WEBVTT 00:00:00.000 --> 00:00:03.759 Imagine a billion dollars, not for the latest 00:00:03.759 --> 00:00:07.160 shiny AI chatbot or some big social network, 00:00:07.320 --> 00:00:11.060 but for, well, for organizing data. What's that 00:00:11.060 --> 00:00:12.900 about? Sounds kind of wild, right? Yeah. It's 00:00:12.900 --> 00:00:15.759 absolutely crucial. It's really the silent force 00:00:15.759 --> 00:00:20.359 behind how AI truly learns and gets better. Welcome 00:00:20.359 --> 00:00:22.899 to the Deep Dive. Today, we're going to follow 00:00:22.899 --> 00:00:25.789 the money. uh take a journey into the world of 00:00:25.789 --> 00:00:28.410 artificial intelligence just looking at july 00:00:28.410 --> 00:00:32.229 2025 alone billions, literally billions of dollars 00:00:32.229 --> 00:00:35.189 flowed into companies that are shaping AI's future. 00:00:35.469 --> 00:00:36.689 Yeah. And we're going to unpack where all that 00:00:36.689 --> 00:00:38.990 financial energy is really going inside the AI 00:00:38.990 --> 00:00:41.469 ecosystem. Think of it like mapping out the nervous 00:00:41.469 --> 00:00:43.490 system of this whole thing as it grows so fast. 00:00:43.609 --> 00:00:45.909 We'll look at everything from the textbooks AI 00:00:45.909 --> 00:00:48.509 learns from, to its muscles, the actual computing 00:00:48.509 --> 00:00:52.009 power, the agents that do tasks for us, and even 00:00:52.009 --> 00:00:54.479 the stuff you don't see. the infrastructure making 00:00:54.479 --> 00:00:56.960 it all work. Yeah. Our mission for you, the listener, 00:00:57.100 --> 00:01:00.060 is to get past just the huge numbers. We want 00:01:00.060 --> 00:01:02.939 to understand the big ideas behind them. What's 00:01:02.939 --> 00:01:05.420 this flood of cash telling us about where AI 00:01:05.420 --> 00:01:08.859 is actually heading and what might it mean for 00:01:08.859 --> 00:01:11.540 us? Let's start exploring. Okay, so our first 00:01:11.540 --> 00:01:14.500 stop is right at the core of AI. When we think 00:01:14.500 --> 00:01:16.400 about artificial intelligence, yeah, our minds 00:01:16.400 --> 00:01:18.719 often jump straight to those huge language models. 00:01:19.019 --> 00:01:23.420 But what about the raw materials, the fundamental 00:01:23.420 --> 00:01:25.640 building blocks that actually make them smart? 00:01:25.980 --> 00:01:28.140 Exactly. We're seeing these massive investments 00:01:28.140 --> 00:01:30.579 in what we could call AI's textbooks, you know, 00:01:30.579 --> 00:01:32.519 the data and its muscles, the specialized computer 00:01:32.519 --> 00:01:35.359 power. It's like like funding the library in 00:01:35.359 --> 00:01:37.640 the gym for this new intelligence. And here's 00:01:37.640 --> 00:01:39.140 where it gets really interesting, maybe even 00:01:39.140 --> 00:01:42.719 a bit counterintuitive. Surge AI landed a staggering 00:01:42.719 --> 00:01:45.379 billion dollars, a billion for data labeling. 00:01:45.480 --> 00:01:47.260 That feels like a huge amount for something that 00:01:47.260 --> 00:01:51.239 sounds so meticulous. Yeah. What exactly is data 00:01:51.239 --> 00:01:53.359 labeling and why is it worth so much right now? 00:01:53.640 --> 00:01:55.879 Right. So data labeling is basically this super 00:01:55.879 --> 00:01:58.500 careful process of organizing and tagging raw 00:01:58.500 --> 00:02:02.459 data. Like imagine feeding an AI thousands of 00:02:02.459 --> 00:02:04.879 pictures. And for every single one, a person 00:02:04.879 --> 00:02:07.500 marks it. This is a cat or that's a stop sign. 00:02:07.959 --> 00:02:10.379 You're giving the AI organized examples so it 00:02:10.379 --> 00:02:13.060 can learn patterns. OK. It's not just about having 00:02:13.060 --> 00:02:15.560 lots of data. It's about having really good, 00:02:15.659 --> 00:02:18.659 high quality data. This human curated stuff is 00:02:18.659 --> 00:02:21.370 like. The bedrock for AI models to get nuance, 00:02:21.669 --> 00:02:24.789 avoid bias, and actually work reliably out in 00:02:24.789 --> 00:02:27.270 the real world. A billion dollar bet here kind 00:02:27.270 --> 00:02:28.949 of tells you that getting that data quality right 00:02:28.949 --> 00:02:31.530 with humans involved is still a major bottleneck. 00:02:31.530 --> 00:02:33.129 It's a really high value piece of the puzzle, 00:02:33.210 --> 00:02:36.090 even as AI gets smarter. So it's the quiet, really 00:02:36.090 --> 00:02:38.490 precise work that truly feeds and refines an 00:02:38.490 --> 00:02:42.349 AI's intelligence. Eat. Fascinating. Then, switching 00:02:42.349 --> 00:02:45.370 gears a bit, you have Grok. They snag $600 million 00:02:45.370 --> 00:02:48.409 for AI computer chips. But these aren't your 00:02:48.409 --> 00:02:49.849 standard chips. They're what we call inference 00:02:49.849 --> 00:02:52.090 chips. Inference chips. Okay. And for our listener, 00:02:52.150 --> 00:02:54.310 what does inference mean in this context? What 00:02:54.310 --> 00:02:57.240 are these chips doing? So inference is when an 00:02:57.240 --> 00:03:00.300 AI model, one that's already been trained, uses 00:03:00.300 --> 00:03:02.439 what it learned to actually make a prediction 00:03:02.439 --> 00:03:04.300 or a decision. And it needs to do that really 00:03:04.300 --> 00:03:06.560 fast. Inference chips are basically specialized 00:03:06.560 --> 00:03:09.280 computer hardware built just for running those 00:03:09.280 --> 00:03:11.219 AI programs quickly. It's different from the 00:03:11.219 --> 00:03:13.780 huge computer clusters used for training the 00:03:13.780 --> 00:03:16.460 AI, which is like the teaching phase. Inference 00:03:16.460 --> 00:03:19.319 chips are for the doing phase, running what the 00:03:19.319 --> 00:03:21.699 AI knows super fast and often using way less 00:03:21.699 --> 00:03:24.520 energy. Grok getting that much money shows there's 00:03:24.520 --> 00:03:26.780 huge demand for putting AI to work efficiently 00:03:26.780 --> 00:03:29.740 in real time, everywhere from your phone to massive 00:03:29.740 --> 00:03:32.520 data centers. It's all about speed and sustainability, 00:03:32.620 --> 00:03:35.219 too. These seem like quite different things, 00:03:35.379 --> 00:03:38.139 carefully labeling data versus building super 00:03:38.139 --> 00:03:40.300 fast chips. What's the common thread? What connects 00:03:40.300 --> 00:03:42.860 these foundational investments? They're both 00:03:42.860 --> 00:03:45.419 core infrastructure. Really, they represent the 00:03:45.419 --> 00:03:48.400 foundational layers that let AI grow and function 00:03:48.400 --> 00:03:51.060 effectively. That makes a lot of sense. OK, moving 00:03:51.060 --> 00:03:54.479 beyond that foundation, we're seeing AI shift 00:03:54.479 --> 00:03:57.159 from just being a tool to becoming more of a 00:03:57.159 --> 00:04:00.500 true agent, something capable of doing complex 00:04:00.500 --> 00:04:03.800 tasks on its own, sometimes with a surprising 00:04:03.800 --> 00:04:05.699 amount of autonomy. Yeah, totally. It's like 00:04:05.699 --> 00:04:08.590 upgrading from, you know. basic calculator to 00:04:08.590 --> 00:04:10.849 having a digital assistant that doesn't just 00:04:10.849 --> 00:04:12.870 give suggestions, but actually does stuff for 00:04:12.870 --> 00:04:15.969 you. It anticipates things. So Kuvi .ai got $700 00:04:15.969 --> 00:04:18.110 million for something called agentic finance. 00:04:18.490 --> 00:04:20.329 What does that actually look like? How would 00:04:20.329 --> 00:04:23.709 that work for, say, a person or a business? Well, 00:04:23.730 --> 00:04:26.009 think of it less like your current financial 00:04:26.009 --> 00:04:28.610 advisor app that gives you tips and more like 00:04:28.610 --> 00:04:30.889 an AI financial manager. It could potentially 00:04:30.889 --> 00:04:33.490 execute trades or rebalance your portfolio or 00:04:33.490 --> 00:04:35.490 manage company expenses sort of automatically 00:04:35.490 --> 00:04:37.649 based on patterns it learns and goals you set. 00:04:37.769 --> 00:04:40.470 Agentic finance basically means AI making smart 00:04:40.470 --> 00:04:42.790 financial decisions independently. It signals 00:04:42.790 --> 00:04:45.300 a shift. You know, from AI just assisting us 00:04:45.300 --> 00:04:47.540 passively to becoming an active participant, 00:04:47.699 --> 00:04:49.779 especially in finance. That sounds incredibly 00:04:49.779 --> 00:04:52.660 powerful, definitely useful. But I can also imagine 00:04:52.660 --> 00:04:55.660 some businesses maybe feeling a bit nervous about 00:04:55.660 --> 00:04:59.439 privacy or maybe losing control if an AI is managing 00:04:59.439 --> 00:05:02.240 spending all by itself. How do companies like 00:05:02.240 --> 00:05:05.879 RAMP, which uses AI for spend management, how 00:05:05.879 --> 00:05:08.019 do they handle those worries? Yeah, that's a 00:05:08.019 --> 00:05:10.540 really important point. Companies like RAMP usually 00:05:10.540 --> 00:05:12.540 tackle this by focusing hard on transparency. 00:05:12.910 --> 00:05:15.709 and giving users lots of control. The AI isn't 00:05:15.709 --> 00:05:17.870 meant to be some rogue agent. It's more like 00:05:17.870 --> 00:05:20.250 an intelligent layer on top of existing processes. 00:05:20.589 --> 00:05:24.110 It spots patterns, flags things that look weird 00:05:24.110 --> 00:05:26.769 in spending, maybe auto -approves small things 00:05:26.769 --> 00:05:29.269 within rules you set up. But the human oversight 00:05:29.269 --> 00:05:31.430 is still there, crucially. The AI just takes 00:05:31.430 --> 00:05:33.350 away a ton of the manual grunt work, letting 00:05:33.350 --> 00:05:35.250 people focus on the bigger picture, the strategy, 00:05:35.410 --> 00:05:37.649 not just typing numbers. Right. That makes sense. 00:05:37.850 --> 00:05:39.790 And then in healthcare, you mentioned Ambien's 00:05:39.790 --> 00:05:42.129 healthcare, using AI to automatically write... 00:05:42.120 --> 00:05:44.579 medical notes from doctor -patient talks. That 00:05:44.579 --> 00:05:46.680 sounds like it could be a massive help for doctors 00:05:46.680 --> 00:05:49.420 drowning in paperwork. Absolutely. It saves doctors 00:05:49.420 --> 00:05:51.420 so much time, lets them actually focus more on 00:05:51.420 --> 00:05:53.259 the patient in front of them instead of constantly 00:05:53.259 --> 00:05:55.420 thinking about the documentation. It's a really 00:05:55.420 --> 00:05:57.319 tangible quality of life improvement for them. 00:05:57.579 --> 00:06:00.639 And Motive, applying AI in tracking vehicles, 00:06:00.920 --> 00:06:04.019 managing driver safety, that seems critical for 00:06:04.019 --> 00:06:06.360 logistics. Definitely. And then there's NAN. 00:06:06.990 --> 00:06:08.829 which helps different software apps talk to each 00:06:08.829 --> 00:06:10.689 other and work together automatically. Think 00:06:10.689 --> 00:06:13.689 of it like connecting Lego blocks of data, but 00:06:13.689 --> 00:06:16.410 intelligently automating these really complex 00:06:16.410 --> 00:06:18.629 workflows between systems that normally don't 00:06:18.629 --> 00:06:20.990 cooperate. These examples really show AI moving 00:06:20.990 --> 00:06:24.290 into more proactive roles. But beyond just automating 00:06:24.290 --> 00:06:27.290 tasks, how do they fundamentally show AI taking 00:06:27.290 --> 00:06:30.819 initiative? They highlight AI managing complex, 00:06:30.860 --> 00:06:33.279 you know, multi -step tasks independently across 00:06:33.279 --> 00:06:35.180 different industries. Okay, let's shift focus 00:06:35.180 --> 00:06:37.980 again. How is AI directly touching our lives 00:06:37.980 --> 00:06:40.560 in more visible, maybe more personal ways? Thinking 00:06:40.560 --> 00:06:43.220 about things like creativity or even critical 00:06:43.220 --> 00:06:45.569 public services. Yeah, these are the applications 00:06:45.569 --> 00:06:47.649 where you really start to feel the immediate, 00:06:47.790 --> 00:06:50.589 tangible effects of this technology, sometimes 00:06:50.589 --> 00:06:53.509 in pretty profound ways. For example, Falves 00:06:53.509 --> 00:06:56.569 is working deep in generative AI for media. They 00:06:56.569 --> 00:06:58.610 use something called diffusion models. Diffusion 00:06:58.610 --> 00:07:00.129 models? Yeah, diffusion models are basically 00:07:00.129 --> 00:07:03.269 AI systems that can create totally new media, 00:07:03.329 --> 00:07:06.670 like images, video, even music, just from a simple 00:07:06.670 --> 00:07:08.879 text description you give them. It's radically 00:07:08.879 --> 00:07:11.160 changing creative fields, artists, designers. 00:07:11.379 --> 00:07:14.399 They can quickly try out ideas or generate completely 00:07:14.399 --> 00:07:17.019 new content at a scale that just wasn't possible 00:07:17.019 --> 00:07:19.920 before. And Carbon, that's fascinating. Using 00:07:19.920 --> 00:07:23.300 AI for emergency response, helping 911 dispatchers 00:07:23.300 --> 00:07:25.759 find callers faster, maybe sharing live video 00:07:25.759 --> 00:07:27.839 from a scene. That sounds like it could literally 00:07:27.839 --> 00:07:30.560 save lives. It really underscores the potential 00:07:30.560 --> 00:07:32.660 scale and impact, doesn't it? I mean, just thinking 00:07:32.660 --> 00:07:35.579 about AI speeding up emergency response by even 00:07:35.579 --> 00:07:38.399 a few seconds, but across millions and millions 00:07:38.399 --> 00:07:40.699 of calls, you're talking about fundamentally 00:07:40.699 --> 00:07:44.540 saving lives. Whoa. Imagine scaling that to a 00:07:44.540 --> 00:07:47.180 billion queries or just the sheer number of lives 00:07:47.180 --> 00:07:50.079 potentially saved through faster response. That's 00:07:50.079 --> 00:07:52.819 kind of a moment of true wonder, right? A really 00:07:52.819 --> 00:07:55.259 profound example of AI's potential positive impact. 00:07:55.579 --> 00:07:58.439 Wow. Yeah. And then Slingshot AI is looking at 00:07:59.000 --> 00:08:01.560 Using AI for mental health coaching, like a personal 00:08:01.560 --> 00:08:04.000 AI guide for well -being accessible almost anywhere. 00:08:04.019 --> 00:08:06.000 That's another very personal application. For 00:08:06.000 --> 00:08:08.899 sure. And then Ultramix uses AI to analyze heart 00:08:08.899 --> 00:08:11.379 scans. looking for early signs of heart disease. 00:08:11.639 --> 00:08:14.120 That's incredibly vital. Catching things earlier 00:08:14.120 --> 00:08:16.120 means better outcomes for patients. These are 00:08:16.120 --> 00:08:19.339 intensely personal uses. How does AI really impact 00:08:19.339 --> 00:08:21.199 our daily lives through these kinds of innovations 00:08:21.199 --> 00:08:23.560 and creative work and critical services? From 00:08:23.560 --> 00:08:25.420 how we create art to how we manage our health, 00:08:25.540 --> 00:08:28.279 AI is deeply transforming personal experiences. 00:08:28.660 --> 00:08:31.420 Okay, finally, let's explore the areas that maybe 00:08:31.420 --> 00:08:34.840 aren't as flashy, but are just as crucial. The 00:08:34.840 --> 00:08:37.379 funding pouring into the behind the scenes infrastructure 00:08:37.379 --> 00:08:40.919 and the vital security that holds everything 00:08:40.919 --> 00:08:43.360 together. Yeah. This is the essential scaffolding, 00:08:43.379 --> 00:08:45.980 you know, the unseen guardians for the whole 00:08:45.980 --> 00:08:48.980 AI world. Without this stuff working reliably 00:08:48.980 --> 00:08:51.500 and securely, none of the cool applications would 00:08:51.500 --> 00:08:54.620 function at scale. Right. So Observe helps companies 00:08:54.620 --> 00:08:57.139 manage and actually understand the massive amounts 00:08:57.139 --> 00:08:59.769 of data they have stored in the cloud. making 00:08:59.769 --> 00:09:04.470 sense of all that digital noise. And 1KOMMA5Degrees, 00:09:04.470 --> 00:09:06.389 that's a clean energy company using technology, 00:09:06.649 --> 00:09:09.730 often involving AI, to make homes smarter, more 00:09:09.730 --> 00:09:11.850 energy efficient. Kind of a quiet revolution, 00:09:11.889 --> 00:09:15.029 but really powerful for sustainability. Anaconda 00:09:15.029 --> 00:09:17.710 provides open source tools for building AI applications. 00:09:17.909 --> 00:09:19.870 That sounds like it makes complex AI development 00:09:19.870 --> 00:09:22.009 more accessible for everyone, right? Not just 00:09:22.009 --> 00:09:24.889 big companies. Exactly. It democratizes it a 00:09:24.889 --> 00:09:28.960 bit. Inheritives provide security for older software 00:09:28.960 --> 00:09:31.120 legacy systems that maybe aren't supported anymore 00:09:31.120 --> 00:09:33.559 but are still critical. Keeps companies safe 00:09:33.559 --> 00:09:36.159 from hackers. It's like a digital bodyguard for 00:09:36.159 --> 00:09:38.519 aging tech infrastructure. And Oxide Computer 00:09:38.519 --> 00:09:40.500 Company, interesting, they actually build the 00:09:40.500 --> 00:09:43.340 servers for cloud computing from the ground up, 00:09:43.379 --> 00:09:45.600 not just assemble parts, designing the hardware 00:09:45.600 --> 00:09:48.039 itself. Then there's Noma Security, building 00:09:48.039 --> 00:09:50.299 what they call an autonomous security platform. 00:09:50.559 --> 00:09:53.279 And autonomous here means it works by itself. 00:09:53.360 --> 00:09:55.240 Pretty much, yeah. Autonomous security means 00:09:55.240 --> 00:09:57.179 AI protecting computer systems automatically, 00:09:57.519 --> 00:10:00.399 without needing constant human oversight. It 00:10:00.399 --> 00:10:02.960 learns what normal looks like for a system, spots 00:10:02.960 --> 00:10:05.580 weird stuff, anomalies, and can actually shut 00:10:05.580 --> 00:10:08.220 down threats before they cause damage. All on 00:10:08.220 --> 00:10:11.299 its own. 2047. That seems like a massive change 00:10:11.299 --> 00:10:13.960 in how we approach cybersecurity, moving from 00:10:13.960 --> 00:10:16.200 just reacting to threats to actually predicting 00:10:16.200 --> 00:10:19.519 and stopping them proactively. Huge shift. Absolutely. 00:10:19.799 --> 00:10:23.399 And then SAFE gives businesses sort of a cybersecurity 00:10:23.399 --> 00:10:26.580 credit score, helps them understand their specific 00:10:26.580 --> 00:10:30.269 risks in a clear, quantifiable way. Looking at 00:10:30.269 --> 00:10:32.490 their vulnerabilities, I have to admit, I still 00:10:32.490 --> 00:10:35.250 wrestle with prompt drift myself sometimes, trying 00:10:35.250 --> 00:10:37.169 to get an AI to consistently do what I want. 00:10:37.250 --> 00:10:39.009 So understanding vulnerabilities and managing 00:10:39.009 --> 00:10:42.269 risk proactively is just key in this complex 00:10:42.269 --> 00:10:45.610 world. And GUPS focuses on conversational AI, 00:10:45.850 --> 00:10:48.070 powering those chatbots we're all interacting 00:10:48.070 --> 00:10:50.009 with more and more for customer service and things 00:10:50.009 --> 00:10:52.409 like that. Right. And Ashby helps companies hire 00:10:52.409 --> 00:10:54.389 better with an all -in -one recruiting platform, 00:10:54.649 --> 00:10:57.830 often using AI, you know, to sift through applications 00:10:57.830 --> 00:11:00.490 and find the best potential candidates much faster. 00:11:00.649 --> 00:11:02.750 So we've got data management, clean energy tech. 00:11:03.240 --> 00:11:05.860 open source tools, security for old and new systems, 00:11:06.000 --> 00:11:08.480 even recruiting. What's the common thread tying 00:11:08.480 --> 00:11:10.379 these diverse companies together in this sort 00:11:10.379 --> 00:11:13.440 of unseen pillar category? They really represent 00:11:13.440 --> 00:11:16.600 the essential, often invisible systems that enable 00:11:16.600 --> 00:11:19.379 both AI itself and just modern business operations 00:11:19.379 --> 00:11:22.299 in general. Sponsor. So pulling this all together, 00:11:22.580 --> 00:11:26.840 what does it all mean? This deep dive into where 00:11:26.840 --> 00:11:29.299 the AI money's been flowing recently. It paints 00:11:29.299 --> 00:11:31.320 a pretty clear picture, doesn't it? The cash 00:11:31.320 --> 00:11:33.960 isn't just going into the brains, those big language 00:11:33.960 --> 00:11:35.799 models everyone talks about. Right, exactly. 00:11:35.840 --> 00:11:38.620 It's flowing into the entire ecosystem, like 00:11:38.620 --> 00:11:42.379 every single layer. From the textbooks that meticulously 00:11:42.379 --> 00:11:46.600 label data, teaching the AI to the muscles, those 00:11:46.600 --> 00:11:50.450 super fast, efficient computer chips. the agents 00:11:50.450 --> 00:11:52.669 that are starting to perform complex tasks for 00:11:52.669 --> 00:11:54.909 us kind of on their own. We've seen these huge 00:11:54.909 --> 00:11:57.370 investments covering everything from the absolute 00:11:57.370 --> 00:12:00.789 bedrock foundations of AI, like preparing data 00:12:00.789 --> 00:12:03.169 and building specialized hardware. all the way 00:12:03.169 --> 00:12:04.750 through to these really advanced applications 00:12:04.750 --> 00:12:08.110 that directly touch our daily health, our safety, 00:12:08.230 --> 00:12:10.210 even our creativity. It's a truly distributed 00:12:10.210 --> 00:12:12.730 effort. It feels like a systemic re -architecture 00:12:12.730 --> 00:12:14.490 happening across pretty much every industry. 00:12:14.730 --> 00:12:17.009 This isn't just some tech bubble, you know. It 00:12:17.009 --> 00:12:18.950 feels more like a fundamental rethinking of how 00:12:18.950 --> 00:12:20.990 businesses run and how we interact with the world. 00:12:21.049 --> 00:12:23.429 It's pretty profound when you look at it that 00:12:23.429 --> 00:12:25.919 way. This explosion of funding seems to tell 00:12:25.919 --> 00:12:29.080 us that AI isn't just a passing trend. It feels 00:12:29.080 --> 00:12:31.700 like a profound, undeniable shift that's already 00:12:31.700 --> 00:12:34.779 well underway. It truly is. We're basically watching 00:12:34.779 --> 00:12:38.019 the building blocks of an AI first future get 00:12:38.019 --> 00:12:41.179 put into place right now, sometimes quietly, 00:12:41.259 --> 00:12:43.580 but very, very rapidly. So for you, the listener, 00:12:43.679 --> 00:12:45.320 maybe here's a provocative thought to leave you 00:12:45.320 --> 00:12:48.240 with. As this huge amount of capital keeps pouring 00:12:48.240 --> 00:12:51.899 in and reshaping the landscape. How will this 00:12:51.899 --> 00:12:54.759 AI -driven future really change the fabric of 00:12:54.759 --> 00:12:57.159 our society over the next, say, five, ten years? 00:12:57.340 --> 00:13:00.179 What completely new applications, things we maybe 00:13:00.179 --> 00:13:02.759 can't even properly imagine yet, might emerge 00:13:02.759 --> 00:13:04.840 from these investments and fundamentally alter 00:13:04.840 --> 00:13:07.399 our daily lives? Keep your eyes open, because 00:13:07.399 --> 00:13:09.179 the implications are definitely far -reaching. 00:13:09.220 --> 00:13:11.340 And honestly, the journey feels like it's just 00:13:11.340 --> 00:13:13.440 getting started. Thank you for joining us on 00:13:13.440 --> 00:13:16.100 this deep dive into the latest AI funding landscape. 00:13:16.259 --> 00:13:18.039 We hope you feel a bit more informed and maybe 00:13:18.039 --> 00:13:20.720 just a little more curious about the world that's 00:13:20.720 --> 00:13:22.940 being built all around us. Out Hero Music.