WEBVTT 00:00:00.000 --> 00:00:02.980 It is strange that asking an AI for a simple 00:00:02.980 --> 00:00:05.219 diet plan might soon be illegal in New York. 00:00:05.459 --> 00:00:08.320 Yet that exact same technology is actively saving 00:00:08.320 --> 00:00:11.359 lives in southern Africa. It does this by reading 00:00:11.359 --> 00:00:14.439 old newspaper clippings. It is a strange paradox. 00:00:14.699 --> 00:00:16.800 Oh, right. And it gets weird fast. The landscape 00:00:16.800 --> 00:00:19.120 is shifting constantly. You know, we're watching 00:00:19.120 --> 00:00:21.660 society scramble to keep up. Welcome to this 00:00:21.660 --> 00:00:24.480 deep dive. We are exploring the friction of integrating 00:00:24.480 --> 00:00:27.820 AI into society today. First, we will examine 00:00:27.820 --> 00:00:30.899 a new New York bill. It tries to ban AI from 00:00:30.899 --> 00:00:33.750 acting like a licensed professional. Then we 00:00:33.750 --> 00:00:36.850 look at the Wild West of everyday AI tools. We 00:00:36.850 --> 00:00:38.909 will uncover some unexpected economic ripple 00:00:38.909 --> 00:00:41.390 effects there. Finally, we explore a brilliant 00:00:41.390 --> 00:00:43.890 pivot by Google. They're predicting flash floods 00:00:43.890 --> 00:00:46.409 using written text. It is a massive spectrum 00:00:46.409 --> 00:00:49.030 of human adaptation. We're watching society try 00:00:49.030 --> 00:00:51.030 to build guardrails. At the same time, the technology 00:00:51.030 --> 00:00:53.329 is sprinting right past them. Let us start with 00:00:53.329 --> 00:00:55.170 what is happening in New York. State lawmakers 00:00:55.170 --> 00:00:57.609 are advancing a very specific, aggressive bill. 00:00:57.729 --> 00:01:00.729 It targets AI tools acting like licensed professionals. 00:01:01.030 --> 00:01:04.090 The scope here is incredibly broad. I mean, we're 00:01:04.090 --> 00:01:06.969 talking about 14 different licensed professions. 00:01:07.390 --> 00:01:10.969 This includes medicine, law, psychology, and 00:01:10.969 --> 00:01:14.129 engineering. It even covers dentistry and social 00:01:14.129 --> 00:01:17.450 work. The core of the bill is a strict ban. It 00:01:17.450 --> 00:01:19.510 would make it illegal for software to give a 00:01:19.510 --> 00:01:22.530 substantive response in those areas. That applies 00:01:22.530 --> 00:01:24.709 to anything normally requiring a state license. 00:01:24.989 --> 00:01:27.549 This is a radical departure from normal tech 00:01:27.549 --> 00:01:30.409 regulation. Usually, tech regulation relies on 00:01:30.409 --> 00:01:33.170 government fines. An agency investigates an issue, 00:01:33.290 --> 00:01:36.290 then issues a penalty. This bill is completely 00:01:36.290 --> 00:01:39.469 different. It includes a private right of action. 00:01:39.709 --> 00:01:42.290 Let us unpack the mechanics of that. A private 00:01:42.290 --> 00:01:45.150 right of action. That means users can sue the 00:01:45.150 --> 00:01:47.269 chatbot providers directly. Exactly. You don't 00:01:47.269 --> 00:01:48.989 have to wait for a government regulator at all. 00:01:49.049 --> 00:01:51.290 Any regular citizen can file a civil lawsuit. 00:01:51.569 --> 00:01:53.969 If they believe the AI gave professional advice, 00:01:54.250 --> 00:01:57.069 they can sue. And companies can't just hide behind 00:01:57.069 --> 00:01:59.030 their terms of service. The sources state that 00:01:59.030 --> 00:02:01.129 adding warnings will not protect the developers. 00:02:01.549 --> 00:02:04.390 Legal disclaimers are completely useless under 00:02:04.390 --> 00:02:07.010 this proposed law. Yeah, users can still sue 00:02:07.010 --> 00:02:09.750 the providers directly. And if it passes, this 00:02:09.750 --> 00:02:12.990 law moves incredibly fast. It takes effect roughly 00:02:12.990 --> 00:02:15.229 three months after the governor signs it. That 00:02:15.229 --> 00:02:17.550 is practically overnight in the tech world, giving 00:02:17.550 --> 00:02:19.889 companies three months to audit entire language 00:02:19.889 --> 00:02:21.849 models. That seems practically impossible to 00:02:21.849 --> 00:02:24.629 execute. It probably is impossible. And here 00:02:24.629 --> 00:02:27.610 is the real underlying issue. The bill does not 00:02:27.610 --> 00:02:30.009 clearly define what counts as a substantive response. 00:02:30.389 --> 00:02:34.189 That is the ultimate danger zone. Beat. It is 00:02:34.189 --> 00:02:36.669 kind of like outlawing medical textbooks just 00:02:36.669 --> 00:02:39.409 because they contain medical facts and then letting 00:02:39.409 --> 00:02:41.969 the readers sue the printing press. That is a 00:02:41.969 --> 00:02:44.789 perfect analogy. A medical textbook is full of 00:02:44.789 --> 00:02:48.139 substantive medical facts. But we don't sue publishers 00:02:48.139 --> 00:02:51.080 for practicing unlicensed medicine. The New York 00:02:51.080 --> 00:02:53.419 bill essentially says that if an AI reads that 00:02:53.419 --> 00:02:55.819 textbook and then summarizes a paragraph for 00:02:55.819 --> 00:02:58.919 you, the developer is suddenly liable. It creates 00:02:58.919 --> 00:03:01.520 a massive gray area. This ambiguity could make 00:03:01.520 --> 00:03:03.819 harmless educational responses legally risky. 00:03:04.250 --> 00:03:06.009 Think about the chilling effect this creates. 00:03:06.710 --> 00:03:09.050 Developers might just block any query related 00:03:09.050 --> 00:03:12.289 to those 14 professions. They will heavily over 00:03:12.289 --> 00:03:15.009 censor the models just to avoid frivolous lawsuits. 00:03:15.430 --> 00:03:17.810 Let me ask you this. If a student asks an AI 00:03:17.810 --> 00:03:21.069 to explain a legal concept for school, does the 00:03:21.069 --> 00:03:24.090 developer get sued? The sheer ambiguity of the 00:03:24.090 --> 00:03:27.729 word substantive creates that exact risk. A thorough, 00:03:27.889 --> 00:03:30.650 helpful explanation could easily be misconstrued 00:03:30.650 --> 00:03:33.189 as legal advice. A jury would have to decide. 00:03:33.659 --> 00:03:35.960 So basic education becomes a legal minefield 00:03:35.960 --> 00:03:38.030 for developers. Yes. They will have to choose 00:03:38.030 --> 00:03:40.590 between educational utility and legal safety. 00:03:40.810 --> 00:03:43.129 In a corporate environment, safety will almost 00:03:43.129 --> 00:03:45.569 always win. While regulators try to draw neat 00:03:45.569 --> 00:03:48.370 lines, the actual technology is spilling into 00:03:48.370 --> 00:03:50.289 everything. It's creating totally unexpected 00:03:50.289 --> 00:03:52.889 economic loops. It really is a messy reality. 00:03:53.129 --> 00:03:55.270 Let's look at the strange economics of coding 00:03:55.270 --> 00:03:58.430 right now. AI makes writing code much faster 00:03:58.430 --> 00:04:00.710 and cheaper, but hiring for software engineers 00:04:00.710 --> 00:04:03.689 is actually increasing. That seems entirely counterintuitive. 00:04:03.849 --> 00:04:06.389 If the machine writes the code, why hire more 00:04:06.389 --> 00:04:08.189 humans? Human engineers. Because companies now 00:04:08.189 --> 00:04:10.370 realize they want to build more software, the 00:04:10.370 --> 00:04:13.030 overall demand for software is practically infinite. 00:04:13.349 --> 00:04:16.089 We are seeing a classic economic principle playing 00:04:16.089 --> 00:04:18.470 out here. You mean the Jevons paradox? Let's 00:04:18.470 --> 00:04:21.149 define that. It means efficiency makes a resource 00:04:21.149 --> 00:04:24.319 cheaper, so people use more of it. Yes, exactly. 00:04:24.560 --> 00:04:28.639 Lowering the cost of code just unlocks new, highly 00:04:28.639 --> 00:04:31.459 ambitious projects. We aren't replacing the engineers. 00:04:31.680 --> 00:04:35.000 We are massively scaling their output. And the 00:04:35.000 --> 00:04:37.879 tools those engineers use are evolving rapidly. 00:04:38.509 --> 00:04:40.810 We're moving away from simple chatbots toward 00:04:40.810 --> 00:04:43.470 autonomous systems. I still wrestle with prompt 00:04:43.470 --> 00:04:46.730 drift myself. Oh, we all do. You ask an AI to 00:04:46.730 --> 00:04:49.189 draft a simple email. By the third revision, 00:04:49.350 --> 00:04:51.790 it is somehow talking like a 19th century poet. 00:04:51.970 --> 00:04:54.829 Keeping it on track is exhausting. That is exactly 00:04:54.829 --> 00:04:57.269 why the industry is moving toward AI agents. 00:04:57.470 --> 00:04:59.370 To clarify what that means, it is software that 00:04:59.370 --> 00:05:00.930 runs in the background and takes actions for 00:05:00.930 --> 00:05:03.500 you. Perfect definition. Look at what Perplexity 00:05:03.500 --> 00:05:05.100 is doing right now. They just launched something 00:05:05.100 --> 00:05:07.540 called Personal Computer. It is an always -on 00:05:07.540 --> 00:05:10.620 AI agent running on a Mac Mini. Hold on. Giving 00:05:10.620 --> 00:05:13.680 an AI constant access to my screen and files? 00:05:14.519 --> 00:05:17.019 That sounds like a privacy nightmare waiting 00:05:17.019 --> 00:05:19.319 to happen. Why would anyone opt into that? It 00:05:19.319 --> 00:05:21.850 is definitely a privacy trade -off. But people 00:05:21.850 --> 00:05:24.970 opt in because it delegates tedious digital chores. 00:05:25.250 --> 00:05:27.529 It can control your files, your apps, and your 00:05:27.529 --> 00:05:29.810 web sessions. It is fundamentally different from 00:05:29.810 --> 00:05:32.129 a search engine. It operates your machine for 00:05:32.129 --> 00:05:35.290 you. Google is taking a similar interactive approach, 00:05:35.389 --> 00:05:37.730 right? They are quietly updating Google Maps. 00:05:38.009 --> 00:05:40.509 Instead of typing a search, you can now talk 00:05:40.509 --> 00:05:42.769 to it. Yeah, they added a new Ask Maps feature. 00:05:43.230 --> 00:05:45.589 You can ask complex, highly contextual questions 00:05:45.589 --> 00:05:47.810 naturally. For example, you can ask where to 00:05:47.810 --> 00:05:50.230 chart a phone nearby. It understands the context 00:05:50.230 --> 00:05:53.009 and finds a location. It is rolling out to users 00:05:53.009 --> 00:05:55.470 right now. It isn't just about productivity and 00:05:55.470 --> 00:05:57.509 mapping, though. The actual personalities of 00:05:57.509 --> 00:05:59.509 these models are shifting dramatically. This 00:05:59.509 --> 00:06:01.990 is where things get truly weird. Amazon just 00:06:01.990 --> 00:06:04.730 added a sassy personality to their new Alexa 00:06:04.730 --> 00:06:08.220 Plus. Sassy. For a household smart speaker? Yes. 00:06:08.379 --> 00:06:10.639 It is an adults -only mode for the assistant. 00:06:10.800 --> 00:06:13.600 It can actually curse at you or roast your questions. 00:06:13.920 --> 00:06:16.759 That is hilarious. But why would engineers intentionally 00:06:16.759 --> 00:06:19.439 build that? Well, it comes down to human psychology. 00:06:19.660 --> 00:06:22.439 People form parasocial relationships with AI. 00:06:22.800 --> 00:06:25.439 Perfect, subservient software is actually pretty 00:06:25.439 --> 00:06:28.360 boring. It feels robotic. But when a piece of 00:06:28.360 --> 00:06:31.699 software roasts your music taste, it mimics human 00:06:31.699 --> 00:06:35.079 friction. It feels more real. That makes a lot 00:06:35.079 --> 00:06:37.439 of sense. We actually prefer technology that 00:06:37.439 --> 00:06:40.319 mimics our own flaws. Exactly. Though it still 00:06:40.319 --> 00:06:43.939 blocks actual NSFW content, of course. You even 00:06:43.939 --> 00:06:46.259 have to pass extra security checks just to enable 00:06:46.259 --> 00:06:48.420 the sassy mode. Meanwhile, other tech giants 00:06:48.420 --> 00:06:50.939 are stumbling behind the scenes. Meta has a secret 00:06:50.939 --> 00:06:53.879 AI model called Avocado. Right. They just had 00:06:53.879 --> 00:06:56.579 to delay Avocado. They reportedly had some very 00:06:56.579 --> 00:06:58.980 disappointing internal tests. The official launch 00:06:58.980 --> 00:07:01.629 is pushed back to May. And the industry rumors 00:07:01.629 --> 00:07:04.290 surrounding this is wild. Meta might actually 00:07:04.290 --> 00:07:06.149 consider using Google Gemini in the meantime. 00:07:06.430 --> 00:07:08.709 That would be a massive shift in the AI arms 00:07:08.709 --> 00:07:11.709 race. Meta using a Google model would be a huge 00:07:11.709 --> 00:07:14.089 concession. It shows how hard it is to build 00:07:14.089 --> 00:07:16.550 foundational intelligence from scratch. There 00:07:16.550 --> 00:07:20.050 is also massive money flowing into AI video generation 00:07:20.050 --> 00:07:23.129 right now. Pixverse just secured $300 million 00:07:23.129 --> 00:07:25.850 in funding. And that funding is heavily backed 00:07:25.850 --> 00:07:28.550 by Alibaba. It reflects immense growing confidence 00:07:28.550 --> 00:07:31.750 in Pixverse specifically. The AI video sector 00:07:31.750 --> 00:07:34.269 is just exploding right now. Speaking of AI video, 00:07:34.529 --> 00:07:36.910 it is already entering the geopolitical arena. 00:07:37.170 --> 00:07:39.870 We need to look at this very objectively. A Chinese 00:07:39.870 --> 00:07:42.990 embassy recently posted an AI -generated video 00:07:42.990 --> 00:07:46.410 online. It was directly mocking a U .S. policy 00:07:46.410 --> 00:07:48.970 proposal. They were mocking Trump's Shield of 00:07:48.970 --> 00:07:51.589 the Americas concept. The video showed a U .S. 00:07:51.610 --> 00:07:55.319 eagle promising security. Then... The eagle aggressively 00:07:55.319 --> 00:07:58.339 locks the region inside a cage. Now, we must 00:07:58.339 --> 00:08:00.920 be perfectly clear here. Right. We are not endorsing 00:08:00.920 --> 00:08:03.300 any political viewpoint left or right. We are 00:08:03.300 --> 00:08:05.699 merely analyzing this as an objective example 00:08:05.699 --> 00:08:08.279 of the technology. Exactly. We are looking at 00:08:08.279 --> 00:08:11.139 how AI video tools are being deployed. They are 00:08:11.139 --> 00:08:13.819 now actively used in global geopolitical messaging. 00:08:14.170 --> 00:08:16.670 It drastically lowers the barrier for high -quality 00:08:16.670 --> 00:08:19.250 propaganda. Anyone can generate a compelling 00:08:19.250 --> 00:08:21.430 metaphorical video in minutes. You don't need 00:08:21.430 --> 00:08:23.329 an animation studio anymore. You just need a 00:08:23.329 --> 00:08:25.449 prompt. This brings me back to the economics 00:08:25.449 --> 00:08:29.430 of it all. Will always -on AI agents eventually 00:08:29.430 --> 00:08:33.409 break the Jevons paradox loop by completely replacing 00:08:33.409 --> 00:08:36.549 the need for human developers to build that extra 00:08:36.549 --> 00:08:39.850 software? Not anytime soon. Right now, it just 00:08:39.850 --> 00:08:42.210 means humans manage the agents to build even 00:08:42.210 --> 00:08:45.350 more complex systems. We become directors, not 00:08:45.350 --> 00:08:47.669 just typists. Lowering the barrier just raises 00:08:47.669 --> 00:08:49.909 the ceiling for what we build. Precisely. We 00:08:49.909 --> 00:08:51.750 just keep building taller and taller digital 00:08:51.750 --> 00:08:54.190 structures. We will continue our deep dive in 00:08:54.190 --> 00:08:56.330 just a moment. Stick around. Mid -roll sponsor 00:08:56.330 --> 00:09:00.269 break. And we are back. It is easy to get distracted 00:09:00.269 --> 00:09:02.629 by sassy smart speakers, but there is a much 00:09:02.629 --> 00:09:05.429 quieter AI breakthrough happening that is literally 00:09:05.429 --> 00:09:08.259 saving lives. We need to talk about the reality 00:09:08.259 --> 00:09:11.080 of flash floods. They are incredibly destructive 00:09:11.080 --> 00:09:13.779 natural disasters. They kill over 5 ,000 people 00:09:13.779 --> 00:09:16.000 every single year. Flash floods are incredibly 00:09:16.000 --> 00:09:18.320 destructive, mostly because they're notoriously 00:09:18.320 --> 00:09:21.279 hard to predict. They happen extremely fast and 00:09:21.279 --> 00:09:23.360 are hyper -localized to specific neighborhoods. 00:09:24.029 --> 00:09:26.970 Usually predicting them requires expensive physical 00:09:26.970 --> 00:09:29.990 infrastructure. You need physical river gauges 00:09:29.990 --> 00:09:32.750 installed. You need advanced local radar systems 00:09:32.750 --> 00:09:36.049 scanning the skies. Many countries simply do 00:09:36.049 --> 00:09:38.509 not have that kind of infrastructure. Building 00:09:38.509 --> 00:09:41.269 a national radar network costs billions of dollars. 00:09:41.570 --> 00:09:44.370 So Google came up with a genuinely brilliant 00:09:44.370 --> 00:09:47.990 alternative. They decided to teach an AI to read 00:09:47.990 --> 00:09:51.710 the news. They analyzed five million news articles 00:09:51.710 --> 00:09:54.320 from around the world. That is a staggering amount 00:09:54.320 --> 00:09:57.059 of unstructured text. They used advanced natural 00:09:57.059 --> 00:09:59.799 language processing. The AI wasn't just searching 00:09:59.799 --> 00:10:02.279 for the word flood. It had to understand the 00:10:02.279 --> 00:10:04.360 linguistic context. Right. It has to distinguish 00:10:04.360 --> 00:10:07.480 between a literal disaster and a metaphor. A 00:10:07.480 --> 00:10:10.000 flood of tears or a flood of emails isn't a weather 00:10:10.000 --> 00:10:12.580 event. Exactly. It filtered out the metaphors 00:10:12.580 --> 00:10:14.960 to extract reports of actual flood events from 00:10:14.960 --> 00:10:16.860 those historical news stories. They essentially 00:10:16.860 --> 00:10:19.179 turned historical journalism into historical 00:10:19.179 --> 00:10:21.480 weather data. They did. From this text, they 00:10:21.480 --> 00:10:23.679 built a brand new data set. They call it the 00:10:23.679 --> 00:10:25.700 ground source data set. Ground source contains 00:10:25.700 --> 00:10:29.419 2 .6 million individual flood reports. All of 00:10:29.419 --> 00:10:31.299 them were identified purely from written news 00:10:31.299 --> 00:10:34.620 coverage. They meticulously geotagged the locations 00:10:34.620 --> 00:10:37.919 mentioned in the text. They accurately timestamped 00:10:37.919 --> 00:10:40.279 the flood events based on the publication dates. 00:10:40.500 --> 00:10:44.240 Two sec silence. Oh. Imagine scaling millions 00:10:44.240 --> 00:10:46.879 of old news clippings into a global radar. It 00:10:46.879 --> 00:10:49.399 is a profound shift in scientific thinking. It 00:10:49.399 --> 00:10:51.879 is a historical weather data set created from 00:10:51.879 --> 00:10:53.740 written reports instead of physical sensors. 00:10:54.100 --> 00:10:57.000 They used this ground source data set to train 00:10:57.000 --> 00:10:59.879 a predictive forecasting model. Yes. They've 00:10:59.879 --> 00:11:02.720 fed this massive historical pattern into an AI. 00:11:02.980 --> 00:11:06.220 By understanding exactly where and when it flooded 00:11:06.220 --> 00:11:09.379 before, the AI learns the geographic vulnerabilities. 00:11:09.779 --> 00:11:12.519 Now, the system is integrated into Google Flood 00:11:12.519 --> 00:11:15.250 Hub. It identifies flood risks in urban areas 00:11:15.250 --> 00:11:18.450 across 150 countries. It shares these alerts 00:11:18.450 --> 00:11:20.850 directly with emergency response agencies. And 00:11:20.850 --> 00:11:22.769 it is already working in the real world. One 00:11:22.769 --> 00:11:24.970 emergency response official in southern Africa 00:11:24.970 --> 00:11:27.809 recently spoke about the trials. They said the 00:11:27.809 --> 00:11:29.970 system actually helped them respond faster to 00:11:29.970 --> 00:11:32.929 incoming flood events. Real human lives are being 00:11:32.929 --> 00:11:35.450 positively impacted by this. It is important 00:11:35.450 --> 00:11:37.730 to acknowledge the scientific limitations, though. 00:11:38.299 --> 00:11:40.799 The predictions currently cover areas of about 00:11:40.799 --> 00:11:43.860 20 square kilometers. That is fairly broad. It 00:11:43.860 --> 00:11:46.139 is not exactly block by block precision. Right. 00:11:46.240 --> 00:11:49.519 It is not as precise as systems like the US National 00:11:49.519 --> 00:11:52.940 Weather Service. Those systems use high fidelity 00:11:52.940 --> 00:11:56.919 local radar data for pinpoint accuracy. But this 00:11:56.919 --> 00:11:59.620 project wasn't built to replace local radar systems. 00:12:00.000 --> 00:12:03.419 It was designed specifically for regions entirely 00:12:03.419 --> 00:12:06.519 lacking. advanced weather infrastructure. It 00:12:06.519 --> 00:12:08.159 is for countries where traditional forecasting 00:12:08.159 --> 00:12:10.759 tools are completely unavailable. It provides 00:12:10.759 --> 00:12:13.240 a crucial baseline of safety where there was 00:12:13.240 --> 00:12:16.179 previously none. Is this text -to -sensor methodology 00:12:16.179 --> 00:12:19.519 the missing link for developing nations that 00:12:19.519 --> 00:12:21.460 can't afford billion -dollar weather arrays? 00:12:21.759 --> 00:12:25.179 Absolutely. It shows how historical text leapfrogs 00:12:25.179 --> 00:12:27.399 the need for expensive physical infrastructure. 00:12:27.600 --> 00:12:30.019 You leverage the conversational data that already 00:12:30.019 --> 00:12:32.840 exists in the world. So text data acts as virtual 00:12:32.840 --> 00:12:35.080 weather sensors for the past. Yes. And by deeply 00:12:35.080 --> 00:12:37.159 understanding the past, the AI can predict the 00:12:37.159 --> 00:12:39.940 future. It is a brilliant, life -saving substitution 00:12:39.940 --> 00:12:42.659 of resources. We have covered a lot of fascinating 00:12:42.659 --> 00:12:45.250 ground today. We are currently stuck in a very 00:12:45.250 --> 00:12:47.929 messy technological transition. We really are. 00:12:48.070 --> 00:12:50.610 We are watching society wrestle with a fundamental 00:12:50.610 --> 00:12:54.090 shift in capability. On one end, we have blunt 00:12:54.090 --> 00:12:56.850 legal instruments. Look at the proposed New York 00:12:56.850 --> 00:13:00.610 bill. It is trying to shove AI back into a neat 00:13:00.610 --> 00:13:03.649 professional box. It is trying to apply old liability 00:13:03.649 --> 00:13:06.659 frameworks to a totally new paradigm. By doing 00:13:06.659 --> 00:13:09.259 so, it will probably stifle basic education in 00:13:09.259 --> 00:13:11.440 the process. On the other end, we have the messy 00:13:11.440 --> 00:13:14.179 reality of the Wild West. The Jevons paradox 00:13:14.179 --> 00:13:16.779 is driving software demand through the roof. 00:13:16.940 --> 00:13:19.220 We have sassy Amazon assistants intentionally 00:13:19.220 --> 00:13:22.659 roasting us. We have geopolitical AI memes rapidly 00:13:22.659 --> 00:13:25.299 reshaping digital diplomacy. The friction of 00:13:25.299 --> 00:13:27.259 integration is everywhere. But beneath all that 00:13:27.259 --> 00:13:29.539 friction, the technology is performing quiet 00:13:29.539 --> 00:13:32.480 miracles. Look at the success of Google Flood 00:13:32.480 --> 00:13:35.429 Hub. turning 5 million old news articles into 00:13:35.429 --> 00:13:37.789 a global flood warning system. It proves that 00:13:37.789 --> 00:13:40.250 the most valuable application of AI is finding 00:13:40.250 --> 00:13:42.789 patterns in the noise. That is exactly why this 00:13:42.789 --> 00:13:44.690 matters to you, the listener. The fundamental 00:13:44.690 --> 00:13:46.870 definition of knowledge is changing rapidly. 00:13:47.149 --> 00:13:50.269 It used to be entirely about what you know. Rope 00:13:50.269 --> 00:13:53.450 memorization. Stirring facts in your head. Now. 00:13:53.789 --> 00:13:55.809 Knowledge is about how you connect the dots. 00:13:56.070 --> 00:13:59.110 The raw facts are universally available to everyone. 00:13:59.370 --> 00:14:02.789 The synthesis is what actually matters. AI is 00:14:02.789 --> 00:14:05.049 the ultimate synthesis engine. It can read five 00:14:05.049 --> 00:14:07.309 million articles in an afternoon. You cannot. 00:14:08.000 --> 00:14:10.200 But you can direct the engine. You can ask the 00:14:10.200 --> 00:14:12.220 right questions. You can manage the autonomous 00:14:12.220 --> 00:14:15.019 agents. You can navigate the ethical and legal 00:14:15.019 --> 00:14:18.000 gray areas. The New York lawmakers are stubbornly 00:14:18.000 --> 00:14:20.659 focused on what the AI knows. They are missing 00:14:20.659 --> 00:14:22.659 the bigger picture of how the AI synthesizes 00:14:22.659 --> 00:14:24.620 information. Which leaves us with a fascinating 00:14:24.620 --> 00:14:26.840 prospect for the future. We're just scratching 00:14:26.840 --> 00:14:29.460 the surface of using text as a sensor. The ground 00:14:29.460 --> 00:14:31.799 source data set is just one single application. 00:14:32.200 --> 00:14:34.860 Flood prediction is just one narrow domain of 00:14:34.860 --> 00:14:37.080 human experience. Think about the sheer volume 00:14:37.080 --> 00:14:40.259 of written history we possess. Centuries of local 00:14:40.259 --> 00:14:42.799 newspapers, medical journals, and shipping logs. 00:14:43.139 --> 00:14:45.740 It is an endless ocean of unstructured human 00:14:45.740 --> 00:14:48.379 data. We have never had the tools to process 00:14:48.379 --> 00:14:50.940 it comprehensively before now. If Google can 00:14:50.940 --> 00:14:53.899 reconstruct historical weather patterns, Just 00:14:53.899 --> 00:14:57.120 by having an AI read the news, what other invisible 00:14:57.120 --> 00:15:00.059 patterns exist in the text? Humanity is already 00:15:00.059 --> 00:15:02.740 written, just waiting for an AI to connect the 00:15:02.740 --> 00:15:05.840 dots. Could we predict economic crashes or disease 00:15:05.840 --> 00:15:08.179 outbreaks purely from historical literature? 00:15:08.419 --> 00:15:10.480 It is something to think about. Thank you for 00:15:10.480 --> 00:15:11.539 joining us on this deep dive.