1 00:00:00,000 --> 00:00:02,220 Okay, so get this, you sent over this YouTube video 2 00:00:02,220 --> 00:00:06,780 and the title is Agents as a Service or a Go-S. 3 00:00:06,780 --> 00:00:08,880 And it's basically saying that this is gonna be 4 00:00:08,880 --> 00:00:11,200 even bigger than sauce. 5 00:00:11,200 --> 00:00:15,000 We're talking millionaires, billionaires in the next year, 6 00:00:15,000 --> 00:00:16,640 just from this new business model. 7 00:00:16,640 --> 00:00:18,560 Yeah, it's pretty amazing, huge potential. 8 00:00:18,560 --> 00:00:22,040 So whether you're a techie or not, 9 00:00:22,040 --> 00:00:24,080 you're gonna wanna pay attention to this AOS thing 10 00:00:24,080 --> 00:00:25,760 because it's gonna change everything. 11 00:00:25,760 --> 00:00:27,080 Yeah, for sure. 12 00:00:27,080 --> 00:00:29,400 So what's interesting about this is it's like 13 00:00:29,400 --> 00:00:31,200 the next logical step in something 14 00:00:31,200 --> 00:00:33,480 we've been seeing for decades. 15 00:00:33,480 --> 00:00:36,000 Think about like back in the day when you had to get 16 00:00:36,000 --> 00:00:40,240 software on CDs and install them on your computer 17 00:00:40,240 --> 00:00:43,080 and the internet came along and sauce changed everything. 18 00:00:43,080 --> 00:00:44,320 Huge. 19 00:00:44,320 --> 00:00:47,320 Cloud-based solutions like Salesforce HubSpot, 20 00:00:47,320 --> 00:00:49,240 it's accessible from anywhere. 21 00:00:49,240 --> 00:00:53,120 But this video is saying AOS is going a step further. 22 00:00:53,120 --> 00:00:54,800 So what's the difference? 23 00:00:54,800 --> 00:00:56,920 So think of it this way, sauce gives you the tools 24 00:00:56,920 --> 00:00:59,080 but OAS uses the tools for you. 25 00:00:59,080 --> 00:01:01,640 It's like imagine a marketing agent 26 00:01:01,640 --> 00:01:04,400 that not only handles your CRM 27 00:01:04,400 --> 00:01:07,480 but generates leads, schedules, appointments, 28 00:01:07,480 --> 00:01:11,080 follows up with people all without you lifting a finger. 29 00:01:11,080 --> 00:01:12,160 Wow. 30 00:01:12,160 --> 00:01:14,160 Like a whole team of tireless, 31 00:01:14,160 --> 00:01:17,120 hyper-efficient employees working for you 24-7. 32 00:01:17,120 --> 00:01:18,400 Okay, that's pretty incredible. 33 00:01:18,400 --> 00:01:20,400 But I did notice in the video that they said 34 00:01:20,400 --> 00:01:22,360 enterprise spending on Brash the last year 35 00:01:22,360 --> 00:01:25,560 was only like one, two billion dollars. 36 00:01:25,560 --> 00:01:28,360 Which doesn't sound like a lot when we're talking about, 37 00:01:28,360 --> 00:01:30,560 you know, this huge potential. 38 00:01:30,560 --> 00:01:31,680 Yeah, you're right. 39 00:01:31,680 --> 00:01:34,760 But that one, two billion is 12 times higher 40 00:01:34,760 --> 00:01:36,000 than it was the year before. 41 00:01:36,000 --> 00:01:36,840 Really? 42 00:01:36,840 --> 00:01:38,520 Yeah, so it's growing like crazy. 43 00:01:38,520 --> 00:01:39,360 Okay. 44 00:01:39,360 --> 00:01:41,600 So it might be small now, but it's gonna be massive soon. 45 00:01:41,600 --> 00:01:43,680 Okay, so we're definitely in the early stages. 46 00:01:43,680 --> 00:01:44,520 That's sure. 47 00:01:44,520 --> 00:01:46,400 But it sounds like there's a huge opportunity here. 48 00:01:46,400 --> 00:01:47,320 Absolutely. 49 00:01:47,320 --> 00:01:50,320 So are all these AI agents the same? 50 00:01:50,320 --> 00:01:51,680 So there's actually two main types. 51 00:01:51,680 --> 00:01:52,520 Okay. 52 00:01:52,520 --> 00:01:54,160 There's vertical agents and horizontal agents. 53 00:01:54,160 --> 00:01:55,000 Okay. 54 00:01:55,000 --> 00:01:56,200 Vertical agents think of them as specialists. 55 00:01:56,200 --> 00:01:57,040 Okay. 56 00:01:57,040 --> 00:01:59,880 And for a very specific role in the specific industry. 57 00:01:59,880 --> 00:02:02,440 And so the video gives an example of Carmen, 58 00:02:02,440 --> 00:02:03,760 which is an AI agent, 59 00:02:03,760 --> 00:02:06,440 specifically for construction project managers. 60 00:02:06,440 --> 00:02:07,280 Okay. 61 00:02:07,280 --> 00:02:09,680 And it automates all those administrative tasks 62 00:02:09,680 --> 00:02:11,560 that are unique to that field. 63 00:02:11,560 --> 00:02:14,840 So super targeted solution for a very specific problem. 64 00:02:14,840 --> 00:02:15,680 Exactly. 65 00:02:15,680 --> 00:02:17,560 Okay, what about horizontal agents? 66 00:02:17,560 --> 00:02:19,960 Horizontal agents are more like generalists. 67 00:02:19,960 --> 00:02:20,800 Okay. 68 00:02:20,800 --> 00:02:23,560 They can adapt to any niche, any function. 69 00:02:23,560 --> 00:02:24,560 Okay. 70 00:02:24,560 --> 00:02:26,360 Almost like a platform that you can use 71 00:02:26,360 --> 00:02:28,360 to build all sorts of vertical agents on. 72 00:02:28,360 --> 00:02:29,200 Gotcha. 73 00:02:29,200 --> 00:02:32,000 And one example the video gave was agency swarm. 74 00:02:32,000 --> 00:02:32,840 Okay. 75 00:02:32,840 --> 00:02:34,760 Which is that type of framework. 76 00:02:34,760 --> 00:02:36,520 Okay, I'm starting to see the difference now. 77 00:02:36,520 --> 00:02:39,840 So is it a matter of like, is one better than the other? 78 00:02:39,840 --> 00:02:40,680 Or? 79 00:02:40,680 --> 00:02:41,520 Not necessarily. 80 00:02:41,520 --> 00:02:42,360 Okay. 81 00:02:42,360 --> 00:02:43,200 It really depends on what your needs are 82 00:02:43,200 --> 00:02:44,040 and what your goals are. 83 00:02:44,040 --> 00:02:44,880 Sure. 84 00:02:44,880 --> 00:02:45,800 You can think of it as the classic trade-off 85 00:02:45,800 --> 00:02:47,200 between performance and time. 86 00:02:47,200 --> 00:02:48,040 Okay. 87 00:02:48,040 --> 00:02:51,240 Vertical agents, they're already pre-trained for their task. 88 00:02:51,240 --> 00:02:53,400 So they can deliver results quickly, 89 00:02:53,400 --> 00:02:54,960 but they're not as customizable. 90 00:02:54,960 --> 00:02:57,000 Okay, so it's like those fancy espresso machines 91 00:02:57,000 --> 00:02:57,840 that you get. 92 00:02:57,840 --> 00:02:59,080 Like they make a perfect espresso, 93 00:02:59,080 --> 00:03:01,160 but if you want a smoothie, you're out of luck. 94 00:03:01,160 --> 00:03:02,000 Exactly. 95 00:03:02,000 --> 00:03:02,840 Yeah, okay. 96 00:03:02,840 --> 00:03:04,240 Horizontal agents, on the other hand, 97 00:03:04,240 --> 00:03:06,440 take a bit more setup and training. 98 00:03:06,440 --> 00:03:09,320 But the payoff is you have way more flexibility 99 00:03:09,320 --> 00:03:11,640 in the long run because you can tailor them 100 00:03:11,640 --> 00:03:13,040 to do exactly what you need. 101 00:03:13,040 --> 00:03:13,880 Okay, got it. 102 00:03:13,880 --> 00:03:16,280 So it's like a specialist versus a generalist. 103 00:03:16,280 --> 00:03:17,120 Exactly. 104 00:03:17,120 --> 00:03:18,920 And you know, they each have their own pros and cons. 105 00:03:18,920 --> 00:03:19,760 Yeah. 106 00:03:19,760 --> 00:03:21,080 Cool. 107 00:03:21,080 --> 00:03:24,000 The video also highlighted some really interesting 108 00:03:24,000 --> 00:03:27,760 real-world IRA solutions that are already out there. 109 00:03:27,760 --> 00:03:28,920 Yeah, for sure. 110 00:03:28,920 --> 00:03:30,840 What were some of the ones that stood out to you? 111 00:03:30,840 --> 00:03:33,360 So one that really stood out to me was 11X. 112 00:03:33,360 --> 00:03:34,320 Okay. 113 00:03:34,320 --> 00:03:36,840 They're developing AI-powered sales agents 114 00:03:36,840 --> 00:03:39,160 that can automate the entire sales process. 115 00:03:39,160 --> 00:03:40,120 Wow. 116 00:03:40,120 --> 00:03:42,800 And they've raised $74 million in funding. 117 00:03:42,800 --> 00:03:44,480 Wow, that's a lot of money. 118 00:03:44,480 --> 00:03:45,560 It is a lot of money. 119 00:03:45,560 --> 00:03:47,840 But the video also said that you don't need millions 120 00:03:47,840 --> 00:03:49,720 of dollars to get started with IS. 121 00:03:49,720 --> 00:03:51,760 Right, the creator even kind of implied 122 00:03:51,760 --> 00:03:53,960 that maybe 11X has too much funding. 123 00:03:53,960 --> 00:03:56,720 He really emphasized that the real key 124 00:03:56,720 --> 00:03:58,840 is to focus on delivering value 125 00:03:58,840 --> 00:04:02,360 and solving real problems, even if you're starting small. 126 00:04:02,360 --> 00:04:03,360 I love that. 127 00:04:03,360 --> 00:04:06,480 It's very empowering to hear that you don't need to be, 128 00:04:06,480 --> 00:04:09,360 you know, some Silicon Valley hotshot to get in on this. 129 00:04:09,360 --> 00:04:10,200 Yeah, exactly. 130 00:04:10,200 --> 00:04:12,000 What were some of the other examples that you liked? 131 00:04:12,000 --> 00:04:13,480 So we already talked about Carmen, 132 00:04:13,480 --> 00:04:16,800 the AI agent for construction project managers. 133 00:04:16,800 --> 00:04:17,640 Yeah. 134 00:04:17,640 --> 00:04:20,080 I think it's a perfect example of how powerful 135 00:04:20,080 --> 00:04:23,480 it can be to target a very specific niche. 136 00:04:23,480 --> 00:04:25,640 There's also Normy, which is designed 137 00:04:25,640 --> 00:04:27,560 for regulatory compliance teams. 138 00:04:27,560 --> 00:04:28,400 Okay. 139 00:04:28,400 --> 00:04:32,600 Shows that AS can go way beyond just sales and marketing. 140 00:04:32,600 --> 00:04:34,880 There are opportunities in all kinds of industries. 141 00:04:34,880 --> 00:04:36,680 Yeah, it seems like the possibilities 142 00:04:36,680 --> 00:04:38,320 are pretty much endless. 143 00:04:38,320 --> 00:04:39,160 Yeah. 144 00:04:39,160 --> 00:04:41,120 The video also mentioned Devin, 145 00:04:41,120 --> 00:04:44,400 which is a development agent that costs $500 a month. 146 00:04:44,400 --> 00:04:45,240 Yeah. 147 00:04:45,240 --> 00:04:46,080 I'd be curious to hear more about that one. 148 00:04:46,080 --> 00:04:48,440 Yeah, Devin is intriguing, 149 00:04:48,440 --> 00:04:51,200 but I honestly think that deserves a deep dive of its own. 150 00:04:51,200 --> 00:04:52,040 Okay. 151 00:04:52,040 --> 00:04:53,360 Maybe we can do that in a future episode. 152 00:04:53,360 --> 00:04:54,560 Okay, I'm putting that on the list. 153 00:04:54,560 --> 00:04:55,880 All right, cool. 154 00:04:55,880 --> 00:04:57,840 So let's say someone is listening to this 155 00:04:57,840 --> 00:05:00,040 and they're like, okay, I'm sold. 156 00:05:00,040 --> 00:05:02,280 How do I build my own vertical agent? 157 00:05:02,280 --> 00:05:03,120 Okay. 158 00:05:03,120 --> 00:05:03,960 What are the first steps? 159 00:05:03,960 --> 00:05:06,840 So the video laid out three key ingredients. 160 00:05:06,840 --> 00:05:07,680 Okay. 161 00:05:07,680 --> 00:05:10,400 You need data, you need industry expertise, 162 00:05:10,400 --> 00:05:11,760 and you need resources. 163 00:05:11,760 --> 00:05:12,600 Okay. 164 00:05:12,600 --> 00:05:13,440 So let's start with data. 165 00:05:13,440 --> 00:05:14,280 Okay. 166 00:05:14,280 --> 00:05:17,840 The quality of your AI agent is completely dependent 167 00:05:17,840 --> 00:05:20,040 on the data that you train it on. 168 00:05:20,040 --> 00:05:23,400 So step one is to gather valuable internal data 169 00:05:23,400 --> 00:05:25,800 that's relevant to the task you wanna automate. 170 00:05:25,800 --> 00:05:27,480 That makes sense, but what if you don't have access 171 00:05:27,480 --> 00:05:29,040 to that data right away? 172 00:05:29,040 --> 00:05:29,880 That's a great question, 173 00:05:29,880 --> 00:05:31,400 and the video actually addressed that. 174 00:05:31,400 --> 00:05:32,240 Okay. 175 00:05:32,240 --> 00:05:33,960 One suggestion was to partner with someone 176 00:05:33,960 --> 00:05:35,640 who does have access to the data. 177 00:05:35,640 --> 00:05:36,480 Okay. 178 00:05:36,480 --> 00:05:39,560 Or even offer to build the agent for a client 179 00:05:39,560 --> 00:05:41,360 in exchange for access to their data. 180 00:05:41,360 --> 00:05:42,200 Oh, that's smart. 181 00:05:42,200 --> 00:05:44,600 Yeah, so you're getting paid to develop your solution 182 00:05:44,600 --> 00:05:46,840 and you're gathering the data you need at the same time. 183 00:05:46,840 --> 00:05:47,680 Brilliant. 184 00:05:47,680 --> 00:05:48,500 Okay, so data is covered. 185 00:05:48,500 --> 00:05:50,020 What about industry expertise? 186 00:05:50,020 --> 00:05:52,160 You need to have a really deep understanding 187 00:05:52,160 --> 00:05:56,160 of your target customer, their workflow, their pain points. 188 00:05:56,160 --> 00:05:59,160 You need to know their processes inside and out 189 00:05:59,160 --> 00:06:00,520 so you can design an agent 190 00:06:00,520 --> 00:06:02,240 that truly solves their problems. 191 00:06:02,240 --> 00:06:03,080 Gotcha. 192 00:06:03,080 --> 00:06:05,760 So if someone isn't an expert in the field, 193 00:06:05,760 --> 00:06:07,680 they need to partner with someone who is. 194 00:06:07,680 --> 00:06:08,520 Absolutely. 195 00:06:08,520 --> 00:06:09,360 Okay. 196 00:06:09,360 --> 00:06:12,840 And remember, you don't need like a huge investment 197 00:06:12,840 --> 00:06:13,800 to get started. 198 00:06:13,800 --> 00:06:14,640 Okay. 199 00:06:14,640 --> 00:06:17,320 You can leverage your existing skills network 200 00:06:17,320 --> 00:06:19,640 and resources to build your first agent. 201 00:06:19,640 --> 00:06:20,480 Yeah. 202 00:06:20,480 --> 00:06:22,600 In fact, the video emphasized that a lot of times 203 00:06:22,600 --> 00:06:24,640 you can secure funding from your first client. 204 00:06:24,640 --> 00:06:25,480 Okay. 205 00:06:25,480 --> 00:06:28,240 Essentially getting them to pay for the development 206 00:06:28,240 --> 00:06:29,440 of the solution they need. 207 00:06:29,440 --> 00:06:31,280 Okay, that's a game changer. 208 00:06:31,280 --> 00:06:32,640 All right, so we've got our data. 209 00:06:32,640 --> 00:06:34,840 We've got our industry expertise lined up. 210 00:06:34,840 --> 00:06:35,680 Right. 211 00:06:35,680 --> 00:06:38,680 What are our options for actually building the agent? 212 00:06:38,680 --> 00:06:40,640 So the video presented three main approaches. 213 00:06:40,640 --> 00:06:43,480 You can use a framework, you can leverage a platform 214 00:06:43,480 --> 00:06:45,280 or you can develop a custom solution. 215 00:06:45,280 --> 00:06:46,240 Okay. 216 00:06:46,240 --> 00:06:47,800 Each approach has its pros and cons. 217 00:06:47,800 --> 00:06:49,680 It really just depends on your technical skills 218 00:06:49,680 --> 00:06:51,600 and how complex of an agent you're building. 219 00:06:51,600 --> 00:06:52,920 Okay, let's dive into those. 220 00:06:52,920 --> 00:06:53,760 Okay. 221 00:06:53,760 --> 00:06:55,860 So using a framework like agency swarm, 222 00:06:55,860 --> 00:06:57,300 which we mentioned earlier, 223 00:06:57,300 --> 00:06:59,160 can save you a lot of time and effort 224 00:06:59,160 --> 00:07:00,920 because it handles some of the lower level 225 00:07:00,920 --> 00:07:02,560 technical details for you. 226 00:07:02,560 --> 00:07:03,600 Gotcha. 227 00:07:03,600 --> 00:07:05,420 But you'll still need some development experience 228 00:07:05,420 --> 00:07:06,840 to make it work. 229 00:07:06,840 --> 00:07:08,200 So for people that are comfortable 230 00:07:08,200 --> 00:07:10,440 with a little bit of coding, 231 00:07:10,440 --> 00:07:12,120 a framework would be a good way to go. 232 00:07:12,120 --> 00:07:13,160 Yeah, exactly. 233 00:07:13,160 --> 00:07:14,000 Okay. 234 00:07:14,000 --> 00:07:15,720 What about using a platform? 235 00:07:15,720 --> 00:07:19,640 Platforms like Google Clouds, Vertex, AI, 236 00:07:19,640 --> 00:07:24,120 Amazon's Bedrock are incredibly powerful. 237 00:07:24,120 --> 00:07:26,000 They handle all the infrastructure for you 238 00:07:26,000 --> 00:07:28,360 so you can focus on building the agent 239 00:07:28,360 --> 00:07:30,360 without having to be like a coding wizard. 240 00:07:30,360 --> 00:07:31,920 Right. 241 00:07:31,920 --> 00:07:33,880 But they can get pretty expensive as you scale up. 242 00:07:33,880 --> 00:07:36,360 Okay, so maybe not the best if you're just starting out, 243 00:07:36,360 --> 00:07:38,120 but good once you get going a little bit. 244 00:07:38,120 --> 00:07:38,960 Yeah, exactly. 245 00:07:38,960 --> 00:07:40,000 What about the third option, 246 00:07:40,000 --> 00:07:42,400 building a custom solution from scratch? 247 00:07:42,400 --> 00:07:44,080 That gives you complete control. 248 00:07:44,080 --> 00:07:44,920 Mm-hmm. 249 00:07:44,920 --> 00:07:45,880 Complete flexibility, 250 00:07:45,880 --> 00:07:48,120 but it's definitely the most challenging path. 251 00:07:48,120 --> 00:07:48,960 Yeah. 252 00:07:48,960 --> 00:07:50,360 You'll need a really strong development team, 253 00:07:50,360 --> 00:07:52,440 a lot of technical expertise to pull it off. 254 00:07:52,440 --> 00:07:53,680 So ultimate freedom, 255 00:07:53,680 --> 00:07:55,520 but also requires the most effort. 256 00:07:55,520 --> 00:07:56,360 Exactly. 257 00:07:56,360 --> 00:07:59,760 So what did the video recommend as like the best approach? 258 00:07:59,760 --> 00:08:02,840 So the creator recommended starting with a framework 259 00:08:02,840 --> 00:08:04,000 or a platform, 260 00:08:04,000 --> 00:08:06,880 to validate your idea and get to market quickly. 261 00:08:06,880 --> 00:08:10,440 Then as you grow and your needs become more complex, 262 00:08:10,440 --> 00:08:12,960 you can consider transitioning to a custom solution. 263 00:08:12,960 --> 00:08:14,400 Okay, so it's like a, you know, 264 00:08:14,400 --> 00:08:15,920 you start small and you scale up. 265 00:08:15,920 --> 00:08:16,760 Exactly. 266 00:08:16,760 --> 00:08:17,580 I like it. 267 00:08:17,580 --> 00:08:18,420 All right. 268 00:08:18,420 --> 00:08:19,280 So we've built our agent, 269 00:08:19,280 --> 00:08:21,480 we're ready to launch it into the world. 270 00:08:21,480 --> 00:08:24,560 How do we actually price this AS solution? 271 00:08:24,560 --> 00:08:26,080 So that's the million dollar question, right? 272 00:08:26,080 --> 00:08:26,920 Yeah. 273 00:08:26,920 --> 00:08:30,520 Luckily, the video gave us four key pricing models 274 00:08:30,520 --> 00:08:31,360 to consider. 275 00:08:31,360 --> 00:08:32,200 Okay. 276 00:08:32,200 --> 00:08:35,240 Licensing usage-based, outcome-based, and hybrid. 277 00:08:35,240 --> 00:08:36,120 Okay, let's break those down. 278 00:08:36,120 --> 00:08:37,880 Okay, so licensing is pretty straightforward. 279 00:08:37,880 --> 00:08:38,720 Yeah. 280 00:08:38,720 --> 00:08:40,720 Clients pay a one-time setup fee 281 00:08:40,720 --> 00:08:43,800 or a recurring monthly subscription to use your agent. 282 00:08:43,800 --> 00:08:44,640 Okay. 283 00:08:44,640 --> 00:08:45,520 It's easy to explain, 284 00:08:45,520 --> 00:08:47,800 but the downside is it doesn't always reflect 285 00:08:47,800 --> 00:08:50,040 the actual value that the agent is delivering. 286 00:08:50,040 --> 00:08:50,880 Okay. 287 00:08:50,880 --> 00:08:52,560 So maybe good for the early stages, 288 00:08:52,560 --> 00:08:54,440 but not ideal for the long-term. 289 00:08:54,440 --> 00:08:55,280 Right. 290 00:08:55,280 --> 00:08:57,000 Okay, what about usage-based pricing? 291 00:08:57,000 --> 00:09:00,240 Usage-based pricing is really common in the SAWS world. 292 00:09:00,240 --> 00:09:03,180 So you charge customers based on how much they use the agent. 293 00:09:03,180 --> 00:09:05,280 So maybe by the number of tasks completed 294 00:09:05,280 --> 00:09:07,320 or the amount of data processed, 295 00:09:07,320 --> 00:09:09,920 it's very scalable, especially for larger clients. 296 00:09:09,920 --> 00:09:10,800 Okay. 297 00:09:10,800 --> 00:09:14,040 But it requires consistent agent use to be profitable. 298 00:09:14,040 --> 00:09:16,920 So not a good fit for people that only need it occasionally. 299 00:09:16,920 --> 00:09:17,760 Right. 300 00:09:17,760 --> 00:09:20,040 Okay, now this outcome-based pricing, 301 00:09:20,040 --> 00:09:21,200 that sounds interesting. 302 00:09:21,200 --> 00:09:22,800 This is where AOS gets really exciting. 303 00:09:22,800 --> 00:09:23,640 Okay. 304 00:09:23,640 --> 00:09:27,040 You actually charge clients based on the results 305 00:09:27,040 --> 00:09:28,280 that the agent achieves. 306 00:09:28,280 --> 00:09:29,120 Okay. 307 00:09:29,120 --> 00:09:32,120 So you could charge per appointment, 308 00:09:32,120 --> 00:09:35,400 booked, per lay generated, per website, built. 309 00:09:35,400 --> 00:09:36,320 Wow. 310 00:09:36,320 --> 00:09:37,320 It's a win-win, 311 00:09:37,320 --> 00:09:40,040 because your clients are only paying for tangible results. 312 00:09:40,040 --> 00:09:41,880 And you're incentivized to make sure 313 00:09:41,880 --> 00:09:43,280 that your agent is actually working. 314 00:09:43,280 --> 00:09:44,120 Exactly. 315 00:09:44,120 --> 00:09:44,960 I like that a lot. 316 00:09:44,960 --> 00:09:46,360 Okay, what about the hybrid model? 317 00:09:46,360 --> 00:09:48,360 A hybrid model combines elements 318 00:09:48,360 --> 00:09:50,200 of multiple pricing strategies. 319 00:09:50,200 --> 00:09:51,040 Right. 320 00:09:51,040 --> 00:09:52,960 So for example, you might charge a base fee 321 00:09:52,960 --> 00:09:55,320 plus an additional fee per outcome achieved. 322 00:09:55,320 --> 00:09:56,200 Okay. 323 00:09:56,200 --> 00:09:57,560 So it offers flexibility, 324 00:09:57,560 --> 00:10:00,080 but it can be a little more complex to explain to clients. 325 00:10:00,080 --> 00:10:01,600 Sure, okay, so lots of different options, 326 00:10:01,600 --> 00:10:04,600 depending on the specific agent and the target market. 327 00:10:04,600 --> 00:10:05,440 Exactly. 328 00:10:05,440 --> 00:10:07,120 All right, so now let's get down to the nitty gritty. 329 00:10:07,120 --> 00:10:09,520 If someone wants to ride this AS wave 330 00:10:09,520 --> 00:10:12,360 and find success in 2025, 331 00:10:12,360 --> 00:10:14,200 what's the roadmap look like? 332 00:10:14,200 --> 00:10:18,480 So the video laid out a pretty clear step-by-step plan 333 00:10:18,480 --> 00:10:20,120 that anyone can follow. 334 00:10:20,120 --> 00:10:22,520 So the first step is to find your niche. 335 00:10:22,520 --> 00:10:23,920 All right, how do we do that? 336 00:10:23,920 --> 00:10:27,040 It's all about leveraging what you already know. 337 00:10:27,040 --> 00:10:28,800 What are your existing skills, 338 00:10:28,800 --> 00:10:30,720 experiences your network, 339 00:10:30,720 --> 00:10:33,040 what industries are you familiar with, 340 00:10:33,040 --> 00:10:35,040 what problems have you encountered 341 00:10:35,040 --> 00:10:36,840 that an AI agent could solve? 342 00:10:36,840 --> 00:10:37,680 Okay. 343 00:10:37,680 --> 00:10:39,560 Where can you offer a unique solution 344 00:10:39,560 --> 00:10:41,320 that no one else is providing? 345 00:10:41,320 --> 00:10:44,080 So instead of trying to be everything to everyone, 346 00:10:44,080 --> 00:10:46,240 we should focus on where we can really excel 347 00:10:46,240 --> 00:10:47,520 and offer value. 348 00:10:47,520 --> 00:10:48,360 Exactly. 349 00:10:48,360 --> 00:10:49,200 Okay, what's next? 350 00:10:49,200 --> 00:10:50,800 So once you've identified your niche, 351 00:10:50,800 --> 00:10:54,520 the next step is to pinpoint the specific problem 352 00:10:54,520 --> 00:10:56,000 that you wanna solve for your agent. 353 00:10:56,000 --> 00:10:58,560 What makes a problem a good fit for an AS solution? 354 00:10:58,560 --> 00:11:02,000 So the video highlighted a few key characteristics. 355 00:11:02,000 --> 00:11:02,840 Okay. 356 00:11:02,840 --> 00:11:03,800 The problem should be recurring. 357 00:11:03,800 --> 00:11:04,640 Okay. 358 00:11:04,640 --> 00:11:06,000 Meaning it happens frequently, 359 00:11:06,000 --> 00:11:06,840 it should be dynamic. 360 00:11:06,840 --> 00:11:07,680 Okay. 361 00:11:07,680 --> 00:11:11,480 Meaning it involves some level of variability or complexity. 362 00:11:11,480 --> 00:11:12,320 Gotcha. 363 00:11:12,320 --> 00:11:16,160 And it should be something that traditional automation tools 364 00:11:16,160 --> 00:11:17,880 can't really handle. 365 00:11:17,880 --> 00:11:20,280 It needs to require some level of intelligence, 366 00:11:20,280 --> 00:11:21,200 some decision making. 367 00:11:21,200 --> 00:11:24,040 Okay, so not just like simple repetitive tasks, 368 00:11:24,040 --> 00:11:25,920 but something that requires a little bit more 369 00:11:25,920 --> 00:11:26,760 Exactly. 370 00:11:26,760 --> 00:11:27,600 Adaptability. 371 00:11:27,600 --> 00:11:28,440 Yeah. 372 00:11:28,440 --> 00:11:29,280 Gotcha. 373 00:11:29,280 --> 00:11:31,040 Okay, what's the next step on our roadmap? 374 00:11:31,040 --> 00:11:32,080 This is where it gets interesting. 375 00:11:32,080 --> 00:11:33,400 Sell first, build second. 376 00:11:33,400 --> 00:11:34,240 Wait, hold on. 377 00:11:34,240 --> 00:11:35,360 Do we usually build the solution 378 00:11:35,360 --> 00:11:36,440 and then try to sell it? 379 00:11:36,440 --> 00:11:37,920 Yeah, that's the traditional approach. 380 00:11:37,920 --> 00:11:40,400 But the video argued that selling first 381 00:11:40,400 --> 00:11:42,240 is a much smarter strategy. 382 00:11:42,240 --> 00:11:43,840 Because if you can find a client 383 00:11:43,840 --> 00:11:45,680 who is willing to pay for your solution 384 00:11:45,680 --> 00:11:47,440 before you build it, 385 00:11:47,440 --> 00:11:49,440 you're validating your idea, 386 00:11:49,440 --> 00:11:51,080 you're reducing your risk, 387 00:11:51,080 --> 00:11:53,120 you're ensuring that you're creating something 388 00:11:53,120 --> 00:11:54,720 that meets a real market need. 389 00:11:54,720 --> 00:11:56,640 You're getting paid to develop the solution. 390 00:11:56,640 --> 00:11:57,600 Exactly. 391 00:11:57,600 --> 00:11:58,440 I love that. 392 00:11:58,440 --> 00:11:59,680 Okay, so we've secured our first client, 393 00:11:59,680 --> 00:12:00,960 what happens next? 394 00:12:00,960 --> 00:12:03,920 Time to build a minimum viable product. 395 00:12:03,920 --> 00:12:04,800 Or an MVP. 396 00:12:04,800 --> 00:12:05,640 An MVP. 397 00:12:05,640 --> 00:12:07,640 Can you remind us what an MVP is? 398 00:12:07,640 --> 00:12:11,280 An MVP is a stripped down version of your product. 399 00:12:11,280 --> 00:12:12,120 Okay. 400 00:12:12,120 --> 00:12:15,280 That includes just the essential features 401 00:12:15,280 --> 00:12:18,440 that are needed to solve that core problem. 402 00:12:18,440 --> 00:12:21,240 The goal is to get something functional 403 00:12:21,240 --> 00:12:24,560 in the hands of your client as quickly as possible 404 00:12:24,560 --> 00:12:26,280 so you can start gathering feedback. 405 00:12:26,280 --> 00:12:27,120 Okay. 406 00:12:27,120 --> 00:12:29,160 And iterating based on their needs. 407 00:12:29,160 --> 00:12:31,360 So it's not about building the perfect product 408 00:12:31,360 --> 00:12:32,360 right out of the gate. 409 00:12:32,360 --> 00:12:33,200 Right. 410 00:12:33,200 --> 00:12:34,520 It's about getting something out there 411 00:12:34,520 --> 00:12:35,680 and getting feedback. 412 00:12:35,680 --> 00:12:36,520 Exactly. 413 00:12:36,520 --> 00:12:37,340 I like it. 414 00:12:37,340 --> 00:12:39,240 What's the next step after the MVP? 415 00:12:39,240 --> 00:12:41,680 So the video called a productizing your agent. 416 00:12:41,680 --> 00:12:42,840 Okay, what does that mean? 417 00:12:42,840 --> 00:12:44,720 Basically taking everything you learned 418 00:12:44,720 --> 00:12:46,360 from building that MVP. 419 00:12:46,360 --> 00:12:47,200 Yeah. 420 00:12:47,200 --> 00:12:50,280 And turning it into a repeatable scalable solution. 421 00:12:50,280 --> 00:12:51,120 Yeah. 422 00:12:51,120 --> 00:12:52,440 Identifying those core features 423 00:12:52,440 --> 00:12:54,680 that are gonna be consistent across different clients. 424 00:12:54,680 --> 00:12:55,520 Yeah. 425 00:12:55,520 --> 00:12:58,000 And making it really easy to customize the agent 426 00:12:58,000 --> 00:12:59,200 for different needs. 427 00:12:59,200 --> 00:13:02,000 Maybe through templates or configuration files. 428 00:13:02,000 --> 00:13:04,040 So making it adaptable so you can onboard 429 00:13:04,040 --> 00:13:06,560 new clients without having to start from scratch every time. 430 00:13:06,560 --> 00:13:07,400 Exactly. 431 00:13:07,400 --> 00:13:08,240 Okay, cool. 432 00:13:08,240 --> 00:13:09,080 What's next? 433 00:13:09,080 --> 00:13:10,440 Next step is to set up a system 434 00:13:10,440 --> 00:13:12,360 for evaluating your agent's performance. 435 00:13:12,360 --> 00:13:13,200 Okay. 436 00:13:13,200 --> 00:13:15,040 The video called these evils. 437 00:13:15,040 --> 00:13:15,880 Yeah, okay. 438 00:13:15,880 --> 00:13:19,480 Emphasize that this is crucial for continuous improvement. 439 00:13:19,480 --> 00:13:22,880 You need to be able to track how your agent is performing. 440 00:13:22,880 --> 00:13:25,560 Identify areas where it can be optimized. 441 00:13:25,560 --> 00:13:26,400 Yeah. 442 00:13:26,400 --> 00:13:27,800 Make adjustments as needed. 443 00:13:27,800 --> 00:13:30,000 So it's like this constant feedback loop. 444 00:13:30,000 --> 00:13:30,840 Exactly. 445 00:13:30,840 --> 00:13:32,360 Where your agent is always getting smarter. 446 00:13:32,360 --> 00:13:33,200 Yeah. 447 00:13:33,200 --> 00:13:36,480 And the final step on our roadmap. 448 00:13:36,480 --> 00:13:38,480 The final step is to scale your solution. 449 00:13:38,480 --> 00:13:39,320 Right. 450 00:13:39,320 --> 00:13:40,520 Once you have that productized agent 451 00:13:40,520 --> 00:13:44,240 that's delivering results, ramp up your marketing efforts. 452 00:13:44,240 --> 00:13:46,120 Maybe hire some team members. 453 00:13:46,120 --> 00:13:49,320 Get your agent in the hands of as many clients as possible. 454 00:13:49,320 --> 00:13:51,560 Okay, that's where things get really exciting. 455 00:13:51,560 --> 00:13:52,400 Yeah. 456 00:13:52,400 --> 00:13:53,880 But before we get too caught up in all the hype, 457 00:13:53,880 --> 00:13:56,160 the video shared a real world case study 458 00:13:56,160 --> 00:13:59,280 to show what a success can actually look like in practice. 459 00:13:59,280 --> 00:14:00,120 Yeah. 460 00:14:00,120 --> 00:14:01,320 We got to hear from Chase, 461 00:14:01,320 --> 00:14:04,720 an entrepreneur who's built a successful AS voice solution. 462 00:14:04,720 --> 00:14:05,560 Okay. 463 00:14:05,560 --> 00:14:07,120 And is generating impressive revenue. 464 00:14:07,120 --> 00:14:07,960 Right. 465 00:14:07,960 --> 00:14:08,800 Tell me more about Chase. 466 00:14:08,800 --> 00:14:10,560 So Chase's story is a perfect example 467 00:14:10,560 --> 00:14:13,160 of how to leverage niche expertise 468 00:14:13,160 --> 00:14:15,960 to build a valuable AS solution. 469 00:14:15,960 --> 00:14:16,800 Okay. 470 00:14:16,800 --> 00:14:19,240 He combined his experience in the solar industry 471 00:14:19,240 --> 00:14:22,120 with AI to create a voice agent. 472 00:14:22,120 --> 00:14:22,960 Okay. 473 00:14:22,960 --> 00:14:25,120 That automates the sales process for solar companies. 474 00:14:25,120 --> 00:14:25,960 Oh, that's smart. 475 00:14:25,960 --> 00:14:27,640 So he found a problem in an industry 476 00:14:27,640 --> 00:14:28,840 that he knew really well. 477 00:14:28,840 --> 00:14:29,680 Exactly. 478 00:14:29,680 --> 00:14:32,880 Build a solution tailored to that specific problem. 479 00:14:32,880 --> 00:14:33,800 Yeah. 480 00:14:33,800 --> 00:14:34,640 Very cool. 481 00:14:34,640 --> 00:14:37,880 What were some of the key takeaways from his experience? 482 00:14:37,880 --> 00:14:39,080 One thing that stood out was 483 00:14:39,080 --> 00:14:41,000 he didn't try to reinvent the wheel. 484 00:14:41,000 --> 00:14:41,840 Okay. 485 00:14:41,840 --> 00:14:43,880 He integrated his AS solution 486 00:14:43,880 --> 00:14:45,600 with systems that were already out there. 487 00:14:45,600 --> 00:14:46,440 Okay. 488 00:14:46,440 --> 00:14:49,120 Leveraged his existing CRM automation workflows. 489 00:14:49,120 --> 00:14:50,000 Gotcha. 490 00:14:50,000 --> 00:14:51,720 He also focused on creating templates 491 00:14:51,720 --> 00:14:54,000 to make it easy to customize his solution 492 00:14:54,000 --> 00:14:55,280 for different clients. 493 00:14:55,280 --> 00:14:57,440 Okay, so that echoes that productization thing 494 00:14:57,440 --> 00:14:58,280 that we were talking about. 495 00:14:58,280 --> 00:14:59,120 Exactly. 496 00:14:59,120 --> 00:15:00,080 How else did he do? 497 00:15:00,080 --> 00:15:02,240 He understood the importance of value in results. 498 00:15:02,240 --> 00:15:03,080 Okay. 499 00:15:03,080 --> 00:15:05,280 So instead of just charging a flat fee, 500 00:15:05,280 --> 00:15:06,640 he offered two models. 501 00:15:06,640 --> 00:15:07,480 Okay. 502 00:15:07,480 --> 00:15:08,600 A build and release model, 503 00:15:08,600 --> 00:15:10,640 and then also a growth partner model, 504 00:15:10,640 --> 00:15:13,040 where he would actively manage the AS solution 505 00:15:13,040 --> 00:15:13,880 for his clients. 506 00:15:13,880 --> 00:15:14,720 Okay. 507 00:15:14,720 --> 00:15:16,880 And he would share in the revenue that it generated. 508 00:15:16,880 --> 00:15:17,720 Wow. 509 00:15:17,720 --> 00:15:20,320 So he aligned his incentives with his clients' incentives. 510 00:15:20,320 --> 00:15:21,160 Exactly. 511 00:15:21,160 --> 00:15:22,680 That's really smart. 512 00:15:22,680 --> 00:15:24,400 The video actually included a snippet 513 00:15:24,400 --> 00:15:27,120 of Chase's AI agent in action. 514 00:15:27,120 --> 00:15:27,960 It did. 515 00:15:27,960 --> 00:15:29,240 We'd love to hear that if we can. 516 00:15:29,240 --> 00:15:30,080 Yeah, absolutely. 517 00:15:30,080 --> 00:15:30,920 Okay. 518 00:15:30,920 --> 00:15:33,480 So first we hear Grace Chase's AI sales assistant 519 00:15:33,480 --> 00:15:34,680 introducing herself. 520 00:15:34,680 --> 00:15:35,800 Okay. 521 00:15:35,800 --> 00:15:37,680 Then we hear Grace speaking Spanish 522 00:15:37,680 --> 00:15:40,000 demonstrating her multilingual capabilities. 523 00:15:40,000 --> 00:15:41,080 Wow. 524 00:15:41,080 --> 00:15:43,080 Next up, she delivers an impressive rap 525 00:15:43,080 --> 00:15:44,920 about the power of AI employees. 526 00:15:44,920 --> 00:15:45,760 Really? 527 00:15:45,760 --> 00:15:46,600 It's pretty entertaining. 528 00:15:46,600 --> 00:15:47,560 That's amazing. 529 00:15:47,560 --> 00:15:48,400 Okay. 530 00:15:48,400 --> 00:15:50,720 And then finally, we hear a seamless transfer to Jack, 531 00:15:50,720 --> 00:15:52,040 a specialized sales agent. 532 00:15:52,040 --> 00:15:52,880 Okay. 533 00:15:52,880 --> 00:15:55,640 Showcasing the multi-agent capabilities of his system. 534 00:15:55,640 --> 00:15:56,480 Wow. 535 00:15:56,480 --> 00:15:58,000 And it all come together like that, 536 00:15:58,000 --> 00:15:59,760 really brings the concept to life. 537 00:15:59,760 --> 00:16:02,400 It's clear that he's built something really special. 538 00:16:02,400 --> 00:16:03,960 Did he share any revenue numbers? 539 00:16:03,960 --> 00:16:05,280 He did his best month. 540 00:16:05,280 --> 00:16:07,600 He brought in close to $100,000. 541 00:16:07,600 --> 00:16:08,440 What? 542 00:16:08,440 --> 00:16:09,280 Yeah. 543 00:16:09,280 --> 00:16:12,000 And he's consistently seeing between $30,000 and $60,000 544 00:16:12,000 --> 00:16:12,840 per month. 545 00:16:12,840 --> 00:16:13,680 That's incredible. 546 00:16:13,680 --> 00:16:16,280 And all without millions of dollars in funding. 547 00:16:16,280 --> 00:16:17,120 Yeah. 548 00:16:17,120 --> 00:16:19,640 That just proves that you don't need a ton of money 549 00:16:19,640 --> 00:16:21,080 to succeed in this space. 550 00:16:21,080 --> 00:16:21,920 Right. 551 00:16:21,920 --> 00:16:24,320 You just need a great idea and the right skills 552 00:16:24,320 --> 00:16:26,160 and the willingness to put in the work. 553 00:16:26,160 --> 00:16:27,000 For sure. 554 00:16:27,000 --> 00:16:29,800 This has been such an eye-opening deep dive so far. 555 00:16:29,800 --> 00:16:30,640 It has. 556 00:16:30,640 --> 00:16:33,240 We've gone from like the very basics of understanding 557 00:16:33,240 --> 00:16:34,320 what Aries is. 558 00:16:34,320 --> 00:16:35,160 Yeah. 559 00:16:35,160 --> 00:16:36,160 How it's different from, you know, 560 00:16:36,160 --> 00:16:38,520 all these other traditional software models 561 00:16:38,520 --> 00:16:41,360 to seeing real-world examples of people building 562 00:16:41,360 --> 00:16:43,960 thriving businesses with these AI agents. 563 00:16:43,960 --> 00:16:44,800 Yeah. 564 00:16:44,800 --> 00:16:45,640 It's really exciting stuff. 565 00:16:45,640 --> 00:16:46,880 And we've only scratched the surface. 566 00:16:46,880 --> 00:16:47,720 Right. 567 00:16:47,720 --> 00:16:48,760 We haven't even talked about what he said. 568 00:16:48,760 --> 00:16:51,520 Those horizontal agent platforms 569 00:16:51,520 --> 00:16:53,600 that can build vertical agents. 570 00:16:53,600 --> 00:16:54,680 From a single prompt. 571 00:16:54,680 --> 00:16:55,520 Yeah. 572 00:16:55,520 --> 00:16:56,960 It feels like we're on the verge of like 573 00:16:56,960 --> 00:16:58,920 a massive technological shift. 574 00:16:58,920 --> 00:16:59,760 Absolutely. 575 00:16:59,760 --> 00:17:00,840 It's really exciting. 576 00:17:00,840 --> 00:17:01,680 It is. 577 00:17:01,680 --> 00:17:03,280 But there's so much more to explore. 578 00:17:03,280 --> 00:17:04,120 Yeah. 579 00:17:04,120 --> 00:17:06,320 We still need to talk about what this means 580 00:17:06,320 --> 00:17:08,400 for the future of work. 581 00:17:08,400 --> 00:17:09,240 Right. 582 00:17:09,240 --> 00:17:11,080 What does this mean for the broader economy? 583 00:17:11,080 --> 00:17:12,320 It's exactly. 584 00:17:12,320 --> 00:17:14,240 We'll be tackling all of that and more 585 00:17:14,240 --> 00:17:16,480 in part two of this episode. 586 00:17:16,480 --> 00:17:19,120 So stay tuned because this deep dive 587 00:17:19,120 --> 00:17:21,000 is just getting started. 588 00:17:21,000 --> 00:17:21,840 Yeah. 589 00:17:21,840 --> 00:17:23,800 This is just the beginning. 590 00:17:23,800 --> 00:17:25,240 Welcome back. 591 00:17:25,240 --> 00:17:27,800 I don't know about you, but my head is still spinning 592 00:17:27,800 --> 00:17:29,560 from all the possibilities we talked about 593 00:17:29,560 --> 00:17:30,880 in the first part of this deep dive. 594 00:17:30,880 --> 00:17:31,080 Yeah. 595 00:17:31,080 --> 00:17:32,120 There's a lot to unpack. 596 00:17:32,120 --> 00:17:32,520 For sure. 597 00:17:32,520 --> 00:17:34,480 Like just hearing Chase's story really 598 00:17:34,480 --> 00:17:37,000 drove home the point that this AI thing is not 599 00:17:37,000 --> 00:17:39,320 just some futuristic fantasy. 600 00:17:39,320 --> 00:17:40,560 It's happening right now. 601 00:17:40,560 --> 00:17:42,800 People are already making serious money with it. 602 00:17:42,800 --> 00:17:44,040 Absolutely. 603 00:17:44,040 --> 00:17:46,120 But it also makes you wonder like what 604 00:17:46,120 --> 00:17:50,040 happens when these AI agents get even more powerful. 605 00:17:50,040 --> 00:17:50,560 Yeah. 606 00:17:50,560 --> 00:17:51,400 And sophisticated. 607 00:17:51,400 --> 00:17:53,600 Well, what does that mean for the future of work? 608 00:17:53,600 --> 00:17:54,080 Right. 609 00:17:54,080 --> 00:17:56,480 Are we all going to be replaced by robots? 610 00:17:56,480 --> 00:17:58,320 Well, the video creator definitely 611 00:17:58,320 --> 00:17:59,880 had some interesting thoughts on that. 612 00:17:59,880 --> 00:18:00,480 OK. 613 00:18:00,480 --> 00:18:03,320 He predicted that both vertical and horizontal agents 614 00:18:03,320 --> 00:18:05,200 are going to have a significant impact. 615 00:18:05,200 --> 00:18:05,920 OK. 616 00:18:05,920 --> 00:18:08,760 But not necessarily in a way that like eliminates 617 00:18:08,760 --> 00:18:09,960 human jobs altogether. 618 00:18:09,960 --> 00:18:10,520 OK. 619 00:18:10,520 --> 00:18:13,080 He sees a future where some people 620 00:18:13,080 --> 00:18:15,880 will gravitate towards those highly specialized vertical 621 00:18:15,880 --> 00:18:19,240 solutions, while others will embrace the flexibility 622 00:18:19,240 --> 00:18:22,480 and customization of those horizontal agent platforms. 623 00:18:22,480 --> 00:18:27,200 So not a complete AI takeover, but a shift in how we work 624 00:18:27,200 --> 00:18:29,200 and the kinds of jobs that are in demand. 625 00:18:29,200 --> 00:18:29,800 Exactly. 626 00:18:29,800 --> 00:18:30,300 OK. 627 00:18:30,300 --> 00:18:31,960 He even suggested that A-Rest could 628 00:18:31,960 --> 00:18:35,360 lead to a surge in entrepreneurship and innovation. 629 00:18:35,360 --> 00:18:36,120 Oh, interesting. 630 00:18:36,120 --> 00:18:39,880 Like imagine a world where anyone with a good idea 631 00:18:39,880 --> 00:18:43,640 and access to the right AI tools could build and scale 632 00:18:43,640 --> 00:18:46,840 a business without needing a huge team 633 00:18:46,840 --> 00:18:49,000 or a ton of upfront investment. 634 00:18:49,000 --> 00:18:50,480 That's a pretty amazing thought. 635 00:18:50,480 --> 00:18:50,840 It is. 636 00:18:50,840 --> 00:18:52,000 It's really empowering. 637 00:18:52,000 --> 00:18:52,920 But let's be realistic. 638 00:18:52,920 --> 00:18:54,960 It's not all sunshine and rainbows, right? 639 00:18:54,960 --> 00:18:55,760 Of course not. 640 00:18:55,760 --> 00:18:57,600 There have to be some potential downsides 641 00:18:57,600 --> 00:18:58,720 to this whole worst thing. 642 00:18:58,720 --> 00:18:59,400 Yeah, for sure. 643 00:18:59,400 --> 00:19:00,880 As with any disruptive technology, 644 00:19:00,880 --> 00:19:02,560 there are going to be challenges and adjustments 645 00:19:02,560 --> 00:19:03,080 along the way. 646 00:19:03,080 --> 00:19:03,560 Sure. 647 00:19:03,560 --> 00:19:05,960 One of the biggest concerns is the potential impact 648 00:19:05,960 --> 00:19:06,920 on the workforce. 649 00:19:06,920 --> 00:19:07,320 Right. 650 00:19:07,320 --> 00:19:10,880 If AI agents can automate entire processes, 651 00:19:10,880 --> 00:19:13,800 what happens to the people who used to perform those tasks? 652 00:19:13,800 --> 00:19:14,000 Yeah. 653 00:19:14,000 --> 00:19:16,360 Are we going to see massive job displacement? 654 00:19:16,360 --> 00:19:18,480 Will certain skills become obsolete? 655 00:19:18,480 --> 00:19:19,800 Those are all valid questions. 656 00:19:19,800 --> 00:19:20,120 Yeah. 657 00:19:20,120 --> 00:19:22,800 And they're questions that we need to be asking 658 00:19:22,800 --> 00:19:24,720 as this technology continues to evolve. 659 00:19:24,720 --> 00:19:26,400 So how do we address that? 660 00:19:26,400 --> 00:19:30,280 Are there things that we can do to prepare for those changes? 661 00:19:30,280 --> 00:19:31,600 Well, I think first and foremost, 662 00:19:31,600 --> 00:19:33,720 we need to be proactive in thinking 663 00:19:33,720 --> 00:19:37,160 about how we can retrain and reskill workers 664 00:19:37,160 --> 00:19:40,520 so that they can thrive in this new landscape. 665 00:19:40,520 --> 00:19:42,920 Maybe we'll see a shift towards roles 666 00:19:42,920 --> 00:19:46,080 that require more creativity, critical thinking, 667 00:19:46,080 --> 00:19:47,040 human connection. 668 00:19:47,040 --> 00:19:47,280 Right. 669 00:19:47,280 --> 00:19:49,160 The things that AI can't replicate. 670 00:19:49,160 --> 00:19:49,920 Exactly. 671 00:19:49,920 --> 00:19:51,960 Those are the skills that are going to be in high demand. 672 00:19:51,960 --> 00:19:54,080 So it's not just about learning how to code. 673 00:19:54,080 --> 00:19:54,360 Right. 674 00:19:54,360 --> 00:19:56,840 It's about developing those uniquely human skills. 675 00:19:56,840 --> 00:19:57,160 Yeah. 676 00:19:57,160 --> 00:19:58,520 I'm hoping that will be the case. 677 00:19:58,520 --> 00:19:59,080 Me too. 678 00:19:59,080 --> 00:20:02,520 It would be incredible to see AS create new opportunities 679 00:20:02,520 --> 00:20:05,440 for people to do work that is more meaningful and fulfilling. 680 00:20:05,440 --> 00:20:05,840 Yeah. 681 00:20:05,840 --> 00:20:06,760 Absolutely. 682 00:20:06,760 --> 00:20:10,280 The video creator also had a pretty mind-blowing prediction 683 00:20:10,280 --> 00:20:13,280 about the future of these horizontal agent platforms. 684 00:20:13,280 --> 00:20:14,040 Oh, yeah. 685 00:20:14,040 --> 00:20:15,080 Tell me about that. 686 00:20:15,080 --> 00:20:18,720 So he envisioned a future where these platforms could 687 00:20:18,720 --> 00:20:22,840 essentially create highly effective vertical agents 688 00:20:22,840 --> 00:20:25,360 from a single prompt, almost like magic. 689 00:20:25,360 --> 00:20:26,400 OK. 690 00:20:26,400 --> 00:20:30,720 Imagine being able to say, I need an agent that does X. 691 00:20:30,720 --> 00:20:33,360 And the platform just builds it for you ready to go. 692 00:20:33,360 --> 00:20:36,600 That sounds incredible, but also a little intimidating. 693 00:20:36,600 --> 00:20:37,760 It is a little bit intimidating. 694 00:20:37,760 --> 00:20:41,960 If it's that easy to create these powerful AI agents 695 00:20:41,960 --> 00:20:44,760 that make it even harder for people to keep up. 696 00:20:44,760 --> 00:20:46,320 It's a valid concern. 697 00:20:46,320 --> 00:20:49,800 But the creator also believed that these agents will 698 00:20:49,800 --> 00:20:52,360 be able to learn and improve over time. 699 00:20:52,360 --> 00:20:52,960 OK. 700 00:20:52,960 --> 00:20:56,360 So they essentially become even more valuable and efficient 701 00:20:56,360 --> 00:20:58,840 as they gather more data and experience. 702 00:20:58,840 --> 00:21:00,440 So maybe it's not about competing 703 00:21:00,440 --> 00:21:03,840 with these super intelligent AI agents, 704 00:21:03,840 --> 00:21:05,840 but learning how to work alongside them 705 00:21:05,840 --> 00:21:08,760 and leverage their capabilities to our advantage. 706 00:21:08,760 --> 00:21:10,280 I think that's a great way to look at it. 707 00:21:10,280 --> 00:21:10,480 OK. 708 00:21:10,480 --> 00:21:11,960 So the video also mentioned something 709 00:21:11,960 --> 00:21:14,360 called thermodynamic computing. 710 00:21:14,360 --> 00:21:16,960 And kind of suggested that it could have an even bigger 711 00:21:16,960 --> 00:21:19,040 impact than AI and then your future. 712 00:21:19,040 --> 00:21:19,440 Right. 713 00:21:19,440 --> 00:21:20,800 Have you heard much about that? 714 00:21:20,800 --> 00:21:23,520 To be honest, that's a relatively new concept to me. 715 00:21:23,520 --> 00:21:25,840 It wasn't discussed in a lot of detail in the video. 716 00:21:25,840 --> 00:21:26,360 OK. 717 00:21:26,360 --> 00:21:27,800 It sounds fascinating, though. 718 00:21:27,800 --> 00:21:28,280 Yeah. 719 00:21:28,280 --> 00:21:28,600 It does. 720 00:21:28,600 --> 00:21:30,960 Maybe it's something we can explore in a future deep dive. 721 00:21:30,960 --> 00:21:32,480 I'm adding that to our list, all right. 722 00:21:32,480 --> 00:21:35,040 So we've talked about the potential for NES 723 00:21:35,040 --> 00:21:38,560 to disrupt industries, create new opportunities, 724 00:21:38,560 --> 00:21:41,200 and even transform the nature of work itself. 725 00:21:41,200 --> 00:21:42,080 It's a big deal. 726 00:21:42,080 --> 00:21:43,280 It is a big deal. 727 00:21:43,280 --> 00:21:45,720 But as with any powerful technology, 728 00:21:45,720 --> 00:21:47,760 there are bound to be some challenges 729 00:21:47,760 --> 00:21:50,720 and ethical considerations that we need to address. 730 00:21:50,720 --> 00:21:51,200 For sure. 731 00:21:51,200 --> 00:21:52,720 We've touched on some of those already, 732 00:21:52,720 --> 00:21:55,320 like the potential for job displacement, 733 00:21:55,320 --> 00:21:57,720 the need for retraining and reskilling. 734 00:21:57,720 --> 00:21:58,120 Right. 735 00:21:58,120 --> 00:22:00,920 But what are some of the other ethical considerations 736 00:22:00,920 --> 00:22:02,640 that come into play with an AS? 737 00:22:02,640 --> 00:22:07,240 Well, one concern is the potential for bias in AI 738 00:22:07,240 --> 00:22:08,120 algorithms. 739 00:22:08,120 --> 00:22:09,000 OK. 740 00:22:09,000 --> 00:22:12,680 If these agents are trained on bias data, 741 00:22:12,680 --> 00:22:16,240 they could perpetuate and even amplify existing inequalities. 742 00:22:16,240 --> 00:22:17,840 That's a really important point. 743 00:22:17,840 --> 00:22:18,280 It is. 744 00:22:18,280 --> 00:22:20,080 So we need to make sure that the data that's 745 00:22:20,080 --> 00:22:22,680 used to train these agents is representative. 746 00:22:22,680 --> 00:22:23,040 Yeah. 747 00:22:23,040 --> 00:22:24,000 Unbiased. 748 00:22:24,000 --> 00:22:24,680 Absolutely. 749 00:22:24,680 --> 00:22:27,320 So that they're not making unfair or discriminatory 750 00:22:27,320 --> 00:22:28,240 decisions. 751 00:22:28,240 --> 00:22:28,560 Right. 752 00:22:28,560 --> 00:22:31,320 And we need to be vigilant about monitoring these agents 753 00:22:31,320 --> 00:22:33,560 to make sure that they're being used ethically 754 00:22:33,560 --> 00:22:34,360 and responsibly. 755 00:22:34,360 --> 00:22:34,960 OK. 756 00:22:34,960 --> 00:22:37,240 As they become more sophisticated, 757 00:22:37,240 --> 00:22:40,320 we need to develop clear guidelines and safeguards 758 00:22:40,320 --> 00:22:42,560 to prevent any unintended consequences. 759 00:22:42,560 --> 00:22:44,360 It sounds like we need a combination 760 00:22:44,360 --> 00:22:47,920 of technological advancements and thoughtful regulation. 761 00:22:47,920 --> 00:22:48,480 I agree. 762 00:22:48,480 --> 00:22:51,160 To really navigate this new landscape successfully. 763 00:22:51,160 --> 00:22:51,560 Yeah. 764 00:22:51,560 --> 00:22:53,240 It's going to be a group effort, for sure. 765 00:22:53,240 --> 00:22:55,520 This has been such a thought provoking conversation. 766 00:22:55,520 --> 00:22:56,160 It has been. 767 00:22:56,160 --> 00:22:59,920 We've covered so much ground from the technical details 768 00:22:59,920 --> 00:23:04,160 of what AS is to the potential impact on the workforce, 769 00:23:04,160 --> 00:23:07,240 the ethical considerations that we need to address. 770 00:23:07,240 --> 00:23:07,720 Right. 771 00:23:07,720 --> 00:23:10,600 It's clear that AS is a powerful force that's 772 00:23:10,600 --> 00:23:13,640 going to shape the future in some really profound ways. 773 00:23:13,640 --> 00:23:14,360 It really is. 774 00:23:14,360 --> 00:23:16,560 And as we've discussed, it's important to approach 775 00:23:16,560 --> 00:23:18,800 this technology with both enthusiasm 776 00:23:18,800 --> 00:23:21,000 and a healthy dose of caution. 777 00:23:21,000 --> 00:23:22,040 I agree with that. 778 00:23:22,040 --> 00:23:23,960 We need to embrace the possibilities 779 00:23:23,960 --> 00:23:26,120 while being mindful of the potential challenges. 780 00:23:26,120 --> 00:23:28,240 And most importantly, I think we need 781 00:23:28,240 --> 00:23:31,000 to engage in these open and honest conversations 782 00:23:31,000 --> 00:23:33,840 about how we want to shape this future together. 783 00:23:33,840 --> 00:23:34,360 Right. 784 00:23:34,360 --> 00:23:35,720 It's not something that's going to happen to us. 785 00:23:35,720 --> 00:23:37,560 It's something that we're all creating together. 786 00:23:37,560 --> 00:23:38,320 Exactly. 787 00:23:38,320 --> 00:23:40,080 So let's keep learning. 788 00:23:40,080 --> 00:23:44,280 Keep exploring and keep asking those tough questions. 789 00:23:44,280 --> 00:23:45,520 I love that. 790 00:23:45,520 --> 00:23:47,240 And in the next part of this deep dive, 791 00:23:47,240 --> 00:23:49,840 we'll delve into even more fascinating aspects 792 00:23:49,840 --> 00:23:51,400 of this A-V-S revolution. 793 00:23:51,400 --> 00:23:52,480 So stay tuned. 794 00:23:52,480 --> 00:23:53,000 Yeah. 795 00:23:53,000 --> 00:23:55,000 Stick with us. 796 00:23:55,000 --> 00:23:56,680 Welcome back to the deep dive. 797 00:23:56,680 --> 00:23:58,600 We've covered so much ground already. 798 00:23:58,600 --> 00:24:01,200 It's kind of mind blowing to think about how quickly 799 00:24:01,200 --> 00:24:02,720 this is all evolving. 800 00:24:02,720 --> 00:24:03,080 I know. 801 00:24:03,080 --> 00:24:04,040 It's really incredible. 802 00:24:04,040 --> 00:24:06,120 And it's exciting, but I'm also feeling 803 00:24:06,120 --> 00:24:08,400 like a sense of responsibility. 804 00:24:08,400 --> 00:24:11,840 We need to make sure that we're approaching this technology 805 00:24:11,840 --> 00:24:13,120 thoughtfully and ethically. 806 00:24:13,120 --> 00:24:13,600 Yeah. 807 00:24:13,600 --> 00:24:13,960 Absolutely. 808 00:24:13,960 --> 00:24:15,880 We're not just about the cool factor. 809 00:24:15,880 --> 00:24:16,400 Right. 810 00:24:16,400 --> 00:24:19,520 It's about shaping a future that benefits everybody. 811 00:24:19,520 --> 00:24:20,640 Yeah, for sure. 812 00:24:20,640 --> 00:24:22,920 We need to be proactive about addressing 813 00:24:22,920 --> 00:24:24,880 those potential challenges and making sure 814 00:24:24,880 --> 00:24:25,960 this is used for good. 815 00:24:25,960 --> 00:24:26,360 Yeah. 816 00:24:26,360 --> 00:24:28,200 And one of the things that we talked about last time 817 00:24:28,200 --> 00:24:29,920 was the impact on the workforce. 818 00:24:29,920 --> 00:24:30,320 Right. 819 00:24:30,320 --> 00:24:32,680 Like, that's the big question. 820 00:24:32,680 --> 00:24:34,760 Is AI going to replace human jobs? 821 00:24:34,760 --> 00:24:35,000 Yeah. 822 00:24:35,000 --> 00:24:36,560 That's the question on everybody's minds. 823 00:24:36,560 --> 00:24:38,760 So what are your thoughts on that? 824 00:24:38,760 --> 00:24:41,080 Well, I don't think it's a simple either or scenario. 825 00:24:41,080 --> 00:24:42,360 OK. 826 00:24:42,360 --> 00:24:46,040 AI agents are going to automate certain tasks 827 00:24:46,040 --> 00:24:49,560 and processes, which could lead to some job displacement. 828 00:24:49,560 --> 00:24:50,080 Yeah. 829 00:24:50,080 --> 00:24:51,280 But it's also important to remember 830 00:24:51,280 --> 00:24:54,280 that AI can create new opportunities 831 00:24:54,280 --> 00:24:56,360 and even enhance existing roles. 832 00:24:56,360 --> 00:24:58,240 So maybe it's less about AI taking over 833 00:24:58,240 --> 00:25:01,200 and more about humans and AI working together in new ways. 834 00:25:01,200 --> 00:25:01,800 Exactly. 835 00:25:01,800 --> 00:25:02,400 Yeah. 836 00:25:02,400 --> 00:25:05,080 I think the key is to focus on developing skills 837 00:25:05,080 --> 00:25:06,360 that complement AI. 838 00:25:06,360 --> 00:25:07,000 OK. 839 00:25:07,000 --> 00:25:10,280 Things like creativity, critical thinking, 840 00:25:10,280 --> 00:25:14,120 emotional intelligence, complex problem solving. 841 00:25:14,120 --> 00:25:14,520 OK. 842 00:25:14,520 --> 00:25:16,520 Those are going to be the schools that are in high demand. 843 00:25:16,520 --> 00:25:16,880 Right. 844 00:25:16,880 --> 00:25:19,960 So we need to adapt and evolve, just like we've always 845 00:25:19,960 --> 00:25:22,040 done, as new technologies emerge. 846 00:25:22,040 --> 00:25:22,760 Exactly. 847 00:25:22,760 --> 00:25:24,600 Humans are incredibly adaptable. 848 00:25:24,600 --> 00:25:25,160 That's true. 849 00:25:25,160 --> 00:25:26,960 And I think it's also important to remember 850 00:25:26,960 --> 00:25:29,960 that AI is ultimately a tool. 851 00:25:29,960 --> 00:25:33,200 It's up to us as humans to decide how we want to use it. 852 00:25:33,200 --> 00:25:33,480 Right. 853 00:25:33,480 --> 00:25:35,480 And what kind of future we want to build with it. 854 00:25:35,480 --> 00:25:36,720 It all comes back to us. 855 00:25:36,720 --> 00:25:40,320 So how can we shape the future of AI? 856 00:25:40,320 --> 00:25:42,080 I think one of the most important things 857 00:25:42,080 --> 00:25:45,800 is to have these open and honest conversations 858 00:25:45,800 --> 00:25:47,360 about the ethical considerations. 859 00:25:47,360 --> 00:25:47,920 Right. 860 00:25:47,920 --> 00:25:51,680 You know, we need to establish clear guidelines and safeguards 861 00:25:51,680 --> 00:25:54,800 to make sure that these agents are used responsibly 862 00:25:54,800 --> 00:25:56,200 and that we're prepared to address 863 00:25:56,200 --> 00:25:58,280 any unintended consequences. 864 00:25:58,280 --> 00:26:01,960 You mentioned bias in AI algorithms last time. 865 00:26:01,960 --> 00:26:02,320 Right. 866 00:26:02,320 --> 00:26:03,800 Is one potential concern. 867 00:26:03,800 --> 00:26:07,240 So how do we ensure that these agents are fair 868 00:26:07,240 --> 00:26:10,280 and don't perpetuate existing inequalities? 869 00:26:10,280 --> 00:26:10,680 Yeah. 870 00:26:10,680 --> 00:26:13,800 So it starts with the data that's used to train the agents. 871 00:26:13,800 --> 00:26:14,320 OK. 872 00:26:14,320 --> 00:26:16,240 We need to make sure that the data is representative 873 00:26:16,240 --> 00:26:17,280 and unbiased. 874 00:26:17,280 --> 00:26:17,800 Right. 875 00:26:17,800 --> 00:26:20,200 And that it reflects the diversity of the population. 876 00:26:20,200 --> 00:26:20,480 OK. 877 00:26:20,480 --> 00:26:22,600 So it's not just about the technology itself, 878 00:26:22,600 --> 00:26:25,200 but also about the people who are developing and deploying it. 879 00:26:25,200 --> 00:26:25,680 Exactly. 880 00:26:25,680 --> 00:26:29,160 We need diversity and inclusion at every stage of the process. 881 00:26:29,160 --> 00:26:29,840 I love that. 882 00:26:29,840 --> 00:26:30,000 OK. 883 00:26:30,000 --> 00:26:31,440 So we've talked about a lot. 884 00:26:31,440 --> 00:26:32,360 Covered a lot of ground. 885 00:26:32,360 --> 00:26:35,480 But what's the one key takeaway that you hope our listeners will 886 00:26:35,480 --> 00:26:37,120 walk away with today? 887 00:26:37,120 --> 00:26:39,280 I hope they remember that the future of the cities 888 00:26:39,280 --> 00:26:40,800 is not predetermined. 889 00:26:40,800 --> 00:26:43,360 It's something that we are all shaping together. 890 00:26:43,360 --> 00:26:46,280 How are for the choices we make and the actions that we take. 891 00:26:46,280 --> 00:26:46,720 Right. 892 00:26:46,720 --> 00:26:49,520 So let's be intentional about building a future. 893 00:26:49,520 --> 00:26:50,360 OK. 894 00:26:50,360 --> 00:26:52,400 Where AI empowers us all. 895 00:26:52,400 --> 00:26:54,120 That's a great message to end on. 896 00:26:54,120 --> 00:26:54,480 Yeah. 897 00:26:54,480 --> 00:26:57,440 Thank you so much for joining us on this deep dive 898 00:26:57,440 --> 00:26:58,520 into the world of Astier. 899 00:26:58,520 --> 00:26:59,320 Oh, it's been a pleasure. 900 00:26:59,320 --> 00:27:00,800 It's been an incredible journey. 901 00:27:00,800 --> 00:27:01,840 It really has. 902 00:27:01,840 --> 00:27:04,720 And thank you to all of our listeners for tuning in 903 00:27:04,720 --> 00:27:06,560 and joining us on this exploration. 904 00:27:06,560 --> 00:27:07,320 Yes. 905 00:27:07,320 --> 00:27:09,040 Thank you so much for listening. 906 00:27:09,040 --> 00:27:11,680 Remember, the future is what we make it. 907 00:27:11,680 --> 00:27:13,160 It really is. 908 00:27:13,160 --> 00:27:14,800 So let's keep learning. 909 00:27:14,800 --> 00:27:15,160 Yeah. 910 00:27:15,160 --> 00:27:16,640 Keep questioning. 911 00:27:16,640 --> 00:27:19,840 And keep pushing the boundaries of what's possible. 912 00:27:19,840 --> 00:27:32,680 Until next time, keep diving deep.