Credit Damien Maloney for The New York Times
SAN FRANCISCO — As the oracles of Silicon Valley debate whether the latest tech boom is sliding toward bust, there is already talk about what will drive the industry’s next growth spurt.
The way we use computing is changing, toward a boom (and, if history is any guide, a bubble) in collecting oceans of data in so-called cloud computing centers, then analyzing the information to build new businesses.
The terms most often associated with this are “machine learning” and “artificial intelligence,” or “A.I.” And the creations spawned by this market could affect things ranging from globe-spanning computer systems to how you pay at the cafeteria.
“There is going to be a boom for design companies, because there’s going to be so much information people have to work through quickly,” said Diane B. Greene, the head of Google Compute Engine, one of the companies hoping to steer an A.I. boom. “Just teaching companies how to use A.I. will be a big business.”
This kind of change is what keeps Silicon Valley going. When personal computers displaced mainframe computers, it opened the door not just for Apple, but for companies making PC software for business, games and publishing. In the networking and Internet revolutions, venture capitalists invested in these new computing styles, and another generation of companies was born.
Over the last decade, smartphones, social networks and cloud computing have moved from feeding the growth of companies like Facebook and Twitter, leapfrogging to Uber, Airbnb and others that have used the phones, personal rating systems and powerful remote computers in the cloud to create their own new businesses.
Believe it or not, that stuff may be heading for the rearview mirror already. The tech industry’s new architecture is based not just on the giant public computing clouds of Google, Microsoft and Amazon, but also on their A.I. capabilities. These clouds create more efficient and supple use of computing resources, available for rent. Smaller clouds used in corporate systems are designed to connect to them.
The A.I. resources Ms. Greene is opening up at Google are remarkable. Google’s autocomplete feature that most of us use when doing a search can instantaneously touch 500 computers in several locations as it guesses what we are looking for. Services like Maps and Photos have over a billion users, sorting places and faces by computer. Gmail sifts through 1.4 petabytes of data, or roughly two billion books’ worth of information, every day.
Handling all that, plus tasks like language translation and speech recognition, Google has amassed a wealth of analysis technology that it can offer to customers. Urs Hölzle, Ms. Greene’s chief of technical infrastructure, predicts that the business of renting out machines and software will eventually surpass Google advertising. In 2015, ad profits were $16.4 billion.
“In the ’80s, it was spreadsheets,” said Andreas Bechtolsheim, a noted computer design expert who was Google’s first investor. “Now it’s what you can do with machine learning.”
He added: “Better maps and photos is just the start. It’s going to be in life sciences, automobiles, everything.”
A number of start-ups are already aimed at the new architecture. A Mountain View, Calif., outfit called Mashgin uses “computer vision” to automate retail checkout. Up Highway 101 in San Mateo, a company called Alluxio is creating ways to make cloud-based A.I. work better. Last week, a San Francisco company called Mesosphere, which makes a way to operate among various corporate and public clouds, raised $73.5 million.
Microsoft and Amazon are racing Google to dominate the new architecture.
This week, Microsoft will kick off a conference in San Francisco that is expected to focus on ways machine-based intelligence can be used to analyze, among other things, “the Microsoft graph,” or all the data companies already have in the Microsoft products they’ve owned for decades.
Amazon last year announced its own machine-learning services, and it is amassing its own large repository of corporate data.
Hewlett Packard Enterprise, an older company struggling to find its way in the new landscape, was one of the investors in Mesosphere.
“When you are building predictive data, you don’t know what you are going to need next,” said William Hilf, a senior vice president at H.P.E. “If someone makes a bet in machine learning on Microsoft or Google, they may need to come down to their old data systems, too. We are building platforms to bridge among all of them.”
To Ms. Greene, all of the activity so far, along with the size and sophistication of computing, is small compared to what will happen when the world’s biggest businesses start leaning on the new A.I. technology.
“We may build an A.I. system to figure out all the ways businesses can use this,” she joked. “The relationship between big companies and deep machine intelligence is just starting.”