WEBVTT 00:00:00.000 --> 00:00:02.319 Starting an online brand used to be, you know, 00:00:02.520 --> 00:00:05.480 months of just guessing, followed by that endless 00:00:05.480 --> 00:00:07.879 spreadsheet anxiety. Oh, absolutely. Total guesswork. 00:00:08.359 --> 00:00:12.640 But today, these specialized AI tools, they can 00:00:12.640 --> 00:00:15.539 take, what, three months of that painful, slow 00:00:15.539 --> 00:00:18.199 research and just convert it. Into three days. 00:00:18.719 --> 00:00:21.679 Three highly focused days of strategic planning. 00:00:21.760 --> 00:00:23.780 That speed. That right there is the ultimate 00:00:23.780 --> 00:00:25.879 competitive edge in modern commerce. Yeah. It's 00:00:25.879 --> 00:00:28.839 not even negotiable anymore. Welcome back to 00:00:28.839 --> 00:00:31.670 The Deep Dive. Today, we are dissecting the blueprint 00:00:31.670 --> 00:00:35.530 for building a successful online brand, a focused, 00:00:35.990 --> 00:00:39.149 powerful brand, using this new generation of 00:00:39.149 --> 00:00:41.329 AI e -commerce tools. Yeah, that's the mission. 00:00:41.450 --> 00:00:43.329 We've got a comprehensive guide in front of us 00:00:43.329 --> 00:00:45.630 that really demonstrates a complete shift in 00:00:45.630 --> 00:00:47.429 thinking. We're moving from the old way, just 00:00:47.429 --> 00:00:49.250 hoping you picked a good product. And crossing 00:00:49.250 --> 00:00:52.009 your finger. To the new way. Strategically engineering 00:00:52.009 --> 00:00:54.829 a solution based on pure data. And what's so 00:00:54.829 --> 00:00:56.409 fascinating here is that the goal isn't just 00:00:56.409 --> 00:00:58.950 about optimization, it's intelligence. We're 00:00:58.950 --> 00:01:01.710 going to reveal how this specialized AI moves 00:01:01.710 --> 00:01:06.450 the entire process from blind optimism to strategic 00:01:06.450 --> 00:01:08.590 data -backed decisions. We're going to cover 00:01:08.590 --> 00:01:11.280 a lot today. how to find these hidden trends, 00:01:11.659 --> 00:01:13.900 what the secret language of negative customer 00:01:13.900 --> 00:01:16.920 reviews actually sounds like, and sourcing products 00:01:16.920 --> 00:01:19.060 like a high -level pro. And critically, how to 00:01:19.060 --> 00:01:21.760 avoid those deadly mistakes that just kill most 00:01:21.760 --> 00:01:23.819 startup capital right out of the gate. And just 00:01:23.819 --> 00:01:25.819 to be clear, when we say AI e -commerce, what 00:01:25.819 --> 00:01:27.659 are we talking about? We're talking about using 00:01:27.659 --> 00:01:30.719 a specialized robot connected to real -time market 00:01:30.719 --> 00:01:33.579 data. Okay, so not just a chatbot. Definitely 00:01:33.579 --> 00:01:35.700 not just a chatbot. All right, let's unpack this 00:01:35.700 --> 00:01:38.920 core strategic shift. The source material really 00:01:38.920 --> 00:01:41.200 emphasizes moving away from selling what you 00:01:41.200 --> 00:01:43.819 think is cool. Right. And immediately focusing 00:01:43.819 --> 00:01:46.640 on finding these market gaps. Exactly. And this 00:01:46.640 --> 00:01:49.079 is why it has to be specialized AI. It's fundamentally 00:01:49.079 --> 00:01:51.180 different from a general model like, say, chat 00:01:51.180 --> 00:01:54.939 GPT, which is trained on broad, often outdated 00:01:54.939 --> 00:01:57.900 public data. This kind of tool connects directly 00:01:57.900 --> 00:02:01.480 to global transaction databases like Alibaba. 00:02:01.640 --> 00:02:04.200 So it's not just scraping the web. It's seeing 00:02:04.200 --> 00:02:07.040 the actual buying trends, the factory reliability 00:02:07.040 --> 00:02:09.939 scores, the deep structure of product reviews 00:02:09.939 --> 00:02:12.860 in real time. Precisely. It's like having a hyper 00:02:12.860 --> 00:02:15.400 -efficient business partner who has read every 00:02:15.400 --> 00:02:18.780 single bad review and watched every global transaction 00:02:18.780 --> 00:02:22.020 in the last, like, 72 hours. Wow. The whole goal 00:02:22.020 --> 00:02:24.520 is just finding those gaps where customers are 00:02:24.520 --> 00:02:26.300 actively spending money, but they're unhappy. 00:02:26.659 --> 00:02:29.400 And that instantly moves you past selling abroad 00:02:29.400 --> 00:02:32.319 category, like camping lights. Yeah. The AI's 00:02:32.319 --> 00:02:35.780 real power is finding that micro niche. You aren't 00:02:35.780 --> 00:02:37.900 competing for lights. You're focused on something 00:02:37.900 --> 00:02:41.819 like... The portable camping lantern with built 00:02:41.819 --> 00:02:44.340 -in solar panels and mosquito repellent features. 00:02:44.439 --> 00:02:47.060 The strategic value there is immense because 00:02:47.060 --> 00:02:49.900 you find what they call a blue ocean. You just 00:02:49.900 --> 00:02:51.900 don't need to fight against those giants with 00:02:51.900 --> 00:02:54.280 the million dollar ad budgets for general terms. 00:02:54.360 --> 00:02:55.800 Because you're operating where the big brands 00:02:55.800 --> 00:02:57.900 aren't even paying attention. Exactly. And that 00:02:57.900 --> 00:02:59.719 links directly to understanding buying intent. 00:03:00.139 --> 00:03:03.379 Yeah. Once the AI pinpoints that micro niche, 00:03:03.599 --> 00:03:06.099 you have to ask why people are searching for 00:03:06.099 --> 00:03:08.830 that specific item. Right. Is that portable power 00:03:08.830 --> 00:03:11.150 station being bought because people are, you 00:03:11.150 --> 00:03:13.729 know, remote working from parks? Or is it because 00:03:13.729 --> 00:03:16.189 of the increase in emergency power outages and 00:03:16.189 --> 00:03:18.830 natural disasters? And understanding that reason 00:03:18.830 --> 00:03:21.389 changes absolutely everything about your marketing. 00:03:21.909 --> 00:03:23.930 If it's for remote work, you shouldn't be using 00:03:23.930 --> 00:03:25.949 rugged mountain climbing photos in your ads. 00:03:26.189 --> 00:03:29.069 No, you should be marketing it with clean, modern 00:03:29.069 --> 00:03:32.530 photos of someone in a cozy coffee shop. That 00:03:32.530 --> 00:03:36.479 why saves you thousands in wasted ad spend. So 00:03:36.479 --> 00:03:38.680 if that speed to market is the key, what's the 00:03:38.680 --> 00:03:40.979 ultimate strategic advantage of finding these 00:03:40.979 --> 00:03:44.020 tiny micro niches? It's about minimizing competition 00:03:44.020 --> 00:03:47.819 and lowering ad costs dramatically, which brings 00:03:47.819 --> 00:03:49.860 us to what the guide calls the pain point flip. 00:03:50.060 --> 00:03:53.120 This is the fun part. The strategy is to find 00:03:53.120 --> 00:03:55.460 an existing product that only has, say, a three 00:03:55.460 --> 00:03:58.860 -star rating and use AI analysis to fix its exact 00:03:58.860 --> 00:04:01.719 flaws. Then you relaunch it as a five -star brand. 00:04:02.099 --> 00:04:04.060 You stop guessing what to improve, and you start 00:04:04.060 --> 00:04:06.860 listening to these patterns of pain. I find this 00:04:06.860 --> 00:04:09.000 genuinely counterintuitive. Most sellers would 00:04:09.000 --> 00:04:11.030 look for five -star products to just copy. But 00:04:11.030 --> 00:04:14.330 the source gives this fantastic example, analyzing 00:04:14.330 --> 00:04:16.930 negative reviews for ergonomic office chairs 00:04:16.930 --> 00:04:20.290 under $150, and then grouping the complaints. 00:04:20.610 --> 00:04:23.230 Yeah, they grouped them into three buckets, comfort, 00:04:23.470 --> 00:04:26.519 durability, and assembly. And the surprising 00:04:26.519 --> 00:04:29.800 insight the AI revealed was that assembly bad 00:04:29.800 --> 00:04:33.519 instructions or missing parts was actually a 00:04:33.519 --> 00:04:36.379 bigger, more common complaint pattern than the 00:04:36.379 --> 00:04:38.519 comfort of the chair itself. That's amazing. 00:04:38.759 --> 00:04:40.879 I would have bet money on comfort being the top 00:04:40.879 --> 00:04:44.290 complaint. But that shows the depth AI provides. 00:04:44.790 --> 00:04:47.009 Most sellers are just focused on physical features. 00:04:47.970 --> 00:04:50.250 But the AI shows that the experience of assembly 00:04:50.250 --> 00:04:53.230 is often the real gap. And think about the implication 00:04:53.230 --> 00:04:56.050 there. If 70 % of negative reviews are about 00:04:56.050 --> 00:04:58.170 how hard it is to put together, and you engineer 00:04:58.170 --> 00:05:01.310 a chair that comes 90 % pre -assembled. You've 00:05:01.310 --> 00:05:02.910 already won the market. It almost doesn't matter 00:05:02.910 --> 00:05:05.029 what the foam density is. And this allows you 00:05:05.029 --> 00:05:08.129 to justify a premium price. If the AI tells you, 00:05:08.250 --> 00:05:10.569 hey, people hate that the cheap plastic wheels 00:05:10.569 --> 00:05:13.310 scratch their new wooden floors, your job is 00:05:13.310 --> 00:05:15.149 clear. You're no longer just selling a chair. 00:05:15.230 --> 00:05:17.860 You're selling floor protection. Exactly. You're 00:05:17.860 --> 00:05:21.360 solving a specific noisy problem. A customer 00:05:21.360 --> 00:05:23.680 who just spent five grand on hardwood flooring 00:05:23.680 --> 00:05:26.420 will happily pay an extra 30 bucks for soft rubber 00:05:26.420 --> 00:05:28.879 wheels that promise not to ruin them. Solving 00:05:28.879 --> 00:05:31.379 a specific problem justifies that higher price 00:05:31.379 --> 00:05:34.060 point. So how can this granular review analysis 00:05:34.060 --> 00:05:37.980 turn a simple product into a high -profit premium 00:05:37.980 --> 00:05:41.199 item? Solving a specific noisy problem justifies 00:05:41.199 --> 00:05:43.920 a higher price point. Simple as that. Okay, now 00:05:43.920 --> 00:05:46.180 we move past just keyword searching and review 00:05:46.180 --> 00:05:49.439 analysis into what the guide calls agent tasks. 00:05:49.720 --> 00:05:52.199 This is about going beyond the surface to uncover 00:05:52.199 --> 00:05:55.920 these genuine, untapped customer wishes. This 00:05:55.920 --> 00:05:59.360 is where the AI truly starts to think like a 00:05:59.360 --> 00:06:01.839 human researcher. An agent task involves the 00:06:01.839 --> 00:06:04.439 AI browsing specialized forums, Reddit, Quora, 00:06:04.519 --> 00:06:06.259 focused hobbyist groups, to see what people are 00:06:06.259 --> 00:06:07.800 actually talking about. When they think no one 00:06:07.800 --> 00:06:09.660 is listening. Right. And the prompt strategy 00:06:09.660 --> 00:06:12.589 here is just brilliant. Scours, say, are parenting 00:06:12.589 --> 00:06:14.490 threads for sentences that start with the phrase, 00:06:14.550 --> 00:06:17.470 I wish there was AA, to find genuinely missing 00:06:17.470 --> 00:06:19.670 baby travel gear. See, Google shows you what 00:06:19.670 --> 00:06:22.009 people are selling. Agent tasks show you what 00:06:22.009 --> 00:06:24.050 people are wishing for. That's the difference 00:06:24.050 --> 00:06:27.209 between iterating on old ideas and creating true 00:06:27.209 --> 00:06:30.209 innovation. The source mentioned the AI discovering 00:06:30.209 --> 00:06:32.529 widespread frustration among left handed users 00:06:32.529 --> 00:06:34.850 with just standard kitchen can openers. I mean, 00:06:34.850 --> 00:06:38.069 that's a perfect low competition, high demand 00:06:38.069 --> 00:06:42.120 niche. Whoa. Imagine scaling that listening process 00:06:42.120 --> 00:06:45.819 to a billion forum queries across every niche, 00:06:46.420 --> 00:06:48.560 just instantly surfacing only the pain points 00:06:48.560 --> 00:06:51.339 and the wishes. It's truly transformative for 00:06:51.339 --> 00:06:53.480 product discovery. So if Google shows us what's 00:06:53.480 --> 00:06:56.300 currently selling, what unique actionable insights 00:06:56.300 --> 00:06:59.000 do these wishes provide that existing data just 00:06:59.000 --> 00:07:01.740 misses? Wishes reveal true market needs that 00:07:01.740 --> 00:07:03.879 current sellers are simply failing to address. 00:07:04.319 --> 00:07:05.819 All right, let's shift to the operational side. 00:07:06.100 --> 00:07:08.620 Sourcing. AI entirely shifts the power balance 00:07:08.620 --> 00:07:11.019 here from the factory to you, the buyer. It ensures 00:07:11.019 --> 00:07:13.879 you only deal with serious, high -quality manufacturing 00:07:13.879 --> 00:07:16.480 partners. And this guide lays out a really strict 00:07:16.480 --> 00:07:18.939 filtering process. It's designed to eliminate 00:07:18.939 --> 00:07:20.759 trading companies, the middlemen, right away. 00:07:21.040 --> 00:07:22.899 You're looking for suppliers with a minimum of 00:07:22.899 --> 00:07:25.540 five years on the platform, full trade assurance. 00:07:25.959 --> 00:07:29.620 verified supplier status. Specific quality certifications 00:07:29.620 --> 00:07:33.779 like ISO 9001 and confirmed OEM services. Right. 00:07:33.939 --> 00:07:36.379 That filters the bad options instantly. You don't 00:07:36.379 --> 00:07:39.019 want a thousand bad choices. You want the 10 00:07:39.019 --> 00:07:42.120 best choices. It saves you from that crippling 00:07:42.120 --> 00:07:45.819 analysis paralysis and just weeks of wasted communication. 00:07:46.000 --> 00:07:48.279 And once you find that supplier, you have to 00:07:48.279 --> 00:07:50.949 speak their language. The AI helps you avoid 00:07:50.949 --> 00:07:53.610 being labeled a, what, a newbie tourist. Yeah, 00:07:53.709 --> 00:07:55.970 exactly. It generates these highly technical 00:07:55.970 --> 00:07:58.089 questions. For example, asking about the difference 00:07:58.089 --> 00:08:01.089 between thermal fusion versus chemical adhesives 00:08:01.089 --> 00:08:03.569 for double layer bonding in a laptop bag. When 00:08:03.569 --> 00:08:06.009 you ask a manufacturer about custom tooling or 00:08:06.009 --> 00:08:08.509 a specific Pantone color code, they immediately 00:08:08.509 --> 00:08:10.829 see you as a serious partner. It's high level 00:08:10.829 --> 00:08:13.529 professional outreach. The AI drafts emails with 00:08:13.529 --> 00:08:15.490 that industry structure, including requests for 00:08:15.490 --> 00:08:17.569 detailed quotation sheets and product compliance 00:08:17.569 --> 00:08:20.870 documents like CE or FCC. And that authority, 00:08:20.870 --> 00:08:23.550 I imagine, scares off the low quality factories 00:08:23.550 --> 00:08:25.569 that don't have the certifications for global 00:08:25.569 --> 00:08:27.569 markets. It does. You skip the small talk and 00:08:27.569 --> 00:08:29.569 move straight to the serious conversations. And 00:08:29.569 --> 00:08:33.149 this is where AI truly becomes your chief financial 00:08:33.149 --> 00:08:36.090 officer. Because once you get Five quotes back, 00:08:36.250 --> 00:08:38.450 you're looking at a confusing mess of different 00:08:38.450 --> 00:08:43.850 prices and shipping terms. EXW, FOB, DDP. It's 00:08:43.850 --> 00:08:46.370 almost impossible for a beginner to compare that 00:08:46.370 --> 00:08:48.629 accurately. Wait, before we move on, what does 00:08:48.629 --> 00:08:51.190 DDP practically mean for my profit sheet? I see 00:08:51.190 --> 00:08:54.370 those acronyms constantly. DDP stands for Delivered 00:08:54.370 --> 00:08:56.970 Duty Paid. And practically, it means the supplier 00:08:56.970 --> 00:08:59.570 is responsible for absolutely everything. Shipping, 00:08:59.809 --> 00:09:03.340 tariffs, taxes. until the goods physically hit 00:09:03.340 --> 00:09:05.600 your warehouse. It saves you from those surprise 00:09:05.600 --> 00:09:07.340 customs bills that just wipe out your profit 00:09:07.340 --> 00:09:10.179 margin later on. Got it. So the AI calculates 00:09:10.179 --> 00:09:12.879 the true cost for each quote, factoring in all 00:09:12.879 --> 00:09:15.340 of that shipping risk lead time based on those 00:09:15.340 --> 00:09:17.620 terms. So between quality assurance and pricing, 00:09:18.019 --> 00:09:20.159 what is the most critical calculation a beginner 00:09:20.159 --> 00:09:22.659 must get right? The true cost calculation is 00:09:22.659 --> 00:09:24.720 everything. It determines the actual path to 00:09:24.720 --> 00:09:27.049 profit. So the engineered product is identified, 00:09:27.230 --> 00:09:29.850 the supplier is locked, the final strategic step 00:09:29.850 --> 00:09:32.750 is branding. And we're moving beyond just picking 00:09:32.750 --> 00:09:35.809 your favorite colors to using data -driven branding 00:09:35.809 --> 00:09:39.940 based on established color psychology. That's 00:09:39.940 --> 00:09:43.000 right. If your target is, say, Gen Z professionals, 00:09:43.419 --> 00:09:46.080 instead of picking bright, salesy colors, the 00:09:46.080 --> 00:09:48.600 AI might suggest a minimalist, calmer palette 00:09:48.600 --> 00:09:51.700 that conveys authenticity and trust. And that 00:09:51.700 --> 00:09:53.659 data -driven visual approach makes the store 00:09:53.659 --> 00:09:56.100 feel expensive and credible right away. Yeah. 00:09:56.840 --> 00:10:00.220 The AI... also defines the brand voice. Is it 00:10:00.220 --> 00:10:03.000 sarcastic, inspirational, highly educational, 00:10:03.539 --> 00:10:06.059 and provides example greetings for customer service? 00:10:06.340 --> 00:10:09.100 It just creates an authentic vibe instantly across 00:10:09.100 --> 00:10:11.419 all platforms. Which leads directly to high converting 00:10:11.419 --> 00:10:13.279 product descriptions through what the guide calls 00:10:13.279 --> 00:10:16.039 sensory marketing. Since a customer can't touch 00:10:16.039 --> 00:10:18.159 your product online, the language has to be so 00:10:18.159 --> 00:10:20.639 descriptive that it creates a mental image of 00:10:20.639 --> 00:10:22.860 the product in their mind. Right, like the eco 00:10:22.860 --> 00:10:25.820 -friendly pen example. You still use that FAB 00:10:25.820 --> 00:10:28.740 framework features, advantages, benefits, but 00:10:28.740 --> 00:10:31.159 you focus heavily on the sensory experience. 00:10:31.379 --> 00:10:33.639 You're describing the weight in the hand, the 00:10:33.639 --> 00:10:36.460 faint smell of the recycled wood, the satisfying 00:10:36.460 --> 00:10:39.179 glide of the ink on the page. You're selling 00:10:39.179 --> 00:10:41.620 the transformation, not just a list of features. 00:10:41.840 --> 00:10:46.000 So how does using AI for that brand voice create 00:10:46.000 --> 00:10:48.639 a competitive advantage over competitors who 00:10:48.639 --> 00:10:51.350 are just using generic marketing copy? It creates 00:10:51.350 --> 00:10:53.529 immediate authenticity, making the store feel 00:10:53.529 --> 00:10:56.350 more expensive and trustworthy. OK, now for the 00:10:56.350 --> 00:11:00.230 traps. AI provides incredible speed and intelligence, 00:11:00.710 --> 00:11:03.529 but haste is still a deadly poison. You have 00:11:03.529 --> 00:11:05.450 to protect your capital by actively avoiding 00:11:05.450 --> 00:11:07.809 these three major mistakes the source highlights. 00:11:08.129 --> 00:11:10.690 Mistake number one, digital blindness. The biggest 00:11:10.690 --> 00:11:13.409 rookie mistake is assuming that a high AI score 00:11:13.409 --> 00:11:15.990 for a supplier or a product equals high quality. 00:11:16.070 --> 00:11:19.019 It doesn't. The absolute unbreakable rule. You 00:11:19.019 --> 00:11:21.960 must always order a physical sample. An AI can 00:11:21.960 --> 00:11:25.019 analyze 10 ,000 beta points on paper, but it 00:11:25.019 --> 00:11:27.620 cannot touch the fabric to verify it's not itchy. 00:11:27.820 --> 00:11:30.879 It can't smell the plastic for toxic odors. You 00:11:30.879 --> 00:11:33.139 have to stress test it yourself. You know, I 00:11:33.139 --> 00:11:35.419 still wrestle with prompt drift myself when I'm 00:11:35.419 --> 00:11:37.440 trying to find reliable suppliers who aren't 00:11:37.440 --> 00:11:40.059 overselling their capabilities. So you can never, 00:11:40.179 --> 00:11:42.960 ever skip the physical check. Mistake number 00:11:42.960 --> 00:11:46.690 two, hidden margin. This is the $2 product that 00:11:46.690 --> 00:11:48.889 somehow becomes a $10 nightmare by the time it 00:11:48.889 --> 00:11:51.690 reaches your warehouse. Right. Beginners forget 00:11:51.690 --> 00:11:54.049 that shipping often charges for space, not just 00:11:54.049 --> 00:11:57.090 weight. That's volumetric weight. A light, fluffy 00:11:57.090 --> 00:12:00.429 pillow is extremely expensive to ship. This is 00:12:00.429 --> 00:12:03.190 why you must demand those DDP quotes. So you 00:12:03.190 --> 00:12:05.710 know the full landed cost upfront, including 00:12:05.710 --> 00:12:08.389 all the tariffs and taxes. If your profit margin 00:12:08.389 --> 00:12:10.870 isn't at least 30 percent after every single 00:12:10.870 --> 00:12:12.929 fee is paid, the math doesn't work. The math 00:12:12.929 --> 00:12:14.450 doesn't work. You have to be willing to walk 00:12:14.450 --> 00:12:16.970 away. And the final trap is the generalist trap. 00:12:17.470 --> 00:12:20.129 Greed kills growth. You cannot try to build a 00:12:20.129 --> 00:12:22.029 general department store selling 20 unrelated 00:12:22.029 --> 00:12:25.509 items. No, you use the AI to become the undisputed 00:12:25.509 --> 00:12:28.629 king of a single micro niche, something absurdly 00:12:28.629 --> 00:12:31.399 specific like pink orthopedic collars for senior 00:12:31.399 --> 00:12:35.580 cats. This laser focus lowers your ad costs dramatically, 00:12:35.980 --> 00:12:38.539 it increases your conversion rates, and it establishes 00:12:38.539 --> 00:12:40.759 you as the expert in the customer's mind. Which 00:12:40.759 --> 00:12:43.019 allows you to charge that premium. Focus creates 00:12:43.019 --> 00:12:45.289 wealth. So if AI does all this heavy lifting 00:12:45.289 --> 00:12:47.429 and provides such high -level data analysis, 00:12:48.070 --> 00:12:50.590 why is the physical sample still the single most 00:12:50.590 --> 00:12:53.269 important step in the entire process? AI verifies 00:12:53.269 --> 00:12:55.889 data, but only physical testing verifies premium 00:12:55.889 --> 00:12:58.509 quality and customer experience. So what does 00:12:58.509 --> 00:13:01.090 this all mean for you, the listener? Well, AI 00:13:01.090 --> 00:13:03.809 e -commerce is the ultimate shortcut. It replaces 00:13:03.809 --> 00:13:06.649 months of painful guesswork and random spreadsheets 00:13:06.649 --> 00:13:09.529 with real -time data and strategic intent. It 00:13:09.529 --> 00:13:12.090 allows small focused brands to win by identifying 00:13:12.090 --> 00:13:14.610 and solving problems the big players are simply 00:13:14.610 --> 00:13:18.429 too slow or frankly too arrogant to notice. And 00:13:18.429 --> 00:13:20.970 success in this new environment is no longer 00:13:20.970 --> 00:13:23.990 about being some kind of coding genius or a marketing 00:13:23.990 --> 00:13:27.049 prodigy. It's about knowing how to ask the right 00:13:27.049 --> 00:13:30.590 highly specific questions to the AI. The process 00:13:30.590 --> 00:13:33.659 is structured. discovery, engineering the improved 00:13:33.659 --> 00:13:37.539 V2 product, professional sourcing, and then strategic 00:13:37.539 --> 00:13:39.860 market entry. The only thing left to do is to 00:13:39.860 --> 00:13:41.580 start asking those questions. So think about 00:13:41.580 --> 00:13:44.039 this. What is the single biggest, I wish there 00:13:44.039 --> 00:13:45.980 was a complaint you have heard recently, maybe 00:13:45.980 --> 00:13:48.980 from a friend or a colleague, that AI could immediately 00:13:48.980 --> 00:13:51.960 turn into your next million dollar idea? Something 00:13:51.960 --> 00:13:54.039 for you to mull over. We'll be mulling over that 00:13:54.039 --> 00:13:55.799 one. Thank you for joining us for the Deep Dive. 00:13:55.879 --> 00:13:56.620 Until next time.