Transcript
Narrator (0:08)
June 2007 Santa Clara, California. It's lunchtime and several dozen financial analysts are tucking into sandwiches in a tent set up in the parking lot of Nvidia's headquarters. They've spent this morning listening to presentations about Nvidia's latest bet, Cuda, a platform that allows computer programmers to turn Nvidia's graphics chips into into the workhorses of high end computing. For years, Nvidia used its engineering prowess to design GPUs graphical processing units to make video games look and play better. Now it's chasing a different crowd, scientists and technical professionals whose work demands incredible amounts of computing power. But many of the analysts here today think it's a waste of time. At one table, Nvidia CEO Jensen Huang is getting grilled by a skeptical analyst. Even the rosiest estimates put the market opportunity for this at $100 million. You spent almost five times that just bringing it to market. How will this ever deliver returns for investors? Huang puts down his sandwich and trots out the official company line.
Jensen Huang (1:18)
We're creating an entirely new customer base for our company. Our graphics chips offer 10 to 200 times the performance of general purpose chips. We're about to enter the era of the gpu.
Narrator (1:31)
The analyst isn't swayed. You're sacrificing profit margin on a long shot.
Jensen Huang (1:37)
Our revenues and profits are still growing. We are delivering for shareholders today while also ensuring we deliver for them tomorrow.
Narrator (1:45)
But you could be delivering more for them today. Cuda. Add $0 to your market capitalization. One of the other analysts at the table senses that Huang's patience is wearing thin and interjects jensen, I'm interested in the potential use cases. I have a two year old and I'm taking a lot of high resolution photos of her. But when I transfer them to my Mac to edit them in Photoshop, my computer grinds to a halt. Could Cuda help with that? Huang's eyes light up.
Jensen Huang (2:15)
Yes. In fact, we've already partnered with Adobe on this. Photoshop with Cuda will hand the task of editing photos to the gpu. It won't just stop your Mac from slowing down, it will process the images faster. This is what we mean by the era of the gpu.
Narrator (2:33)
The analyst who asked the question is impressed, but it's clear the others at the table still don't get it. They're thinking in quarters, but Huang's thinking in years. Huang doesn't know yet who will use Nvidia's chips for high end computing or how, but he believes if he gives them the tools they will build amazing things. Still, Nvidia is a public company with stockholders to answer to, and their patience won't last forever. If the era of the GPU is going to happen, Nvidia will have to create a market that doesn't exist yet. Closing the books, getting your people paid, and bringing on new hires Running a small or mid sized business can be exciting and also a little chaotic. Workday Go makes simplifying your business, well, simple. Imagine all the important aspects of your company hr, finance and payroll, all on the AI platform. No more juggling multiple systems. No more worrying about growing too fast. Just the full power of Workday Go, helping small to mid sized businesses like yours scale and run more smoothly. Think about what that means. Seamless onboarding for new team members, real time insights at your fingertips and payroll that works perfectly every single time. So you can focus on the big picture and go after your big ambitions. And with Workday you can activate quickly in as little as 30 to 60 business days. So simplify your business. Go for growth. Go with Workday Go. This message is brought to you by Apple Card. Apple Card members can earn unlimited daily cash back on everyday purchases wherever they shop. This means you could be earning daily cash on just about anything, like a slice of pizza from your local pizza place or a latte from the corner coffee shop. Apply for Apple Card in the Wallet app to see your credit limit offer in minutes. Subject to credit approval. Apple Card issued by Goldman Sachs Bank USA, Salt Lake City branch terms and more@applecard.com from wondering I'm David Brown and this is Business Wars. Last episode, Nvidia went from an idea hashed out in a Denny's diner to the leader in video game graphics chips. Now CEO Jensen Huang wants to use the company's graphics processing units to bring supercomputing to the masses. But with mounting costs and no obvious customers to sell to, investor patience may run out before Huang's big bet pays off. This is episode two once in a lifetime it's 2007 and Nvidia stuck between a rock and a hard place. It's burned through hundreds of millions of dollars developing Cuda. The platform was supposed to usher in a new computing era. Nvidia imagined programmers would use it to harness the parallel computing power of its GPUs and deliver the next wave of technological breakthrough. But simply making the tools available wasn't enough. Developers are ignoring it, which makes Wall street fear that Cuda will never deliver for stockholders. Nvidia has the tech, but the market's not ready. You know, entrepreneurs often fall into this trap. They bill for tomorrow while customers are still stuck in yesterday. The key isn't just invention, it's translation, helping people see why your future matters. Now Nvidia decides to try to get universities to embrace CUDA and teach the next generation of computer scientists how to use it. The company's chief scientist, David Kirk, leads the charge, wooing computer science professors with offers of free Nvidia hardware if they teach their students about cuda. It's a tried and tested strategy. Hardware and software makers often donate money and products to universities not out of generosity, but to ensure students learn to use their products instead of the competition's. But time after time, universities decline Nvidia's offer because professors don't know CUDA and aren't familiar with how to program GPUs. They're more comfortable with CPUs, which work linearly, but GPUs use parallel processing. You know, you can think of a CPU like a single lane street where the vehicles represent individual computing tasks. They all move forward in a single line, one after the other. Each vehicle can only proceed down the road in the order they arrive. GPUs, on the other hand, are more like multi lane highways. There are thousands of tasks happening at once and they don't share the same lane. So they can all move forward at different speeds at the same time. That's the big advantage of parallel computing. The downside is that it's much harder to program. Developers need to coordinate thousands of simultaneous tasks. There is a steep learning curve for anyone used to programming CPUs, which is almost all computer science professors. And this leaves Nvidia in a catch 22 to grow interest in CUDA. It needs universities to teach it. But if the teachers don't know it, they can't teach it. Still, Nvidia is onto something here. Seeding adoption in schools is a proven approach. It's how Apple conquered design schools and how Autodesk ruled architecture labs. When marketing won't open doors, curriculums will. Smart tech companies don't just sell products, they teach people how to need them. Finally, Nvidia gets a break. Kirk contacts the University of Illinois at Urbana Champaign. The head of its computer engineering department tells Kirk that if he really wants students to learn about parallel computing, he should teach the class. Kirk hesitates. He is not interested in teaching. But the university offers to pair him with another professor who can teach him how to teach. So Kirk agrees to give it a try. He flies from his home in Colorado to Illinois every other week and teaches a course called Programming Massively Parallel Processors. He assigns students research projects using Nvidia's tech and then publishes their findings. These research papers pique the interest of other universities who start asking for Kirk's teaching materials. Soon, parallel programming classes start appearing at universities around the world. Wow. Think of this. Instead of buying billboards, Nvidia bought something a whole lot more durable. Credibility. Every professor who taught CUDA became an unpaid brand ambassador, and every student turned evangelist extended the reach. That's network growth the old fashioned way, through reputation, not algorithm. Sure, it's slower at first, but stronger once it catches on. If you can turn believers into evangelists, you'll never need to outspend your rivals. But academia's embrace still isn't enough. Cuda remains a novelty rather than a catalyst for a tech revolution. The era of the GPU still seems far away. What Nvidia really needs is a killer use case, something that can demonstrate the untapped potential of GPUs. It's 2010, and in Stanford, California, Nvidia's new chief scientist, Bill Dally, is catching up with a friend, Andrew Ng, over breakfast. Ng is the director of Stanford University's Artificial Intelligence Lab and is working with Google on an ambitious project. The project's goal is to train computers to recognize images. To do this, Ng and his team are using neural networks, a type of computer system that was inspired by how brains work. When fed enough data, neural networks learn to identify patterns and find connections using a process of trial and error. And Ng's team is training its neural networks on one of Google's biggest data sets, YouTube. They're using all those cat videos uploaded to YouTube to teach their neural network to recognize cats, even in images its machines have never encountered before. Dall? E is impressed by UNK's progress. Neural networks are an old idea. They were first proposed in the 1940s. But it's only now that computers are powerful enough and data sets big enough to make for meaningful progress. Even so, the project is a heavy lift. Ng informs Dali that the project's neural network uses 2000 CPUs. Dali smiles and tells Ng that he's sure Nvidia's GPUs could do the same job faster and better. After that breakfast, Nvidia tests that theory by helping the project replace its CPUs with GPUs. But it's not a simple transition. GPUs need tasks diced into small pieces and specialist programming, but the final results make the effort More than worth it, the cat recognition algorithm that once required 2,000 CPUs now runs on just 12 Nvidia chips. It's a great leap forward for artificial intelligence research, or AI, and a great leap forward for Nvidia. With CPUs, machine learning crawled. With GPUs, it soars. Every breakthrough needs its proof of life. For Cuda, it was a neural network that learned to spot cats. Silly on the surface, but revolutionary where it matters. When you're selling something complicated, stories beat specs every time people don't remember tenfold efficiency gains. They remember how cat videos taught computers how to see. When Nvidia CEO Jensen Huang hears about the project's success, he takes a personal interest in it. For years, Nvidia has been hunting for a reason for Cuda to exist. Now it feels like it's founded in machine learning, a field that's full of promise and one that Nvidia now has a chance to claim for itself. But even with Nvidia's chips powering new technological advances, investors still aren't convinced. Machine learning is a bet on future breakthroughs. And as the years tick by, the pressure on Nvidia rises. In 2011, John Nichols, the driving force behind Cuda, dies from cancer. He believed to the end that Cuda could transform the world, and he kept working on it, even as his health gave way. But outside of Nvidia, enthusiasm continues to wane. Downloads of Cuda's software tools keep falling year after year. The company's share price has barely changed since Cuda's introduction, and Most of Nvidia's $4 billion of annual revenue still comes from selling graphics chips to gamers. To investors and analysts, Cuda looks like a vanity project, an expensive toy for academic tinkerers. To them, Nvidia is wasting money that could be invested elsewhere or paid out in dividends. And Huang is seeming less and less like a visionary and more like a CEO too proud to change direction. As investor unhappiness festers, hedge funds start circling. They see a company obsessed with long term bets that may never pay off and one that's failing to deliver maximum shareholder value. So they start buying Nvidia's flatlining stock, preparing to strike and force change that will net them millions.