The Grad Student Who Couldn't Stop Being Annoyed
In a UC Berkeley computer science lab sometime around 2016, Robert Nishihara kept running into the same wall. He was doing AI research - interesting work, the kind that could matter - but the actual research kept getting displaced by something more tedious: managing clusters, shuffling data, wrestling with distributed systems that weren't built for machine learning. He wasn't writing AI. He was wrangling infrastructure.
So he and his labmates - Philipp Moritz and advisor Ion Stoica - built something to fix that. They called it Ray. It was meant to be a research tool. It became the distributed computing engine that now runs inside OpenAI, Apple, Uber, Amazon, Pinterest, Canva, and Instacart. Instacart used it to train models on 100x more data. Canva cut cloud costs in half.
"We were doing AI research, but we were bottlenecked by the tooling. We found ourselves spending all of our time managing clusters, wrangling data, and solving distributed systems challenges." - Robert Nishihara
The Ray paper landed at USENIX OSDI 2018, one of the top systems conferences in computer science. It described a framework capable of handling over 1.8 million tasks per second. By then, the vision had expanded: what if you could make distributed computing invisible to the developer? What if Python programmers could just write Python, and the scaling happened underneath?
In 2019, Nishihara, Moritz, and Stoica left the lab to start Anyscale - the commercial company built around Ray. The timing was either very early or perfectly calibrated, depending on how you look at it. The AI infrastructure wave that now looks obvious wasn't obvious at all when they started. They were building for a future they had to make people believe in.
It worked. Anyscale raised $259.6M across multiple rounds, reaching a $1B+ valuation. But the more interesting inflection point came in July 2024, when Nishihara stepped back from the CEO seat - not under pressure, but by suggestion. At a board meeting roughly six months earlier, he had told the board that Anyscale needed a different kind of leader to push toward the next revenue milestone. Keerti Melkote, founder of Aruba Networks, became CEO. Nishihara moved into a product-focused role.
That move - a founder proactively nominating himself out of the top job because the company needed it - is not common. It's the kind of self-awareness that gets taught in business school but rarely practiced. For Nishihara, it was just arithmetic: he cared more about what Anyscale built than about what his title said.
Now he watches the AI compute landscape shift in real time. His thesis, expressed in a 2025 post on X: the era where compute primarily means training is ending. The next era spends compute on data - generation, annotation, curation. "I expect to see the money & compute spent on data processing grow to match and exceed the money on pre-training," he wrote. He's also watching reinforcement learning closely enough to hire aggressively for it at Anyscale.
He grew up in the Bay Area, skateboarding and thinking about mathematics - the kind of kid who found uncountable infinities genuinely interesting rather than abstractly impressive. He went to Harvard for math, then Berkeley for a PhD in computer science, working under Michael Jordan and Ion Stoica in the AMPLab and later the RISELab. Along the way he interned at Google, the NSA, Facebook, Microsoft Research, and Jane Street - five very different organizations that probably gave him five very different definitions of scale. He completed the SF Half Marathon and wrote about it like a person who had surprised himself: "A goal that once felt out of reach is now complete."
The thing Ray did - and the thing Anyscale continues to try to do - is collapse the distance between an idea in a researcher's head and code actually running across a cluster. That gap, invisible to most people, used to eat weeks of a researcher's time. Nishihara built infrastructure so that other people could think instead of wrangle. He's still at it.