Former Stanford Vision Lab graduates founded or lead four of AI's most closely watched organizations, including World Labs, which raised $1.23 billion in spatial intelligence funding between 2023 and 2026. The alumni spread across commercial AI, academic computer science, and independent research institutes reflects both the breadth of Fei-Fei Li's research program and the field's rapid institutional diversification. (Bloomberg)
Justin Johnson and the $1.23 billion spatial intelligence bet
Justin Johnson completed his doctoral work under Li at Stanford and later served as an assistant professor at the University of Michigan, where he worked on visual reasoning, image synthesis, and 3D scene understanding. In 2023, he co-founded World Labs with Li, alongside Christoph Lassner and Ben Mildenhall, whose work on neural radiance fields gave the company a strong foundation in photorealistic 3D reconstruction.
World Labs emerged from stealth in September 2024 with a $230 million seed round that valued the company at $1 billion, backed by Andreessen Horowitz and Radical Ventures, among others. In January 2026, Bloomberg reported the company was in talks to raise additional capital at a $5 billion valuation, a fivefold increase in roughly fifteen months. The Series A, announced in February 2026 at $1 billion, brought in strategic investors including Nvidia, AMD, and Autodesk but closed without a publicly disclosed valuation.
World Labs launched Marble, its spatial intelligence product, in limited beta in November 2025, with the commercial release following in February 2026 alongside the funding announcement. Li described the company's mission at Bloomberg Tech 2026 in San Francisco: large world models that give AI systems a persistent, three-dimensional understanding of their environment rather than a flat, text-based one. StartupHub.ai data shows World Labs is the most heavily funded spatial intelligence startup in our database, having raised $1.23 billion since its founding.
Olga Russakovsky: from ImageNet benchmarks to Princeton and AI4ALL
Russakovsky completed her Stanford PhD in 2015 under Li. Her doctoral work was central to building and running the ImageNet Large Scale Visual Recognition Challenge (LSVRC), the annual benchmark competition from 2010 through 2017 that measured progress in object detection and classification. The final years of the challenge, in which AI models surpassed human-level accuracy, are now cited as a founding moment of the modern AI era.
