NVIDIA's New Computer Released a TERRIFYING Message To Jensen Huang! Jun 16, 2024 !

2:25 / 28:28 NVIDIA's New Computer Released a TERRIFYING Message To Jensen Huang! Matter 62.9K subscribers views Jun 16, 2024 ! NVIDIA's New Computer Released a TERRIFYING Message To Jensen Huang! NVIDIA CEO Jensen Huang recently returned to Stanford, sharing a story that shocked everyone. He spoke about his early days at LSI Logic and his courageous decision to leave a secure job and venture out independently. This bold move led to the creation of NVIDIA, a company renowned for its groundbreaking AI technologies that have revolutionized industries such as healthcare and automotive. How can one man build a billion-dollar company? Let's delve into Jensen's advice that could help every entrepreneur build their own empire. The push came from his interactions with Chris and Curtis, two acquaintances from Sun Microsystems. They were all working together at LSI Logic, surrounded by some of the smartest people in tech. Chris and Curtis were thinking about leaving Sun and were curious about what could be next. During the early '90s, everyone was talking about the microprocessor revolution. Personal computers were becoming a household item, and it was clear that these tiny chips were going to change everything. Huang and his friends saw a chance to create a new kind of company. They wanted to build computers that could solve problems regular ones couldn’t handle. That’s how they started focusing on what we today call artificial intelligence. They envisioned machines that could assist in everything from designing drugs to driving cars without a human at the wheel. How this content was made Altered or synthetic content Sound or visuals were significantly edited or digitally generated. Learn more Transcript Follow along using the transcript. Show transcript Transcript 0:09 NVIDIA CEO Jensen Huang recently returned to Stanford, sharing a story that shocked 0:15 everyone. He spoke about his early days at LSI Logic and his courageous decision to leave 0:21 a secure job and venture out independently. This bold move led to the creation of NVIDIA, 0:28 a company renowned for its groundbreaking AI technologies that have revolutionized 0:33 industries such as healthcare and automotive. How can one man build a billion-dollar company? Let's 0:40 delve into Jensen's advice that could help every entrepreneur build their own empire. 0:46 The push came from his interactions with Chris and Curtis, two acquaintances from Sun Microsystems. 0:55 They were all working together at LSI Logic, surrounded by some of the smartest people in tech. 1:00 Chris and Curtis were thinking about leaving Sun and were curious about what could be next. 1:05 During the early '90s, everyone was talking about the microprocessor revolution. Personal computers 1:12 were becoming a household item, and it was clear that these tiny chips were going to change 1:17 everything. Huang and his friends saw a chance to create a new kind of company. They wanted 1:23 to build computers that could solve problems regular ones couldn’t handle. That’s how they 1:28 started focusing on what we today call artificial intelligence. They envisioned machines that could 1:34 assist in everything from designing drugs to driving cars without a human at the wheel. 1:40 Getting people to believe in and invest in a brand new idea isn’t easy. Back then, 1:47 it was even tougher because Huang was trying to get funding for technology that many couldn't yet 1:52 imagine. How do you convince some of the most influential investors to take a chance on a 1:57 new company led by someone who’s never done this before? Huang took an unconventional approach. He 2:04 decided not to bother with a detailed business plan, which he found daunting and unhelpful. 2:09 Instead, he went straight to the venture capitalists to pitch his idea directly. 2:15 This approach was a big gamble. It involved talking about technologies and possibilities 2:20 that were so new, they didn’t even have a clear market yet. But Huang’s conviction and his direct 2:26 way of dealing meant he didn’t just follow the paths others had laid out. He made his own way, 2:33 and it led him to build NVIDIA, a company that’s at the forefront of AI technology today. 2:39 When Jensen Huang was called back by his old boss to explain what he had been working on, 2:44 it did not go as planned. His former boss couldn’t understand his elevator pitch and 2:49 found it to be quite terrible. Despite this, his boss still decided to call up Don Valentine 2:55 to persuade him to invest in Jensen, claiming he was one of the best employees his previous 3:00 company ever had. This situation taught Jensen an important lesson: you can't hide from your past, 3:07 so it’s better to have a good one. During his time as COO, Jensen realized 3:13 the importance of being the best, especially when his company was one of many trying to make a mark 3:18 in the same field. They were running out of time and money, and their original plan wasn’t working 3:24 out. It was a tough spot to be in, having to figure out a new plan with so much pressure. 3:30 The breakthrough came when they decided that the focus of their technology would be 3D graphics, 3:36 specifically for video games. At that time, making affordable 3D graphics was a huge challenge, and 3:43 the video game industry was just starting to grow into a billion-dollar market. It was a risky move, 3:50 but it was also an opportunity to create something big. This decision was crucial in 3:55 setting the direction for the company’s future. Don's advice at the end of Jensen's pitch was 4:01 a reality check: startups shouldn’t rely on other startups to succeed. It highlighted the 4:08 complexity of their situation, especially when you consider that NVIDIA's success hinged on a 4:14 partnership with Electronic Arts, whose CTO was just a teenager still being driven to work by his 4:20 mom. It was a bizarre and almost laughable scenario, but it was the reality they were 4:26 facing. Don’s final remark to Jensen, a mixture of humor and warning about losing his investment, 4:33 captured the intense and sometimes absurd pressure of navigating the startup world. 4:39 These stories from Jensen’s path show more than just his career progression. They shed light on 4:45 the unpredictable, often ridiculous world of technology startups, where clear plans 4:50 can become muddled and success depends on a mix of hard work, good timing, and sometimes, just 4:56 making the best of a bad situation. They reveal the layers of challenges that entrepreneurs face, 5:02 from proving themselves in menial jobs to making high-stakes business decisions that could either 5:08 make or break their companies. Winning the 3D Graphics War 5:14 Then, as NVIDIA navigated this newly minted market, Microsoft introduced a game-changer 5:20 with Direct3D, quickly becoming a new standard in the industry. This move by Microsoft gave 5:26 rise to hundreds of new companies, turning the market into a battlefield of competition. NVIDIA, 5:33 which had been at the forefront with its own 3D graphics technology, suddenly found 5:38 its creations at odds with Direct3D. They were left with two choices: either adapt to 5:45 the new standards or risk fading into obscurity. During a particularly stressful period, it became 5:51 clear that the company was in a tight spot. NVIDIA was at a disadvantage with 89 companies now vying 5:58 for the same space and using the newer Direct3D standard. They didn’t initially know how to switch 6:05 gears to fit this new mold. But then, a solution appeared in an unlikely place—a local bookstore, 6:12 Fry’s Electronics, which might still be around. The OpenGL manual from Silicon Graphics was 6:19 nestled among tech manuals and computer books. This manual was like a treasure map for NVIDIA. 6:26 It detailed how to create graphics in a way that could put them back in the game. With a 6:32 couple of hundred dollars in hand, these manuals were purchased and brought back 6:36 to the office with a sense of urgency. The manuals weren't just another set of 6:41 instructions; they were a lifeline. They featured a complete blueprint of the OpenGL graphics 6:47 pipeline—a sophisticated diagram that showed exactly how to build the graphics technology 6:53 from the ground up. NVIDIA embraced this challenge wholeheartedly. They weren’t just going to follow 6:59 these instructions; they planned to innovate beyond them. They distributed the manuals, 7:04 each with a prominent display of the OpenGL pipeline, and got to work. The 7:10 team pushed the boundaries of what was possible, crafting something the world had not yet seen. 7:16 By choosing to adapt and innovate, NVIDIA demonstrated their resilience and ingenuity. 7:22 This wasn't just about survival; it was about reinventing themselves in a market that had 7:26 dramatically shifted. They had to navigate through the complexities of tech evolution, constantly 7:32 adjusting and advancing to stay relevant. This situation highlighted the relentless pace of 7:38 the tech industry, where today’s breakthrough is tomorrow’s old news. NVIDIA’s ability to pivot 7:45 and innovate under pressure not only saved them from extinction but also cemented their 7:50 place as a leader in the tech world, always ready to tackle the next big challenge. 7:55 The company’s big leap into artificial intelligence paints a picture of a bold, 8:00 revolutionary move that set NVIDIA up for a massive public offering and multiplied their 8:06 revenue several times over in just a few years. But amidst this wave of success, the story takes 8:13 an odd turn: Huang supposedly shifts NVIDIA's entire innovation strategy based on just one 8:20 chat with a chemistry professor. This idea that a single conversation could steer the direction of 8:26 a massive tech company seems a bit far-fetched, almost as if the company's strategies hinge on 8:31 whims rather than well-researched decisions. From its early days, NVIDIA was always pushing 8:38 the envelope, starting with computer graphics and quickly moving into other areas like image 8:43 processing and particle physics. The development of programmable shaders was a key milestone, 8:50 making it possible to customize imaging and graphics in unprecedented ways. This 8:55 innovation led to something called CG for GPUs, which was developed several years before NVIDIA 9:01 introduced CUDA, marking a significant advance in computing capabilities. CG started getting a lot 9:08 of attention, featured prominently in textbooks and courses, and it caught the interest of 9:13 researchers and professionals in various fields. When researchers, including some doctors at 9:19 Massachusetts General Hospital, began using this new tech for CT reconstructions, it seemed to 9:25 confirm NVIDIA's belief in the utility of their innovations. Huang’s personal visit 9:31 to these doctors further reinforced this belief, giving the company even more confidence to pursue 9:36 these new computing solutions that could tackle problems traditional computers couldn't handle. 9:43 This style of decision-making paints a picture of a company that's constantly on the edge, ready to 9:48 jump on the next big idea, regardless of where it comes from. It highlights a culture of rapid 9:54 adaptation and innovation, which has undoubtedly been a part of NVIDIA's success story. However, 10:01 it also suggests a potentially chaotic environment where major decisions are made in the spur of the 10:07 moment. While this can lead to breakthroughs, it also poses risks, making the company’s history a 10:13 series of high-stakes gambles in the fast-paced tech world. This approach to business and 10:18 innovation could be seen as either a masterstroke of flexibility or a precarious way to navigate 10:23 through the tech industry’s turbulent waters. NVIDIA, under the leadership of Jensen Huang, 10:29 seems to operate on a bold, almost daring principle: they bet big on what they think the 10:34 future of technology will look like, positioning themselves to be at the forefront when these 10:39 technologies finally take off. It's almost as if they're playing a game of high-tech catch, 10:44 ready to grab opportunities the moment they drop. But really, how often can a company count 10:50 on being perfectly positioned every single time? It sounds a bit too good to be true. 10:55 Follow along as NVIDIA takes bold steps into new tech areas, betting on future technologies 11:00 that could redefine computing. The Risky Road to Future Tech 11:04 The strategy is deeply rooted in the belief that they can build computers that do things 11:09 normal computers can't—solve problems that are beyond the capabilities of standard CPUs and the 11:15 usual computing methods. This approach isn't just ambitious; it’s like stepping into a race without 11:22 knowing the track. For a long period, about a decade, NVIDIA poured effort and money into 11:27 developing technologies without any guarantee that these areas would turn into profitable markets. 11:33 They were betting on the future, hoping their chosen fields would grow into something big. 11:39 Managing this kind of high-stakes bet is quite the challenge. Imagine trying to keep everyone 11:44 from engineers to shareholders, from board members to business partners, aligned and committed when 11:51 there's no solid proof of a future market. In the early stages of such pioneering tech, you need 11:57 to spot signs that you’re on the right path—these are called key performance indicators (KPIs). But 12:03 even the concept of KPIs is something that Huang finds tricky. What really counts as a 12:09 KPI? It's not about just looking at immediate results like gross margins, because those are 12:16 outcomes, not indicators of future success. Huang's approach is to stick firmly to his core 12:23 beliefs unless something really convincing comes along to change his mind. He’s constantly on the 12:29 lookout for early signs that might hint at future success. This is a critical part of 12:34 his strategy—figuring out whether the steps they’re taking are moving them closer to their 12:40 goals. It’s about believing that they’re doing the right thing without needing the 12:44 immediate financial evidence to prove it. This approach was put to the test when NVIDIA 12:51 started exploring the field of deep learning. At the beginning, Huang didn’t really understand 12:56 what deep learning was. Despite this, NVIDIA dove in and developed something called CNN, 13:03 a specialized language made specifically for deep learning, similar to how OpenGL works 13:08 for graphics. This was not because there was a promise of quick money. In fact, 13:13 the financial returns were uncertain and likely a long way off. They went ahead because they 13:19 believed deeply in the importance of the work they were doing. They created a tool that researchers 13:24 needed badly, even though these researchers didn’t have any money to offer in return. 13:30 NVIDIA’s willingness to engage in projects that don’t promise immediate financial 13:34 returns highlights their commitment to advancing technology and science, 13:39 even if it doesn’t immediately fill their pockets. They focus on whether the work itself is valuable, 13:45 whether it pushes the field of science forward in significant ways. They don’t need to see a 13:51 business case or financial projections to decide to move forward. The main question they ask is 13:57 whether the work is important and whether it would still happen if they didn’t do it. If NVIDIA steps 14:03 away from a project and it still gets done, Huang actually finds that quite satisfying. It shows 14:10 that they’re focusing on what truly matters, not just on projects where they are needed. 14:15 At NVIDIA, selecting which projects to work on is like choosing which hill to die on. They’ve 14:22 taken on projects like deep learning because they convinced themselves these were the next 14:27 big things that could change the world. This approach of going all-in on select ventures has, 14:33 at times, brought huge rewards, both in terms of money and industry clout. Think of it as 14:40 NVIDIA navigating through a stormy sea where the stakes are high, like during the times when its 14:46 stock price took a massive hit, dropping by 80% during the financial crisis. This was a moment 14:53 that tested their resolve deeply. During such a financial nosedive, 14:58 you might imagine the boss wanting to hide away and not face anyone. It’s embarrassing, after all, 15:04 to see so much value wiped out so quickly. Yet, the daily routine doesn’t change. Get up, plan 15:11 the day, and focus on what needs to be done. The core question remains: do you still believe in the 15:18 foundation of your company? Has anything really changed about the fundamentals of your business, 15:23 despite the falling stock numbers? If the basics haven't changed, then the strategy 15:28 stays the same. This stubborn or maybe principled approach to not shifting course unless absolutely 15:34 necessary is like holding the steering wheel straight even when the waves are crashing hard. 15:40 But then, think about having to stand in front of a room full of employees when things are looking 15:45 grim. Imagine the sea of anxious faces, the whispers of doubt. Some might think this is 15:52 the end of the line for the company, others might be quietly labeling you as out of your depth. Yet, 15:58 here’s where the role of a CEO becomes less about charts and numbers and more about leading with 16:04 heart. You have to address the team, share the situation, and try to keep everyone’s 16:10 spirits up. It’s not just about managing a company; it’s about managing people’s fears 16:16 and expectations. Watch how strong leadership and a united team help NVIDIA navigate through 16:24 tough times and critical decisions. The Human Side of Tech Leadership 16:29 This challenge involves more than just making decisions about technology or markets; 16:34 it’s about showing up every day, facing your team, and convincing them to keep pushing 16:39 forward with you. It’s about transparency, showing that you’re all in this together, 16:45 whether the ship is sailing smooth or hitting rough waters. You have to rally the team, 16:51 keep them focused on the collective goal, and sometimes even absorb their doubts and fears. 16:58 Navigating through these tough times tests a leader’s true mettle. Can they keep their 17:04 team motivated when all signs point south? Can they maintain faith in their vision when doubts 17:11 cloud every corner? This ongoing battle between maintaining course and adapting to new realities 17:18 is what defines a leader’s legacy at a company like NVIDIA. It’s about not just surviving the 17:24 storms but steering through them with confidence and conviction, all while keeping an eye on the 17:30 distant horizon, hoping that the choices you make today will lead to clearer skies tomorrow. 17:36 This guy walks in and claims his team is the best in the business. Not just good, 17:41 but the crème de la crème, leading what he calls the most stacked team of tech wizards the world 17:46 has seen. It sounds impressive, sure, but it's also a bit much, don't you think? 17:52 But what's with this push for a flat company structure? Huang seems to think it's the secret 17:57 sauce for cutting through the corporate noise and keeping communications clear. He believes 18:02 that the fewer people between the CEO and the rest of the team, the better. In his view, 18:08 this setup helps get the best out of his people, empowering them to take charge and make decisions 18:13 without someone constantly looking over their shoulders. It's a big claim, especially for 18:18 a company with 30,000 employees. Huang talks up a storm about how every single NVIDIA worker is 18:26 making big moves every day, all by themselves, which sounds a bit too good to be true. 18:32 Now, let’s dive into the latest tech craze—generative AI. Huang's all over this topic, 18:38 touting NVIDIA as a key player just as this tech hits what he calls a major turning point. He's 18:44 pumped about the endless possibilities, from digitizing genes to cracking the code on what 18:49 all these bits and bytes really mean. But as he spins this tale of tech revolution, it feels 18:55 more like he’s selling a dream—a vision where his company leads the charge into a new digital era. 19:02 The way Huang talks about it, you'd think generative AI is the golden ticket to solving all 19:07 of life's puzzles. According to him, NVIDIA has been at the forefront, turning everything from DNA 19:13 to videos into data that computers can understand and use. This isn't just about making money; 19:19 it's about changing the game, or so he says. He paints a picture of a future where this 19:25 technology reshapes everything from healthcare to entertainment, translating the complex 19:30 language of data into something meaningful. Yet, with all this talk about leading the tech 19:36 revolution, one has to wonder if it's as much about setting trends as it is about crafting 19:41 a narrative where NVIDIA is always one step ahead, out of reach from real critique. It’s 19:48 easy to get caught up in the hype when someone tells you they’re unlocking the puzzles of the 19:52 universe. But beneath all that, there's a simpler, perhaps more critical question 19:57 about how all this high-minded tech talk translates into real-world applications. 20:03 And while Huang lays out his philosophy for a lean, mean management machine, it’s worth asking 20:09 how all this scales up in such a big company. The idea that every employee acts as an independent 20:15 decision-maker every single day might seem a bit of a stretch. It sounds great on paper, 20:20 but in the real world, even the best teams need some level of direction and oversight. 20:26 As for the future, Huang’s approach to leadership and technology offers a blueprint that other 20:31 companies might follow, or at least consider. His emphasis on direct communication and minimal 20:38 management layers is designed to foster innovation and quick decision-making. If nothing else, 20:45 it's a reminder that in the fast-paced world of tech, staying connected and keeping things simple 20:51 can sometimes be the best way forward. But whether NVIDIA’s strategies are truly the best remains to 20:57 be seen. For now, it’s clear that Huang knows how to tell a compelling story. Whether that story 21:04 leads to sustainable success, or just more Silicon Valley hype, is something only time will tell. 21:11 Jensen Huang really paints a picture of a world transformed by what he calls 21:16 generative AI. He talks about how everything from making videos understandable through text, 21:22 to creating pictures from words, is changing the game. He wants everyone to see these 21:27 changes as nothing short of a revolution in how we handle and understand information. 21:33 According to Huang, NVIDIA isn’t just in the business of making chips; it’s in the business 21:38 of redefining how we create and use software. He sees a future where software doesn’t just 21:44 retrieve information but actually generates it from scratch. This means that, in the future, 21:50 a small piece of information, or a 'prompt' as he likes to call it, can be the starting point 21:56 for creating something entirely new, rather than just pulling up something that already exists. 22:03 Step into a future envisioned by Jensen Huang, where technology goes beyond assisting to actively 22:09 creating new ways of handling information. NVIDIA's Role in the AI Boom 22:16 Huang’s vision is all about moving from a world where systems mostly look up and 22:20 manage information, to one where they actually produce new content and solutions on their own. 22:26 But as grand as this vision is, it might be a bit too polished. Is it really going to 22:31 be this smooth and revolutionary? It feels a bit like he’s selling a dream that overlooks 22:37 the messy realities of how technology usually gets adopted and integrated into our lives. 22:43 He also talks a big game about how this shift to generative computing will totally shake up 22:48 different industries, from how networks operate to how we manage data storage. He suggests that 22:54 the ways we’ve been using and abusing internet resources will see a drastic reduction as these 22:59 new technologies take over. However, the big assumption here is that everything will just 23:05 fall into place perfectly, which often isn’t how technological change happens. 23:10 There are also big questions about what all this means for the real world. How will data privacy be 23:16 handled when systems can generate all kinds of new content from a few data points? What about 23:22 the accuracy of this machine-generated content? How do we manage the potential for these systems 23:29 to create false or misleading information? These are questions Huang doesn’t really dive into. 23:35 Moreover, this focus on machines not just finding but also creating information could 23:41 lead to significant changes in how we think about work itself. If machines are doing more 23:47 of the creating, what happens to human creativity? What does it mean for jobs that have traditionally 23:53 relied on human decision-making? Huang’s narrative is so focused on the tech that 23:59 it kind of skips over the human element. And let's be real—while the tech might be 24:04 ready to go this route, are we, as a society, ready to handle all the changes it could bring? 24:10 Are industries really going to be turned upside down, or will the adoption be more 24:15 gradual and less dramatic than Huang makes it sound? It's one thing to have the technology 24:20 to do something; it's another to have the infrastructure, regulations, and societal 24:25 acceptance in place to really make it work. NVIDIA’s CEO really has a way with words, 24:32 especially when he's talking about how companies are set up. He takes a deep dive into history to 24:37 make his point, comparing old business models to ancient kingdoms. Back then, according to him, 24:45 CEOs were like kings and everyone else just followed orders without really knowing why. 24:51 It paints a pretty bleak picture of the past, suggesting that old companies kept their workers 24:56 in the dark on purpose, only feeding them the bare minimum info they needed to do their jobs. 25:02 Now, contrast that with today’s NVIDIA, at least through Huang's lens. He’s got 25:08 around 30,000 employees and he says he wants them all to be inquisitive, to ask questions, 25:14 and to really get into the weeds of why things are done the way they are. It’s a big shift from the 25:19 'do as you’re told' vibe he attributes to the past. According to him, this open, questioning 25:25 environment is a major upgrade, promoting a freer flow of ideas and better job satisfaction. 25:33 But here's where you might start to wonder—was the past really that bad? Were all old companies 25:39 like tight ships where no one knew anything beyond their daily tasks? It sounds a bit too simplistic. 25:46 While it’s true that some companies might have been strict and confidential, it’s probably an 25:51 exaggeration to say that all businesses operated like medieval courts. Using this stark contrast, 25:57 Huang sets up a pretty convenient way to showcase how NVIDIA is supposedly doing everything better. 26:04 He takes it further by questioning why companies in totally different fields would 26:08 organize themselves in the same way. If you're making tech products versus running hospitals, 26:14 your setup should reflect those differences, right? Huang thinks it’s silly to have the same 26:19 structure when the work is so different. On one hand, he's got a point—it makes sense to tailor 26:25 your company’s structure to fit what it actually does. But on the other hand, he seems to dismiss 26:31 the fact that some organizational practices are universal for a reason. They might just work well, 26:37 regardless of the industry. Then Huang gets all philosophical, 26:42 asking what the company is really all about—what goes in, what comes out, 26:47 and what kind of environment it’s operating in. He describes his job as building the perfect 26:53 conditions for everyone at NVIDIA to do the best work of their lives. It’s a lofty goal, painting 26:59 him as the master architect of a company designed to foster personal fulfillment and innovation. 27:06 This brings us to another point of skepticism. How realistic is it to expect a company, 27:12 especially one as big as NVIDIA, to perfectly align with every employee’s dream job? While it’s 27:18 great to aim for such an ideal, the day-to-day realities of running a giant company are bound 27:23 to complicate things. Huang’s portrayal of his role might be a bit too idealistic, suggesting 27:30 a scenario where everything fits together just right, and everyone is happy and productive. 27:36 Furthermore, while Huang praises NVIDIA’s approach to questioning and open dialogue, 27:41 you have to wonder how it actually plays out. Is it really that different, 27:46 or is it more of a subtle evolution rather than the revolution Huang describes? And what about the 27:52 practicalities of having so many inquisitive employees? While it sounds great in theory, 27:58 in practice, too much questioning can lead to delays and confusion if not managed properly. 28:06 Could Jensen Huang's unconventional path from washing dishes to leading a tech giant like NVIDIA 28:12 inspire more Silicon Valley disruptions? What do you think lies behind such bold moves? Share your 28:20 thoughts and don't forget to like and subscribe for more intriguing stories and discussions! 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