Google Senior Fellow Jeff Dean, who works on the Google Brain team, recently highlighted AutoML (for machine learning), a project aimed at using AI-empowered machines to build other AI machines, removing humans from the equation. The need for AI algorithms grows as its capabilities are becoming important to a wide range of industries. But only an estimated 10,000 people worldwide have the education, expertise and ability to construct those algorithms, and Facebook, Google and Microsoft pay millions of dollars for them.

The New York Times reports that, “the shortage isn’t going away anytime soon, just because mastering these skills takes years of work.” That’s why companies are “developing all sorts of tools that will make it easier for any operation to build its own AI software, including things like image and speech recognition services and online chatbots.”

“We are following the same path that computer science has followed with every new type of technology,” said Microsoft vice president Joseph Sirosh. “We are eliminating a lot of the heavy lifting.”

All these companies are also “selling cloud-computing services that can help other businesses and developers build AI.” With AutoML, Google plans to help companies “build systems with artificial intelligence even if they don’t have extensive expertise,” said Dean, who “estimated, no more than a few thousand companies have the right talent for building AI, but many more have the necessary data.”

“We want to go from thousands of organizations solving machine learning problems to millions,” he said. Google is well positioned to accomplish this because it hired “such a large portion of the world’s top AI researchers.”

If AutoML succeeds, engineers will be able to build algorithms that learn tasks on their own. “Computers are going to invent the algorithms for us, essentially,” said University of California Berkeley professor Pieter Abbeel. “Algorithms invented by computers can solve many, many problems very quickly — at least that is the hope.”

The Guardian reports that Ph.D. students are lured away from their studies by six-figure salaries at companies like Apple. A Guardian survey of the U.K.’s top ranking research universities shows a brain drain for AI experts, who are being recruited by only a few companies.

That’s a problem, said Imperial College professor Maja Pantic, “because only a diffusion of innovation, rather than its concentration into just a few companies, can mitigate the dramatic disruptions and negative effects that AI may bring about.” It will also create “a huge pay gap” between AI experts and “the rest of the workforce.”

Imperial College researcher Murray Shanahan had an offer to join DeepMind, “Google’s London-based artificial intelligence group,” but despite the many benefits, he turned the offer down. “The potential impact on academia of the current tech hiring frenzy was one of the issues that bothered me,” he said.

Steven Turner, who joined Amazon Web Services in Cambridge, reports that he left academia because he wanted to “work on real problems rather than more theoretical concepts” — and he ended up finding the Amazon culture “more vibrant than in academia.”

“I personally think that having a greater focus on culture and social interaction to ensure researchers don’t feel as isolated as they can do would have a significant impact on retention,” he said.