Summarize this article for a busy CEO:

NAME_1 is an anomaly in the tech industry. He grew up poor in a small Virginia town not known for producing C-suite technology executives, and eventually earned a PhD in computer science from the University of Virginia.

In his spare time, he constructs leather bags, backpacks — a lot of them, from the looks of his Instagram page — and guitar picks and tools. His Bay Area workshop, which includes laser cutters, drill presses, and industrial sewing machines, is legendary in Silicon Valley.

On paper, he seemed like an unlikely candidate to make Microsoft the leader in artificial intelligence. The field is full of PhDs from MIT, Stanford, and Carnegie Mellon.

“The nice thing about NAME_2 is he's not bothered with any of that,” said NAME_3, a venture capitalist and a friend of NAME_1’s. “He doesn't seem to need the reinforcement. He's sort of independent of what I would characterize as the mainstream, accepted way of doing things, which lets him do stuff like what he did at Microsoft.”

Pinterest’s head of engineering, NAME_4, who has been having breakfast with NAME_1 for years, said his friend could see what would happen with AI far ahead of anyone else.

“He's always just a great guy to bounce ideas off of like, ‘hey, we're going this way, we're thinking about combining these two things together.’ Most of the time, he's already thought about it or knew people who thought about it,” NAME_5 said.

NAME_1 was an engineer at Google before joining LinkedIn. By the time LinkedIn was acquired by Microsoft in 2017, NAME_1 was senior VP of engineering and operations. After the deal, Microsoft CEO NAME_6 named NAME_1 CTO of the parent company.

At the time of the acquisition, hype around AI had turned to disillusionment. While the technology was everywhere, used to automate processes in just about every industry, consumer applications like virtual assistants and chatbots had failed miserably. Even autonomous driving, once considered right around the corner, had turned into a far-off goal.

But soon, OpenAI was about to make a big bet on “transformer models,” then a new kind of artificial intelligence technique that was eventually used to power the company’s ChatGPT and DALL-E products.

In the past, AI models always plateaued in capability after they reached a certain size. In theory, these transformer models would break the mold and keep “learning” with more and more data.

But that was, by definition, a hypothesis. Testing it was a big technical challenge that involved spendin