Somebody had to show that making a model bigger would actually make it better. Before that, it was a hunch. Jared Kaplan ran the numbers, turned the hunch into a curve you could plot, and the industry has been spending against that curve ever since.
He’s a co-founder of Anthropic, the company that makes Claude. If you’ve ever wondered why AI labs keep building data centers the size of small towns, his research is a large part of the answer.
Who they are
Kaplan spent the first 15 years of his career as a theoretical physicist. Quantum gravity, cosmology, the kind of questions that don’t come with a product roadmap. He’s been an associate professor at Johns Hopkins since 2012 (currently on leave). He joined OpenAI in 2019 as a research consultant. There he co-authored the scaling laws paper and worked on GPT-3 and Codex. In 2021 he left with six other OpenAI people to start Anthropic.
He’s the chief science officer there now. Anthropic raised $65 billion in May 2026 at a $965 billion valuation and said its run-rate revenue had crossed $47 billion. For a company that was worth $61.5 billion in early 2025, that’s a steep climb.
Why they matter
His scaling laws research showed that model performance improves predictably when you add more parameters, more data, and more compute. Not maybe. On a curve. That’s why labs got comfortable writing enormous checks for a single training run instead of guessing.
He helped build Constitutional AI. The model gets trained against a written set of principles instead of relying only on humans rating answers one at a time. A lot of Claude’s behavior comes from that, and it made alignment work something you can audit by reading a document.
Anthropic filed a confidential draft S-1 with the SEC on June 1, 2026. If it goes public, his research bets stop being a private matter. They start showing up in quarterly reporting.
What they’ve said or done
At Y Combinator’s AI Startup School in June 2025, Kaplan explained why he went into physics in the first place: “I wanted to figure out if we could build a faster than light drive.”
His mother wrote science fiction. He read it, got curious, and went to check the math on the fun part. It didn’t work out. But the habit stuck. Take a wild claim, find the equation, see whether the curve holds up. That’s more or less what he did to language models a couple of decades later, give or take.