 Um, or like I'd say, uh, with chat GTP is very good in natural language translation. 1000 00:47:57,410 --> 00:48:01,550 That's literally kind of like what the research, that's how this was discovered. 1001 00:48:01,610 --> 00:48:05,750 Like the, the initial research into gans and, you know, uh, 1002 00:48:06,230 --> 00:48:09,380 everything I talked about at the beginning was, was, was initially. 1003 00:48:10,105 --> 00:48:12,775 Thinking about translation and how do you solve for that translation problem? 1004 00:48:12,775 --> 00:48:13,015 Yeah. 1005 00:48:13,025 --> 00:48:13,145 Yeah. 1006 00:48:13,150 --> 00:48:15,235 So it's, it's excellent at doing that. 1007 00:48:15,235 --> 00:48:16,915 And yeah, I, I'd recommend using it. 1008 00:48:17,575 --> 00:48:18,235 Fantastic. 1009 00:48:18,295 --> 00:48:18,955 So there you go. 1010 00:48:18,955 --> 00:48:22,845 Top tips for using chat GPT's tool, which is technically still free, although. 1011 00:48:22,845 --> 00:48:24,385 I think at some point, like you say, they're, 1012 00:48:25,045 --> 00:48:26,635 you've gotta get this podcast out quickly, Matt 1013 00:48:28,985 --> 00:48:30,625 Otherwise just have to pay for it. 1014 00:48:31,045 --> 00:48:34,105 To be fair, it's still worth, if you're gonna do all of that, it's still 1015 00:48:34,105 --> 00:48:35,575 worth the 40 bucks a month, right? 1016 00:48:35,575 --> 00:48:38,245 So, um, just subscribe and have a go and see how you going. 1017 00:48:38,785 --> 00:48:43,810 Now what I need is an affiliate link for chat GPT 1018 00:48:43,830 --> 00:48:45,780 . I can, I can give you one for ecomtent. 1019 00:48:45,800 --> 00:48:48,800 Unfortunately I'm not, uh, you know, I'm not Sam Altman. 1020 00:48:50,150 --> 00:48:51,230 No, no, it's fine. 1021 00:48:52,280 --> 00:48:54,380 But um, so well let's talk about ecomtent then. 1022 00:48:54,380 --> 00:48:57,470 So I'm really curious in the last few minutes of the show, bearing 1023 00:48:57,470 --> 00:48:59,420 in mind that we've talked about, you know, there's free stuff. 1024 00:48:59,420 --> 00:49:03,230 Obviously ecomtent will be a paid system, but it's gonna be much more niche. 1025 00:49:03,260 --> 00:49:03,590 Right. 1026 00:49:04,070 --> 00:49:07,490 And I get that cuz you've not got Bill Gates or whoever funding, 1027 00:49:07,490 --> 00:49:07,610 yeah. 1028 00:49:07,990 --> 00:49:08,150 Yeah. 1029 00:49:08,570 --> 00:49:09,110 . No, we don't. 1030 00:49:09,170 --> 00:49:09,470 Um, 1031 00:49:09,950 --> 00:49:15,800 so, so you, where do you see the future going for you guys? 1032 00:49:15,805 --> 00:49:19,190 What sort of things do you, do you think will be starting to happen? 1033 00:49:20,705 --> 00:49:23,375 Um, for us specifically, I'm super excited. 1034 00:49:23,375 --> 00:49:27,935 As I say, like the goal of ecomtent is to bring this generative ai, uh, 1035 00:49:27,935 --> 00:49:30,515 revolution to the e-commerce industry. 1036 00:49:30,515 --> 00:49:33,815 Like I've worked with hundreds of sellers, I've got many friends who are 1037 00:49:33,815 --> 00:49:37,655 sellers, um, you know, many professional relationships I've built over the years. 1038 00:49:38,165 --> 00:49:40,790 Um, I really kind of admire you. 1039 00:49:40,850 --> 00:49:43,490 I've always admire their kind of entrepreneurial spirit and 1040 00:49:43,490 --> 00:49:47,690 their, um, kind of savviness, like super savvy, the savviest people. 1041 00:49:47,900 --> 00:49:51,890 You know, I've come across and we are, we're, we are just gonna focus on like 1042 00:49:52,010 --> 00:49:55,340 bringing generative AI to E-commerce. 1043 00:49:55,340 --> 00:49:57,530 And one of the things we haven't done so far is text. 1044 00:49:58,040 --> 00:50:01,340 And the reason is like, I didn't want to charge, I didn't want to 1045 00:50:01,340 --> 00:50:04,340 even begin to charge people for something which was fundamentally free. 1046 00:50:04,760 --> 00:50:06,120 Um, in chat GPT. 1047 00:50:06,140 --> 00:50:07,250 Now it's not free. 1048 00:50:07,610 --> 00:50:09,080 Um, well, when it's not free. 1049 00:50:09,085 --> 00:50:12,200 I think it's very interesting because like that gives us the opportunity to 1050 00:50:12,200 --> 00:50:17,270 say like, we, like if you want to use, um, generator AI for descriptions and 1051 00:50:17,270 --> 00:50:24,560 bullet points, like you can pay $42 a month at, um, um, Uh, for at OpenAI, like 1052 00:50:24,565 --> 00:50:28,640 our prices pretty much similar, but also we'll be doing it specifically for like 1053 00:50:28,640 --> 00:50:30,710 Amazon descriptions and add bullet point. 1054 00:50:30,715 --> 00:50:33,950 So I think that's something that we'll definitely be focused on. 1055 00:50:33,950 --> 00:50:37,790 As I mentioned, we want go to all content, so that's video, texts I just said. 1056 00:50:38,420 --> 00:50:40,250 Um, what else? 1057 00:50:40,340 --> 00:50:45,290 Um, I think we want to look at, um, we want to continue to look at. 1058 00:50:45,905 --> 00:50:49,805 Optimization and make sure that like, we are, like we're, we are creating 1059 00:50:49,805 --> 00:50:55,175 like optimized content based on like, um, likes and this kind of stuff. 1060 00:50:55,175 --> 00:50:57,095 So it's super exciting space. 1061 00:50:57,185 --> 00:51:02,075 Um, we're, um, we are, we are like very wary of kind of like darley in 1062 00:51:02,080 --> 00:51:05,105 these free tools and we are like, we are very focused on number one thing 1063 00:51:05,110 --> 00:51:09,155 we we're focused on is quality and like, if you do use our tool, you'll 1064 00:51:09,155 --> 00:51:12,635 see like the images of the products, they never get, they're never weird. 1065 00:51:12,695 --> 00:51:16,175 Or like, they're hardly ever, like, only like 1% or 2% of them are 1066 00:51:16,175 --> 00:51:18,485 kind of weird, like AI kind stuff. 1067 00:51:18,485 --> 00:51:21,155 Like we, we, we continue always to focus on quality. 1068 00:51:21,155 --> 00:51:25,535 We wanna be the, the, um, we wanna be the best, um, at this. 1069 00:51:25,540 --> 00:51:30,275 And, uh, Like as competition comes, which I'm sure it will, we want to just 1070 00:51:30,275 --> 00:51:34,025 be the, the leader that, you know, you can, you can't trade and you, you know, 1071 00:51:34,385 --> 00:51:36,845 maybe people will undercut us some price, but like, we'll still be the 1072 00:51:36,845 --> 00:51:40,945 best in terms of like building images that look super realistic and, you know, 1073 00:51:40,955 --> 00:51:42,275 you can take your product anywhere. 1074 00:51:43,295 --> 00:51:43,685 Yeah. 1075 00:51:43,690 --> 00:51:46,265 And I think you've got a real in, I'm, I'm curious to see where 1076 00:51:46,265 --> 00:51:47,255 your journey takes you, mate. 1077 00:51:47,285 --> 00:51:51,125 Cause I think you've got a real interesting competitive edge in 1078 00:51:51,125 --> 00:51:55,265 all the time you spent in Amazon um, and I can see why you would 1079 00:51:55,385 --> 00:51:56,705 double down on that side of this. 1080 00:51:56,710 --> 00:51:57,425 Yeah, exactly. 1081 00:51:57,425 --> 00:52:02,615 I managed catalog quality for the launch of Amazon Singapore, so I, um, yeah, I 1082 00:52:02,615 --> 00:52:04,535 kind of really understand this space. 1083 00:52:04,535 --> 00:52:08,105 It's quite fortunate and, um, so, uh, so yeah, 1084 00:52:08,915 --> 00:52:10,775 it's a really interesting, uh, thing. 1085 00:52:11,015 --> 00:52:13,565 So Max, listen, it's been great chatting to you. 1086 00:52:13,565 --> 00:52:14,105 Really has. 1087 00:52:14,105 --> 00:52:15,425 How do people reach you? 1088 00:52:15,425 --> 00:52:17,305 How do they connect with you if they want to do that? 1089 00:52:18,115 --> 00:52:29,330 So, um, we have a 20% off promo code at our website, which is ecomtent.ai/promo. 1090 00:52:29,360 --> 00:52:33,860 And that will give people 20% off if they wanna trial the tool. 1091 00:52:33,860 --> 00:52:40,040 You can also book a demo, um, um, so yeah, like just head over to ecomtent.ai 1092 00:52:40,280 --> 00:52:44,120 and, uh, you can, you can see everything we've talked about there, and hopefully 1093 00:52:44,120 --> 00:52:46,730 like, depending on when this is released, you may see more of the 1094 00:52:46,730 --> 00:52:48,500 stuff we've talked about already there. 1095 00:52:48,500 --> 00:52:51,230 Or maybe we'll still be on the lifestyle images, depending, but yeah. 1096 00:52:52,100 --> 00:52:55,940 Fantastic guys so do check it out, ecomtent, the merging of 1097 00:52:55,940 --> 00:52:58,340 e-commerce and content, uh, dot ai. 1098 00:52:58,340 --> 00:53:00,140 Do look at that. 1099 00:53:00,590 --> 00:53:02,480 Max, my last question for you, right. 1100 00:53:02,510 --> 00:53:06,140 Uh, the question I've started to ask everybody, um, yeah. 1101 00:53:06,530 --> 00:53:08,970 Imagine you're in a, in a room, hotel room. 1102 00:53:09,670 --> 00:53:09,890 Yep. 1103 00:53:09,890 --> 00:53:12,755 You've just delivered your keynote on how generative AI is 1104 00:53:12,755 --> 00:53:14,076 gonna transform everyone's life. 1105 00:53:14,076 --> 00:53:14,077 Mm-hmm. 1106 00:53:14,315 --> 00:53:16,175 , all the lawyers in the room are jumping up going, woo-hoo. 1107 00:53:16,175 --> 00:53:20,735 We no longer have to do case law . Um, all the marketeers are going, woo-hoo. 1108 00:53:20,735 --> 00:53:23,465 I no longer have to write product descriptions and do social media. 1109 00:53:23,945 --> 00:53:25,265 I'm curious, right. 1110 00:53:25,355 --> 00:53:29,515 Um, whilst you are stood there and you're standing ovation, you get to thank 1111 00:53:29,520 --> 00:53:31,415 people who have had a big impact on you. 1112 00:53:31,655 --> 00:53:32,075 Yes. 1113 00:53:32,195 --> 00:53:35,735 Um, family mentors, authors, software, podcasts, whatever. 1114 00:53:35,945 --> 00:53:37,440 Who do you thank and why? 1115 00:53:38,630 --> 00:53:42,845 Um, God, I mean, like the first person that jumped out to me would 1116 00:53:42,845 --> 00:53:49,595 be my cto because he has done, he's incredibly hardworking and he, um, 1117 00:53:49,625 --> 00:53:52,925 you know, has built all, you know, he's built all of this stuff that 1118 00:53:52,930 --> 00:53:55,985 we've talked about and all of these crazy ideas and, and made it possible. 1119 00:53:55,985 --> 00:54:00,095 So I think like he is, uh, the real brains behind the operation. 1120 00:54:01,410 --> 00:54:03,090 My girlfriend's putting up with all of it. 1121 00:54:03,090 --> 00:54:06,540 My parents, I don't know, I'd say 1122 00:54:06,540 --> 00:54:06,930 I like that. 1123 00:54:07,380 --> 00:54:08,670 Work, God and girlfriend. 1124 00:54:08,670 --> 00:54:09,600 Yeah, yeah, yeah. 1125 00:54:09,600 --> 00:54:10,740 Congratulations to her. 1126 00:54:10,980 --> 00:54:11,800 Um, so yeah. 1127 00:54:12,350 --> 00:54:12,490 Oh. 1128 00:54:12,490 --> 00:54:13,160 Fantastic. 1129 00:54:13,400 --> 00:54:14,250 Yeah, yeah, yeah. 1130 00:54:14,255 --> 00:54:14,820 No, absolutely. 1131 00:54:14,820 --> 00:54:16,140 Well, it's f it's fantastic. 1132 00:54:16,170 --> 00:54:17,280 It's interesting, isn't it? 1133 00:54:17,280 --> 00:54:21,840 You've, um, how, um, you talk about your CTO and how actually the technology 1134 00:54:21,840 --> 00:54:23,430 behind this needs very clever people. 1135 00:54:23,430 --> 00:54:23,520 Yes. 1136 00:54:24,210 --> 00:54:30,755 And, um, I, I'm smiling in some respects cuz my son is doing, Uh, 1137 00:54:30,755 --> 00:54:34,795 a master's degree in theoretical physics and is looking a lot into ai. 1138 00:54:35,565 --> 00:54:38,495 So really fascinating to see where it all goes. 1139 00:54:38,495 --> 00:54:38,795 Listen, 1140 00:54:38,800 --> 00:54:39,275 good for him. 1141 00:54:39,305 --> 00:54:40,075 Good for him. 1142 00:54:40,075 --> 00:54:41,885 I wish I'd done a, I wish I'd done that. 1143 00:54:42,635 --> 00:54:43,355 Glad I didn't. 1144 00:54:43,360 --> 00:54:47,585 Some of the stuff he talks about , I, me, me, people are much easier to understand. 1145 00:54:48,035 --> 00:54:50,735 Uh, so listen, uh, Max, thanks for coming on the show, man. 1146 00:54:50,735 --> 00:54:51,605 Great to connect with. 1147 00:54:52,120 --> 00:54:54,760 Uh, great to have this conversation about ai. 1148 00:54:54,790 --> 00:54:55,810 No doubt we'll have you on. 1149 00:54:55,810 --> 00:54:55,900 Yes. 1150 00:54:55,960 --> 00:54:57,960 Uh, look forward to seeing the journey for ecomtent. 1151 00:54:58,450 --> 00:55:00,280 Um, but, uh, it's been absolutely brilliant. 1152 00:55:00,310 --> 00:55:00,760 Thank you. 1153 00:55:00,940 --> 00:55:01,480 Thank you, Matt. 1154 00:55:01,690 --> 00:55:02,080 Thanks. 1155 00:55:02,680 --> 00:55:03,220 Awesome. 1156 00:55:03,220 --> 00:55:05,680 So another great conversation with Max. 1157 00:55:05,680 --> 00:55:08,770 We will of course link to Max' info in the show notes, which you can 1158 00:55:08,770 --> 00:55:13,150 get along for free, along with the transcript at ecommercepodcast.net. 1159 00:55:13,510 --> 00:55:17,140 Uh, or if you'll subscribe to the email newsletter it will be winging 1160 00:55:17,140 --> 00:55:18,850 its way direct to your inbox. 1161 00:55:19,210 --> 00:55:23,410 So don't forget to check out our free e-commerce training 1162 00:55:23,410 --> 00:55:25,430 at ecommercecycles.com. 1163 00:55:25,450 --> 00:55:26,740 Get into that methodology. 1164 00:55:27,070 --> 00:55:27,970 Uh, let me know what you think. 1165 00:55:27,970 --> 00:55:28,780 Really curious. 1166 00:55:28,780 --> 00:55:30,790 We're just putting it out there and sharing it with the world for 1167 00:55:30,795 --> 00:55:33,970 free, uh, cuz we know it works super well for our business. 1168 00:55:34,030 --> 00:55:34,810 And, uh, further. 1169 00:55:34,810 --> 00:55:36,460 So I'd love to know your thoughts on the whole thing. 1170 00:55:37,000 --> 00:55:39,670 Be sure to follow the e-commerce podcast wherever you get your 1171 00:55:39,670 --> 00:55:43,060 podcast from because we've got some more great conversations lined up. 1172 00:55:43,330 --> 00:55:47,575 And I don't want you to miss any of them, and in case no one has told 1173 00:55:47,575 --> 00:55:50,935 you yet today, you are awesome. 1174 00:55:51,475 --> 00:55:52,045 Ah, yes, you are. 1175 00:55:52,105 --> 00:55:52,975 Created awesome. 1176 00:55:53,005 --> 00:55:54,625 It's just a burden you have to bear. 1177 00:55:54,835 --> 00:55:57,235 It's true of Max, it's true of me. 1178 00:55:57,835 --> 00:55:59,635 Even in a world with AI we're still awesome. 1179 00:56:00,025 --> 00:56:03,055 Uh, the e-Commerce podcast is produced by Aurion Media. 1180 00:56:03,290 --> 00:56:07,160 You can find our entire archive of episodes on your favorite podcast app. 1181 00:56:07,160 --> 00:56:11,680 The team that makes this show possible is Sadaf Beynon, Josh Catchpole, 1182 00:56:11,680 --> 00:56:13,490 Estella Robin and Tim Johnson. 1183 00:56:13,820 --> 00:56:17,420 Our theme song was written by Josh Edmundson and My Good Self. 1184 00:56:17,480 --> 00:56:19,970 And as I mentioned, if you would like to read the transcript or 1185 00:56:19,970 --> 00:56:24,640 show notes, our podcast, uh, website is ecommercepodcast.net. 1186 00:56:25,675 --> 00:56:27,445 Do check that out. 1187 00:56:27,445 --> 00:56:28,555 So that's it from me. 1188 00:56:28,825 --> 00:56:30,085 That's it from Max. 1189 00:56:30,235 --> 00:56:31,765 Thank you so much for joining us. 1190 00:56:31,765 --> 00:56:33,145 Have a fantastic week. 1191 00:56:33,415 --> 00:56:34,225 I'll see you next time. 1192 00:56:34,645 --> 00:56:35,425 Bye for now.