Nvidia is the world's leading manufacturer of graphics processing units In the AI gold rush, chip-makers are selling shovels – and they are in short supply. As the latest generation of artificial intelligence models like ChatGPT look set to transform our lives, the hardware that makes it all possible is becoming a strategic asset, with countries, companies and researchers all scrabbling to get hold of supplies. With talk of shortages lasting until at least next year, some would-be AI developers face being left behind.

There's an old saying that the surest path to profit in a gold rush is to bet on the companies supplying the pickaxes -- and that idea is now igniting the toolmakers and wholesalers of today's generative AI boom. Why it matters: It's comparatively easy to see a broad tech trend on the horizon, but often much harder to home in on who will win and over what timeframe. Netscape and BlackBerry serve as cautionary tales. Software veteran Tom Siebel, who now runs enterprise AI firm C3.ai, sees a wide swath of the tech industry benefiting from the AI boom. Here are three categories of companies that serve as the modern-day axe vendors for AI.

Silicon Valley churns out new products all the time, but rarely does one receive the level of hype that has surrounded the release of GPT-4. The follow-up to ChatGPT can ace standardized tests, tell you why a meme is funny, and even help do your taxes. Since the San Francisco start-up OpenAI introduced the technology earlier this month, it has been branded as "remarkable but unsettling," and has led to grandiose statements about how "things will never be the same." But actually trying out these features for yourself--or at least the ones that have already been publicly released--does not come cheap. Unlike ChatGPT, which captivated the world because it was free, GPT-4 is currently only available to non-developers through a premium service that costs $20 a month.

Even fields affected by AI are innovating within the infrastructure, causing new use cases and new ways of thinking about AI. It's an exciting time to be working within AI. A lot of money is going into the space while startups continue to revolutionize the entire stack. There are lots of challenges, however, and starting something in AI just because that's what everyone is doing isn't quite the answer. The biggest challenge in the coming years will be the uniqueness of creating something in this space.

We are on the verge of the AI gold rush. Like the prospectors of the infamous historical gold rush, however, only a few leading organizations will strike gold. Real economic growth will be achieved by the companies selling the equivalent of picks, food, supplies, shovels, and jeans for artificial intelligence and machine learning. Think of all the tools required: training data, governance tools, consulting and integration services, and most critical, the creation of new sustainable revenue models. Startups, incumbent tech companies, and corporate innovation centers have already started using artificial intelligence and machine learning to solve real business problems across nearly every industry, including manufacturing, healthcare, transportation, and energy.

We are on the verge of the AI gold rush. Like the prospectors of the infamous historical gold rush, however, only a few leading organizations will strike gold. Real economic growth will be achieved by the companies selling the equivalent of picks, food, supplies, shovels, and jeans for artificial intelligence and machine learning. Think of all the tools required: training data, governance tools, consulting and integration services, and most critical, the creation of new sustainable revenue models. Startups, incumbent tech companies, and corporate innovation centers have already started using artificial intelligence and machine learning to solve real business problems across nearly every industry, including manufacturing, healthcare, transportation, and energy.

Every artificial intelligence startup or corporate R&D lab has to reinvent the wheel when it comes to how humans annotate training data to teach algorithms what to look for. Whether it's doctors assessing the size of cancer from a scan or drivers circling street signs in self-driving car footage, all this labeling has to happen somewhere. Often that means wasting six months and as much as a million dollars just developing a training data system. With nearly every type of business racing to adopt AI, that spend in cash and time adds up. Labelbox builds artificial intelligence training data labeling software so nobody else has to.

At Microsoft, we're confident that many aspects of the IT foundations for healthcare will move from on premise doctors' offices and clinics to live in the cloud. We ask the questions: Can we take advantage of this huge sea change in healthcare to unlock the innovation potential in healthcare data? Can we work as a community to ensure that we don't simply re-create the same data silos that we have today?

Artificial intelligence (AI) is the hot technology for Silicon Valley startups (I've personally talked to 15 AI startups in the past year). In fact, AngelList currently lists more than 2,000 AI startups with an average valuation of $5 million. In his TED Talk, digital visionary Kevin Kelly described AI as the next revolution, and his simple formula for startups is to "solve a problem using AI." And, IDC projected the AI market will reach more than $47 billion by 2020, with 62% of enterprises adopting the technology by next year. Interestingly enough, AI adoption isn't limited to startups.