NVIDIA GPU artificial intelligence AI

NVIDIA’s Success Showcases Tech’s Multiple-Choice and Focus Problem

To say that NVIDIA’s recent financial results were impressive would be a huge understatement. I’ve been working as an internal or external analyst for just short of 40 years now, and there are two recurring mistakes that companies make when creating a new market or moving into a new existing market. The first is treating a list of requirements for success as if they were multiple-choice, not all-of-the-above, and the second is thinking the grass is greener in the market you don’t know.

Let’s take each in turn because NVIDIA’s success is the result of not making these mistakes, at least not with AI.

Multiple-choice vs. all-of-the-above

This is the equivalent of looking at a list of requirements for success and treating the items on that list as if they were suggestions instead of requirements. For instance, when Microsoft wanted to go after the Apple iPod, it had a list of things it knew it needed to get done, chose not to do some of them and the Zune failed. When Apple came out with a server, it had a list of things it needed to accomplish and chose not to, and it failed.

The other mistake that’s often made is to think you must do it all yourself. Facebook just showcased that mistake with its metaverse effort. It tried creating an entire market on its own and discovered that the cost exceeded its capabilities.

What makes NVIDIA’s current success stand out is that it treats the list of market requirements for AI success as if it were a multiple choice question, and it didn’t try to do it all alone. Instead, it formed critical partnerships reminiscent of what IBM did to create the IBM PC.

NVIDIA looked at what was needed to create a viable AI solution that included more than just the GPUs it’s famous for. It would need unique workstations, unique servers, development tools, simulation platforms to automate training and assure safety, and the ability to take the resulting AI technology every place it needed to go. NVIDIA didn’t define markets it didn’t intend to enter. It partnered to blanket the industries where AI needed to go and is now the undisputed AI technology market leader.


Companies often look at adjacent markets as if they are a cure for their inability to compete in their chosen market. Thomas Watson, Jr., IBM’s most famous CEO, once said “Be willing to change everything but who you are.” Too many companies seem to forget who they are. Netscape was the internet pioneer but when faced with Microsoft, it tried to become Microsoft. When faced with the iPhone, BlackBerry tried to become Apple. Sun Microsystems tried to become Microsoft, and the list goes on. All those companies forgot who they were, lost focus and failed, and existed only as shadows of what they once were or disappeared entirely.

NVIDIA has always been NVIDIA and worked to be the best NVIDIA it could be. It recognized that its GPU technology dealt with unstructured data far better than CPU technology did and was the key to creating a working AI. It was also and remains a leader in building ecosystems and that, for AI to be broadly accepted, they needed a rich ecosystem and built it.

Part of this identity problem with other companies is the process of hiring CEOs from outside the company rather than training them up inside. A new CEO from outside will have a very different view of what the company should be, and that view will be corrupted by whatever firm the CEO has led or worked for previously. It is virtually impossible to maintain a constant focus if the head of the company is being swapped out regularly.

Having Jensen Huang steady at the helm as CEO has been a huge benefit to NVIDIA and is largely key to the firm’s current success which was decades in the making. This does beg the question of what will happen if Huang leaves.

Wrapping up

NVIDIA is a showcase of two best practices that many of its peers haven’t seemed to understand. One is that when entering a new market, you must meet all, not just some, of the market requirements (as NVIDIA has showcased with its AI solutions). The second is that you need to be the best at who you are and maintain focus instead of chasing every new company like a cat chases a laser pointer.

NVIDIA’s success is a textbook case of the benefits of understanding market requirements and meeting them. I’ve often told clients that if you are unwilling to do what is necessary to be successful, why bother doing it? Save your money rather than fail.

And trying to be a better someone else is also a losing game. You’ll never be better at being someone else, but you can achieve being a better you. NVIDIA is the best NVIDIA it can be, and that is paying huge dividends to the company, its employees, its customers, and its investors this quarter.

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