WEBVTT 00:00:00.000 --> 00:00:02.319 Imagine a single company getting $10 billion 00:00:02.319 --> 00:00:07.000 for AI. Just boom, $10 billion. Or another one 00:00:07.000 --> 00:00:09.419 raising a billion dollars simply for labeling 00:00:09.419 --> 00:00:11.960 data. The scale of money flowing into artificial 00:00:11.960 --> 00:00:15.539 intelligence right now is, ah, it's really something 00:00:15.539 --> 00:00:19.280 else. Mind -boggling. Welcome to the Deep Dive. 00:00:19.339 --> 00:00:21.579 We're here to unpack complex information, break 00:00:21.579 --> 00:00:23.760 it down into clear, actionable insights for you. 00:00:24.170 --> 00:00:25.789 Today, we're going to take a thoughtful look 00:00:25.789 --> 00:00:28.609 into the recent AI investment scene. We're using 00:00:28.609 --> 00:00:30.910 a really interesting report covering late June 00:00:30.910 --> 00:00:34.229 and early July 2025. We've got the scoop on the 00:00:34.229 --> 00:00:36.570 top 20 deals, the ones really shaping what AI 00:00:36.570 --> 00:00:38.490 looks like next. Yeah, we'll dig into where all 00:00:38.490 --> 00:00:40.270 this cash is actually going, you know, from the 00:00:40.270 --> 00:00:42.810 big foundational models and the super advanced 00:00:42.810 --> 00:00:45.750 AI chips, all the way to really specific uses 00:00:45.750 --> 00:00:48.070 in healthcare, finance, even farming. Our goal 00:00:48.070 --> 00:00:50.409 for you today, cut through the noise, highlight 00:00:50.409 --> 00:00:52.670 some maybe surprising trends, and connect the 00:00:52.670 --> 00:00:55.399 dots. What do these huge investments really mean 00:00:55.399 --> 00:00:58.520 for what's coming? Okay, let's get into it. Right, 00:00:58.579 --> 00:01:00.659 leading the pack, we see these just massive figures 00:01:00.659 --> 00:01:03.420 going into what you might call the core AI infrastructure. 00:01:03.520 --> 00:01:08.120 First off, that colossal $10 billion round for 00:01:08.120 --> 00:01:12.040 XAI, July 1st, 2025. This isn't just big money. 00:01:12.180 --> 00:01:15.980 It instantly makes XAI one of the most heavily 00:01:15.980 --> 00:01:19.400 funded AI labs anywhere. Period. It's almost 00:01:19.400 --> 00:01:21.319 hard to comprehend that number. It really is. 00:01:21.439 --> 00:01:24.000 And XAI, as you probably know, they're focused 00:01:24.000 --> 00:01:27.040 hard on developing advanced AI systems, platforms. 00:01:27.579 --> 00:01:30.219 Grok Chatbot is one piece. But they're aiming 00:01:30.219 --> 00:01:32.359 way beyond just chatbots. They've got these really 00:01:32.359 --> 00:01:34.280 ambitious goals in scientific discovery. They 00:01:34.280 --> 00:01:36.540 want to use their AI to tackle some really tough, 00:01:36.719 --> 00:01:39.480 longstanding problems in science and tech. So 00:01:39.480 --> 00:01:42.159 this $10 billion war chest, as they call it, 00:01:42.180 --> 00:01:44.799 it's fuel. For intense research, getting tons 00:01:44.799 --> 00:01:46.900 of data and securing the massive compute power 00:01:46.900 --> 00:01:49.659 needed for that. cutting edge AI. It's a serious 00:01:49.659 --> 00:01:51.599 long haul bet on pushing the limits. That scale. 00:01:51.879 --> 00:01:53.780 Yeah, it's incredible. And it really highlights 00:01:53.780 --> 00:01:55.599 something fundamental, doesn't it? Building these 00:01:55.599 --> 00:01:58.500 next gen AI systems. It takes an unprecedented 00:01:58.500 --> 00:02:00.900 amount of money now. It's way beyond just coding. 00:02:00.980 --> 00:02:03.299 It's industrial scale research, talent infrastructure. 00:02:03.799 --> 00:02:06.519 Exactly. And then look at Search AI. They pulled 00:02:06.519 --> 00:02:10.340 in a full billion dollars just for AI data labeling. 00:02:10.699 --> 00:02:14.810 July 1st to 3rd. A billion. For data, that just 00:02:14.810 --> 00:02:16.789 screams how critical data is, right? It's the 00:02:16.789 --> 00:02:19.849 hidden fuel for this whole revolution. You absolutely 00:02:19.849 --> 00:02:22.310 need high quality training data. Without it, 00:02:22.349 --> 00:02:24.449 even the smartest algorithms kind of fall flat. 00:02:24.610 --> 00:02:27.069 A billion dollars for what some people think 00:02:27.069 --> 00:02:29.569 of as just back end work. It's staggering. What's 00:02:29.569 --> 00:02:31.509 really interesting with Suji AI is they specialize 00:02:31.509 --> 00:02:35.020 in human in the loop labeling. Pause. For anyone 00:02:35.020 --> 00:02:36.979 less familiar, that basically means they use 00:02:36.979 --> 00:02:39.379 smart software plus skilled human annotators. 00:02:39.520 --> 00:02:41.840 Together, they prep and refine the huge data 00:02:41.840 --> 00:02:44.240 sets AI models learn from. They do computer vision, 00:02:44.319 --> 00:02:47.039 helping AIC, natural language processing, NLP, 00:02:47.120 --> 00:02:49.039 understanding language, speech recognition, too. 00:02:49.139 --> 00:02:51.240 So there's huge funding. It lets them massively 00:02:51.240 --> 00:02:53.199 scale up that essential groundwork, make sure 00:02:53.199 --> 00:02:55.759 AI builders everywhere get the really good, accurately 00:02:55.759 --> 00:02:58.639 labeled data they need. It's like prepping ingredients 00:02:58.639 --> 00:03:01.219 perfectly before you cook a fancy meal. You need 00:03:01.219 --> 00:03:03.740 the good stuff. And speaking of foundational 00:03:03.740 --> 00:03:07.500 pieces, Byron Technology. They got $207 million 00:03:07.500 --> 00:03:11.300 for domestic AI chip development. June 26th, 00:03:11.300 --> 00:03:14.300 this global race for powerful, specialized AI 00:03:14.300 --> 00:03:17.199 chips. It's huge. One of the biggest strategic 00:03:17.199 --> 00:03:19.550 tech stories happening right now. Yeah, and this 00:03:19.550 --> 00:03:22.349 investment, especially being in China, it really 00:03:22.349 --> 00:03:25.169 points to a critical national strategy. Byron 00:03:25.169 --> 00:03:27.710 designs high -performance GPUs, graphics processing 00:03:27.710 --> 00:03:30.409 units, and other semiconductor hardware, stuff 00:03:30.409 --> 00:03:32.650 built specifically for the heavy lifting AI requires. 00:03:33.110 --> 00:03:35.770 In a world dominated by just a few big international 00:03:35.770 --> 00:03:38.729 chip companies, this money really boosts Byron's 00:03:38.729 --> 00:03:41.009 efforts to build competitive homegrown hardware. 00:03:41.270 --> 00:03:43.569 It's a key strategic move for China's tech scene, 00:03:43.669 --> 00:03:46.289 about controlling their own critical AI infrastructure, 00:03:46.650 --> 00:03:48.699 tech sovereignty, basically. Whoa, just try to 00:03:48.699 --> 00:03:51.120 imagine scaling up to handle a billion user queries 00:03:51.120 --> 00:03:53.520 or the sheer compute power needed to even start 00:03:53.520 --> 00:03:55.639 using that $10 billion investment. It's an incredible 00:03:55.639 --> 00:03:58.280 scale. So stepping back, what's the most crucial 00:03:58.280 --> 00:04:00.199 takeaway from these massive foundational investments? 00:04:00.419 --> 00:04:02.620 What's the core message? Fundamentally, it shows 00:04:02.620 --> 00:04:05.080 us that enormous capital is now just essential, 00:04:05.280 --> 00:04:08.719 both for pioneering AI research and for building 00:04:08.719 --> 00:04:11.000 the core infrastructure the whole field relies 00:04:11.000 --> 00:04:12.939 on. We're talking about laying completely new 00:04:12.939 --> 00:04:15.259 groundwork here. Right. And if we connect that 00:04:15.259 --> 00:04:18.529 to the bigger picture. Beyond the giants and 00:04:18.529 --> 00:04:21.829 the core tech, AI is embedding itself into daily 00:04:21.829 --> 00:04:24.410 business operations. Yeah. Everywhere. So this 00:04:24.410 --> 00:04:27.009 raises a really important question. How is AI 00:04:27.009 --> 00:04:29.250 fundamentally changing how companies actually 00:04:29.250 --> 00:04:31.829 run, not just the products they make, but their 00:04:31.829 --> 00:04:34.370 internal workings? Yeah, we're seeing major money 00:04:34.370 --> 00:04:36.949 flowing into making businesses smarter, more 00:04:36.949 --> 00:04:38.889 efficient. All over the place. Take Decagon. 00:04:38.949 --> 00:04:41.730 They raise $131 million for conversational AI 00:04:41.730 --> 00:04:44.329 agents June 23rd. And these aren't your basic 00:04:44.329 --> 00:04:46.529 chatbots, you know. They're advanced systems 00:04:46.529 --> 00:04:48.050 for enterprise customer support. They handle 00:04:48.050 --> 00:04:50.310 complex questions, stuff that usually needs a 00:04:50.310 --> 00:04:52.500 human agent. It's about augmenting the humans, 00:04:52.579 --> 00:04:54.399 not just getting rid of calls. Right. Moving 00:04:54.399 --> 00:04:57.699 beyond simple automation to like actual intelligent 00:04:57.699 --> 00:05:00.560 help. Then there's Savvy Wealth, $72 million 00:05:00.560 --> 00:05:04.480 for an AI boosted financial advice platform and 00:05:04.480 --> 00:05:07.060 CRM for advisors. It's built to streamline their 00:05:07.060 --> 00:05:09.800 workflows, deliver insights from data, basically 00:05:09.800 --> 00:05:12.000 empower the human experts with better analysis, 00:05:12.079 --> 00:05:14.579 more personalized strategies. It enhances the 00:05:14.579 --> 00:05:17.060 advisor, doesn't replace them. And LevelPath, 00:05:17.160 --> 00:05:19.699 $55 million. They're building an AI -native procurement 00:05:19.699 --> 00:05:23.220 platform, uses AI agents for automation, helps 00:05:23.220 --> 00:05:25.399 businesses find better deals, manage suppliers, 00:05:25.879 --> 00:05:27.980 streamline buying. Kind of like having an expert 00:05:27.980 --> 00:05:30.680 AI shopper working 24 -7 for your company. Nice. 00:05:30.939 --> 00:05:34.420 Then Campfire raised $35 million for an AI -first 00:05:34.420 --> 00:05:37.540 ERP platform. ERP systems, enterprise resource 00:05:37.540 --> 00:05:39.800 planning. They're usually these big, complex 00:05:39.800 --> 00:05:42.060 beasts. Kind of clunky, honestly. Campfire's 00:05:42.060 --> 00:05:44.240 rethinking that with AI Big Ten. For much smarter 00:05:44.240 --> 00:05:46.089 resource planning, better image. inventory control, 00:05:46.250 --> 00:05:48.209 way more accurate forecasting for the whole company. 00:05:48.290 --> 00:05:50.050 It's like putting predictive intelligence right 00:05:50.050 --> 00:05:52.490 at the heart of operations. Plus, JASPA got $12 00:05:52.490 --> 00:05:55.689 million. AI for email automation, sales engagement, 00:05:56.009 --> 00:05:58.509 helps sales and marketing teams automate outreach, 00:05:58.790 --> 00:06:01.329 personalize messages, even when sending tons. 00:06:01.610 --> 00:06:04.670 Big time saver, making every message feel unique. 00:06:04.810 --> 00:06:07.810 Though honestly, getting AI to nail that personalization 00:06:07.810 --> 00:06:10.009 perfectly can be tricky. I still wrestle with 00:06:10.009 --> 00:06:12.149 prompt drift myself sometimes. You tell it one 00:06:12.149 --> 00:06:14.660 thing and over time it kind of wanders off. You've 00:06:14.660 --> 00:06:16.480 got to keep guiding it. Yeah, totally. That nuances 00:06:16.480 --> 00:06:19.579 everything there. Okay, then CRD secured $15 00:06:19.579 --> 00:06:22.779 million. AI and finance, specifically credit 00:06:22.779 --> 00:06:25.019 scoring, they're using AI to look at alternative 00:06:25.019 --> 00:06:27.779 data stuff beyond the usual credit reports, aiming 00:06:27.779 --> 00:06:30.379 for more accurate, more inclusive credit models. 00:06:30.720 --> 00:06:32.600 Could potentially open up financial services 00:06:32.600 --> 00:06:35.079 to people who were overlooked before. And finally, 00:06:35.160 --> 00:06:38.670 AI, hey. Raised $10 million. An AI knowledge 00:06:38.670 --> 00:06:40.990 platform focused on the Vietnamese market. Acts 00:06:40.990 --> 00:06:43.329 like a central brain for a company. Makes all 00:06:43.329 --> 00:06:45.389 the internal info instantly searchable using 00:06:45.389 --> 00:06:48.209 natural language. Democratizing knowledge, really. 00:06:48.389 --> 00:06:50.850 Okay, so pulling this section together, how does 00:06:50.850 --> 00:06:53.529 AI really elevate these common business functions? 00:06:53.649 --> 00:06:56.209 What's the step change beyond just basic automation? 00:06:56.899 --> 00:06:59.920 Fundamentally, AI enables this new layer of intelligent 00:06:59.920 --> 00:07:02.759 insights, really deep personalization and much 00:07:02.759 --> 00:07:04.839 more optimized decision making pretty much across 00:07:04.839 --> 00:07:07.800 every business process, driving efficiency, giving 00:07:07.800 --> 00:07:10.000 companies an edge. And what's really fascinating, 00:07:10.180 --> 00:07:13.019 I think, is how AI is starting to move beyond 00:07:13.019 --> 00:07:15.860 just screens and data. It's moving into our physical 00:07:15.860 --> 00:07:18.920 reality, enabling machines not just to process 00:07:18.920 --> 00:07:21.720 info, but to understand and act in the physical 00:07:21.720 --> 00:07:23.879 world. It's not just software anymore. It's about 00:07:23.879 --> 00:07:26.730 real world interaction. Exactly. The next wave 00:07:26.730 --> 00:07:29.470 isn't just code. It's tangible, physical. Look 00:07:29.470 --> 00:07:32.769 at Genesis AI raised $105 million. They're working 00:07:32.769 --> 00:07:35.290 on physical AI and robotics foundation models, 00:07:35.529 --> 00:07:38.110 building the core AI brains for general purpose 00:07:38.110 --> 00:07:41.290 robots, automating complex physical work in manufacturing, 00:07:41.470 --> 00:07:43.910 logistics, you name it, enabling robots to do 00:07:43.910 --> 00:07:46.509 more adaptive, nuanced tasks, not just the same 00:07:46.509 --> 00:07:50.029 thing over and over. And PetroApp, they got $50 00:07:50.029 --> 00:07:53.750 million. AI for digital fuel payments and fleet 00:07:53.750 --> 00:07:57.100 management. A key piece there is predictive maintenance. 00:07:57.800 --> 00:08:00.220 Analyzing vehicle data in real time to predict 00:08:00.220 --> 00:08:02.800 breakdowns before they happen. That means huge 00:08:02.800 --> 00:08:05.300 efficiency gains, big cost savings for logistics, 00:08:05.600 --> 00:08:08.519 transport companies, keeps things moving. And 00:08:08.519 --> 00:08:11.720 one I find really interesting, Ambrock. $26 .1 00:08:11.720 --> 00:08:15.000 million for AI in agriculture. Their farm management 00:08:15.000 --> 00:08:17.300 software aims to hyper -optimize crop yields, 00:08:17.540 --> 00:08:20.740 manage water precisely. Apply fertilizer only 00:08:20.740 --> 00:08:23.300 where it's needed, making farming much more precise, 00:08:23.439 --> 00:08:25.699 efficient with resources, more sustainable. That 00:08:25.699 --> 00:08:27.860 directly impacts how we produce food globally. 00:08:28.019 --> 00:08:31.199 So thinking about logistics, agriculture, how 00:08:31.199 --> 00:08:33.419 is AI physically transforming these industries? 00:08:33.580 --> 00:08:36.100 What's the core shift? AI is bringing new levels 00:08:36.100 --> 00:08:39.019 of autonomy, this predictive power and just unprecedented 00:08:39.019 --> 00:08:42.019 efficiency to physical tasks, optimizing how 00:08:42.019 --> 00:08:44.179 resources are used, preventing failures. Which 00:08:44.179 --> 00:08:46.299 then raises another critical question. How is 00:08:46.299 --> 00:08:48.600 AI specifically hitting something as personal, 00:08:48.720 --> 00:08:51.539 as vital as patient care and finding new medicines? 00:08:51.720 --> 00:08:53.279 What's the near future look like there? Yeah. 00:08:53.399 --> 00:08:55.919 Healthcare and biotech are seeing huge AI investments, 00:08:56.320 --> 00:08:58.679 promising breakthroughs in diagnosis, treatment. 00:08:59.360 --> 00:09:00.919 Stuff that seemed like science fiction not long 00:09:00.919 --> 00:09:03.700 ago. Tandem Health, for instance. $50 million. 00:09:04.519 --> 00:09:07.179 Healthcare AI platform focused on patient engagement, 00:09:07.460 --> 00:09:10.200 digital health. The goal is a more connected, 00:09:10.320 --> 00:09:13.500 supportive patient journey. Proactive. Better 00:09:13.500 --> 00:09:16.000 outcomes. Less burden on the system. And Field 00:09:16.000 --> 00:09:20.059 Medical raised $35 million. AI in medical devices 00:09:20.059 --> 00:09:22.740 and diagnostics. This is about putting AI right 00:09:22.740 --> 00:09:25.120 into the devices, making them smarter, allowing 00:09:25.120 --> 00:09:27.379 for faster, more accurate decisions by doctors 00:09:27.379 --> 00:09:29.259 and nurses right there at the point of care. 00:09:29.379 --> 00:09:31.639 Imagine a device giving immediate AI insights 00:09:31.639 --> 00:09:33.980 during surgery, enhancing precision, safety. 00:09:34.259 --> 00:09:37.990 Portal Biotech also got $35 million. AI in drug 00:09:37.990 --> 00:09:40.289 discovery. This is huge. AI can dramatically 00:09:40.289 --> 00:09:42.750 speed up finding new medicines, analyzing massive 00:09:42.750 --> 00:09:45.350 bio and chemical data sets way faster than humans 00:09:45.350 --> 00:09:47.769 ever could, identifying promising drug candidates, 00:09:47.889 --> 00:09:50.289 potential targets. It cuts down the time and, 00:09:50.330 --> 00:09:53.250 crucially, the cost of R &D so much. Then SintesBio, 00:09:53.330 --> 00:09:56.730 $33 million. They're a biotech AI company using 00:09:56.730 --> 00:09:59.710 synthetic biology. Synthetic biology is basically 00:09:59.710 --> 00:10:03.080 redesigning organisms for useful purposes. With 00:10:03.080 --> 00:10:05.860 AI, they can rapidly design new biological systems 00:10:05.860 --> 00:10:08.580 to produce medicines or sustainable chemicals. 00:10:08.740 --> 00:10:11.120 It's almost like programming biology. Really 00:10:11.120 --> 00:10:14.659 cutting -edge stuff. An emerald AI. $24 .5 million. 00:10:14.919 --> 00:10:18.820 AI in medical imaging. Diagnostics. Training 00:10:18.820 --> 00:10:22.860 AI to read complex scans, x -rays, CTs, MRIs 00:10:22.860 --> 00:10:25.419 with incredible accuracy. Sometimes spotting 00:10:25.419 --> 00:10:27.559 subtle signs of disease a human eye might miss. 00:10:27.779 --> 00:10:30.779 Early, accurate detection via AI. That can be 00:10:30.779 --> 00:10:33.000 life -changing. Lastly, Gallant Therapeutics 00:10:33.000 --> 00:10:35.899 got $18 million. Another biotech AI firm developing 00:10:35.899 --> 00:10:38.419 new therapies. They use machine learning, computational 00:10:38.419 --> 00:10:40.799 models to speed up the whole drug development 00:10:40.799 --> 00:10:43.340 pipeline. From finding disease targets to predicting 00:10:43.340 --> 00:10:45.840 how a drug might behave in the body, de -risking 00:10:45.840 --> 00:10:47.529 and accelerating. the path to new treatments. 00:10:47.710 --> 00:10:49.669 Okay, so given all that activity, what's the 00:10:49.669 --> 00:10:52.090 most exciting, most immediate potential for AI 00:10:52.090 --> 00:10:54.009 in healthcare? I'd say it's about much faster 00:10:54.009 --> 00:10:55.970 diagnosis, really accelerating new drug discovery, 00:10:56.110 --> 00:10:58.610 and delivering truly personalized patient support, 00:10:58.809 --> 00:11:01.090 reshaping the whole experience. Okay, finally, 00:11:01.190 --> 00:11:04.730 to really grasp where this is all heading, the 00:11:04.730 --> 00:11:07.919 long -term belief in this innovation. We need 00:11:07.919 --> 00:11:10.340 to look at who's funding it. Even the structure 00:11:10.340 --> 00:11:13.720 of venture capital itself is adapting, specializing 00:11:13.720 --> 00:11:16.059 for this AI boom. Yeah, we're actually seeing 00:11:16.059 --> 00:11:18.500 dedicated AI investment funds popping up now 00:11:18.500 --> 00:11:21.879 just for this sector. Like Araya Ventures, they 00:11:21.879 --> 00:11:25.620 secured $26 .3 million for an AI venture fund, 00:11:25.779 --> 00:11:28.419 specifically for AI and biotech investments. 00:11:28.799 --> 00:11:30.799 That's not just another tech fund. It's a very 00:11:30.799 --> 00:11:32.919 specific signal being sent. The significance 00:11:32.919 --> 00:11:35.080 there is pretty deep, isn't it? This kind of 00:11:35.080 --> 00:11:37.159 specialized fund really highlights the continued 00:11:37.159 --> 00:11:40.120 strong investor appetite. They're hungry for 00:11:40.120 --> 00:11:42.440 these specific groundbreaking technologies in 00:11:42.440 --> 00:11:44.879 these high growth areas. It shows investors aren't 00:11:44.879 --> 00:11:46.559 just dabbling. They're putting dedicated money 00:11:46.559 --> 00:11:49.220 into AI and biotech, expecting not just returns, 00:11:49.299 --> 00:11:51.919 but big. big long -term future growth. It signals 00:11:51.919 --> 00:11:53.879 a shift from general tech funds to these highly 00:11:53.879 --> 00:11:56.360 specialized vehicles. One's designed to accelerate 00:11:56.360 --> 00:11:59.419 very specific types of AI innovation with focused 00:11:59.419 --> 00:12:01.820 expertise and a longer view. It's a powerful 00:12:01.820 --> 00:12:04.840 vote of confidence in AI's future. So boiling 00:12:04.840 --> 00:12:08.340 that down, what does the creation of these specialized 00:12:08.340 --> 00:12:12.000 AI venture funds tell us about the wider market 00:12:12.000 --> 00:12:14.500 sentiment? It clearly signals that investors 00:12:14.500 --> 00:12:17.700 expect sustained high growth, and they recognize 00:12:17.700 --> 00:12:20.940 the need for deep expertise and targeted AI and 00:12:20.940 --> 00:12:23.919 biotech innovation for years to come. Okay, so 00:12:23.919 --> 00:12:27.159 wrapping this all up, what does this firehose 00:12:27.159 --> 00:12:29.919 of information mean for you, the listener, trying 00:12:29.919 --> 00:12:31.960 to make sense of this incredibly fast -moving 00:12:31.960 --> 00:12:34.379 field? We've seen just unbelievable amounts of 00:12:34.379 --> 00:12:37.259 money pouring into every corner of AI. Exactly. 00:12:37.659 --> 00:12:39.759 And connecting it all, a few key themes really 00:12:39.759 --> 00:12:42.580 jump out, right? First, the sheer scale of capital 00:12:42.580 --> 00:12:45.299 available is unprecedented, monumental. Second, 00:12:45.440 --> 00:12:47.759 the diversification. AI isn't just one thing. 00:12:47.799 --> 00:12:49.559 Its applications are spreading across practically 00:12:49.559 --> 00:12:51.960 every industry you can think of. And third, the 00:12:51.960 --> 00:12:53.940 foundational importance you just can't overstate 00:12:53.940 --> 00:12:55.899 it of high quality data and specialized hardware. 00:12:56.120 --> 00:12:58.580 AI isn't just a concept anymore. It's being applied 00:12:58.580 --> 00:13:01.100 right now to solve real world problems at incredible 00:13:01.100 --> 00:13:03.460 scale. Hopefully this deep dive gives you a shortcut 00:13:03.460 --> 00:13:05.200 to feeling really informed about what's driving 00:13:05.200 --> 00:13:08.129 this AI revolution. This has been a really fascinating 00:13:08.129 --> 00:13:11.429 look into the scale, the diversity of AI investments 00:13:11.429 --> 00:13:14.230 that are actively shaping what comes next. Yeah, 00:13:14.269 --> 00:13:15.669 and it leaves us with an important question, 00:13:15.809 --> 00:13:18.480 something for you to think about. Given this 00:13:18.480 --> 00:13:20.799 massive flood of capital, this incredibly fast 00:13:20.799 --> 00:13:23.820 pace of development, what ethical considerations, 00:13:24.139 --> 00:13:26.480 what societal impacts do you think we really 00:13:26.480 --> 00:13:29.440 need to be most mindful of? Especially as these 00:13:29.440 --> 00:13:32.379 powerful technologies weave themselves even deeper 00:13:32.379 --> 00:13:35.019 into our everyday lives. We really hope this 00:13:35.019 --> 00:13:37.000 deep dive has given you some surprising facts, 00:13:37.240 --> 00:13:39.960 maybe some new perspectives, and enough context 00:13:39.960 --> 00:13:42.379 to feel well -informed. And maybe spark some 00:13:42.379 --> 00:13:44.759 curiosity, too. Thanks for joining us for this 00:13:44.759 --> 00:13:47.000 deep dive. Until next time, keep exploring.