Quantifying the potential financial rewards is difficult, but for the leading AI cloud providers they could be unprecedented. AI could double the size of the $260 billion cloud market in coming years, says Rajen Sheth, senior director of product management in Google’s Cloud AI unit. And because of the nature of machine learning—the more data the system gets, the better the decisions it will make—customers are more likely to get locked in to an initial vendor.

In other words, whoever gets out to the early lead will be very difficult to unseat. “The prize will be to become the operating system of the next era of tech,” says Arun Sundararajan, who studies how digital technologies affect the economy at NYU’s Stern School of Business. And Puneet Shivam, president of Avendus Capital US, an investment bank, says: “The leaders in the AI cloud will become the most powerful companies in history.”

It’s not just Amazon, Google, and Microsoft that are pursuing dominance. Chinese giants such as Alibaba and Baidu are becoming major forces, particularly in Asian markets. Leading enterprise software companies including Oracle, Salesforce, and SAP are embedding machine learning into their apps. And thousands of AI-related startups have ambitions to become tomorrow’s AI leaders.

Merijn Hos

Who will be the winners?

Amazon, Google, and Microsoft all offer services for recognizing faces and other objects in photos and videos, for turning speech into text and vice versa, and for doing the natural-language processing that allows Alexa, Siri, and other digital assistants to understand your queries (or some of them, anyway).

So far, none of this activity has resulted in much in the way of revenue; none of AI’s biggest players bother to break out sales of their commercial AI services in their earnings calls. But that would quickly change for the company that creates the underlying technologies and developer tools to support the widespread commercialization of machine learning. That’s what Microsoft did for the PC, by creating a Windows platform that millions of developers used to build PC programs. Apple did the same with iOS, which spawned the mobile-app era.

Google jumped out to the early lead in 2015, wooing developers when it open-sourced TensorFlow, the software framework its own AI experts used to create machine-learning tools. But Amazon and Microsoft have created similar technologies since then; they even joined forces in 2017 to create Gluon, an open-source interface designed to make machine learning easier to use with or without TensorFlow.

All three continue to work on ways to make machine learning accessible even to total AI novices. That was the idea behind Amazon’s SageMaker, which is designed to make building machine-learning apps not much more complicated than creating a website. A few weeks after SageMaker was announced last November, Google introduced Cloud AutoML. A company can feed its own unique collection of data into this technology, and it will automatically generate a machine-learning model capable of improving the business. Google says that more than 13,000 companies have asked to try Cloud AutoML.

“There are 20 million organizations in the world that could benefit from machine learning, but they can’t hire people with the necessary background,” says Jeff Dean, head of Google Brain. “To get even 10 million of them using machine learning, we have to make this stuff much easier to use.”

So which of the Big Three is best positioned to win that all-important first-mover advantage? All have immense strengths and few obvious weaknesses.

Take Microsoft. It’s been doing breakthrough work on AI problems such as computer vision and natural-language processing for two decades. It’s got access to massive amounts of valuable data to inform its Azure cloud, including content from Bing, LinkedIn, Skype, and the more than a billion people who use Microsoft Office. Simply put, no other company knows more about what it takes to sell, or help other developers sell, software to businesses and other organizations.

Sounds pretty good, until you read Google’s résumé. It’s considered the R&D leader in AI. It’s led the way in trying to apply AI to truly ambitious problems, notably building self-driving cars. It’s developed its own line of chips to run its machine-learning infrastructure.

Ever since handing over TensorFlow, it’s been the champion of the open-source crowd. And it may have access to more data than any other company, thanks to its search engine, which provides a detailed picture of our collective interests and desires. “They’re in the best position by a long shot,” says Alexander Wang, the 20-year-old founder of an AI startup called Scale. “They have tons of data they could monetize, and the best machine-learning researchers in the world.”