5 million years – that's the evolutionary age of human language. 540 million years – that's the starting point of the Cambrian explosion sparked by vision and spatial perception.
In 2025 and 2026, when nearly every top Silicon Valley lab was fiercely competing on language models, Professor Fei-Fei Li of Stanford University and founder of World Labs repeatedly raised a question that forced the industry to look up: If AI can only talk and look at pictures, it will never truly "understand" this world.
In three key interviews – the a16z Podcast in June 2025, the Cisco AI Summit in February 2026, and the in-depth 1-hour 19-minute Lenny's Podcast conversation released on May 22, 2026 – she systematically elaborated on a judgment that is being rapidly validated: Spatial Intelligence is the next frontier of AI.
Her statements in the a16z dialogue about "creating infinite universes" and "living in a multiverse," along with her views in Lenny's Podcast that "world models are the next frontier" and "AGI is more of a marketing term," have recently been widely reposted again on X.
"We Are Missing a World Model"
According to a16z partner Martin Casado, during a lunch meeting in Silicon Valley, a table full of AI practitioners was excitedly discussing large language models. Sitting at the other end of the table, Fei-Fei Li suddenly turned and asked him:
"Do you know what we are missing? We are missing a world model."
Casado, an early investor in World Labs and a long-time friend of Li's from her Stanford days, recalled that moment: "Everything clicked." He had independently reached a similar conclusion coming out of extensive investment in the image field: language is not the end of the story.
But Li's thinking on this issue goes back much further than most.
In April 2024, she gave a 15-minute TED Talk, using evolution as her starting point: The appearance of trilobites 540 million years ago allowed life to "see" the world for the first time. The birth of vision ignited an evolutionary race of intelligence, the nervous system began to develop, animals became active, and intelligence emerged. Language is merely a very recent product of this long race.
This judgment was repeatedly reinforced in the three interviews. At the Cisco AI Summit, her statement was more direct:
"Language's history is only about 500,000 years old. But 1.5 billion years ago, animals began to perceive light and touch their environment. The ability to understand, reason, interact, and navigate in the real 3D, 4D physical world is fundamental, as important as linguistic intelligence."
Li is not negating the value of linguistic intelligence. Her core argument is: Language is essentially a "lossy" way of encoding the world.
In the a16z interview, Casado conducted a thought experiment: Blindfold yourself, describe a room using language, then try to complete a task – your chance of success is extremely low. Because language's description of reality is always rough. Remove the blindfold, your brain instantly reconstructs the 3D space, and you can operate, touch, and move.
Li supplemented with a more extreme example from scientific history: Rosalind Franklin's X-ray diffraction photo of DNA was a flat, two-dimensional image, showing a pattern that looked like a cross with diffraction. But Watson and Crick reasoned from that two-dimensional photo to deduce the three-dimensional double-helix structure of DNA. "That structure cannot be two-dimensional. You cannot deduce that structure with two-dimensional thinking."
"If you observe human intelligence, much of it is beyond the scope of language. Language is a lossy way of capturing the world. Pure generative 'language' does not exist in nature; we look around, there are no ready-made sentences or words, yet the entire physical, perceptual, visual world exists."
This is a perspective easily overlooked: most capabilities of current large models are built on a format of information compression that is inherently lossy. In Lenny's Podcast, she used a more mundane test to puncture this illusion:
"Today, you take a model, give it a video clip showing a few office rooms, and ask the model to count the number of chairs. This is something a toddler can do, but AI cannot."
Not to mention deducing physical laws from celestial motion: "Let's give AI all the data, including modern instrument data that Newton didn't have, and ask it to create a set of 17th-century equations about the laws of object motion. Today's AI cannot do that."
Marble: Orders of Magnitude Smaller Than GPT-5
Pushing this judgment into a product is World Labs' first-generation model, Marble, released at the end of 2024.
At the Cisco AI Summit, Li detailed Marble's technical positioning: receiving text, images, video, or simple 3D inputs, and generating a "fully navigable, interactive, and permanently consistent 3D world." She specifically emphasized that this is fundamentally different from video generation models like Sora; environments generated by Marble possess geometric structure, not pixel animations that "look like" video.
In Lenny's Podcast, she used Plato's allegory of the cave for a deeper explanation: Prisoners are tied to chairs, only able to see two-dimensional shadows projected on the wall, but the real drama unfolds in the three-dimensional space behind them. Video models are those shadows, while spatial intelligence aims to create and reason about the real world behind those shadows.
A comparison: GPT-5's training compute is roughly on the order of 10^26 FLOPS, while Marble is several orders of magnitude smaller in scale. The reasons are two-fold: data acquisition difficulty is completely different (high-quality 3D physical data is extremely scarce), and this field is still in the early stages of the "scaling law upward curve."
In Lenny's Podcast, she further explained why robot learning cannot simply replicate the "bitter lesson" of language models. There is a famous assertion in AI: simple models with massive data will eventually surpass complex ones. But "language models have a perfect setup: the training data is words, and the output is also words." In robotics, "you want actions, but the training data lacks actions in the 3D world." This fundamental misalignment between training objectives and data form is the core challenge of robot learning.
World Labs employs a hybrid data strategy: internet-scale text, images, and video, plus simulation data, plus real-world captured data. Li admits, "We are still in the relatively early stages of exploring model architectures," but she expects "the next few years will be very exciting."
Right after, in February 2026, World Labs completed a $1 billion funding round, with participation from NVIDIA, AMD, a16z, valuing the company at around $5 billion, up from $1 billion a year earlier. In April, the team open-sourced the 3D Gaussian splatting rendering engine Spark 2.0, capable of real-time rendering of hundred-million-polygon 3D scenes in web browsers, shifting from a closed-source product to a dual-track strategy of "product + open-source ecosystem." The technical barrier for spatial intelligence is being rapidly lowered.
In Lenny's Podcast, Li also rarely revealed the hardships of entrepreneurship: "If I could whisper one thing to myself 18 months ago: 'The intensity of competition in this field, both technologically and for talent, far exceeds your imagination.'"
Infinite Universes and Multiverses
What really made that a16z interview go viral repeatedly on X was Li's statement about "infinite universes":
"In the entire history of human civilization, we have all lived together in one 3D world. Only a handful of people have been to the moon, but very few. And this technology makes digital virtual worlds incredibly rich. Suddenly, we can actually create infinite universes, some for robots, some for creativity, some for social interaction, some for travel, some for storytelling. Suddenly, we are able to live in a multiverse; the space for imagination is infinite."
Casado provided a more concrete technical explanation: from a single two-dimensional photo, the model can generate a complete 360-degree 3D representation, including the back of a table. You can manipulate, measure, stack—anything you can do in space can be achieved.
This is not science fiction. In the two interviews, Li listed applications where Marble is already being used:
• Game developers used early versions to create games
• A virtual production team collaborating with Sony reduced film production cycles by 40 times
• NVIDIA and multiple academic labs used Marble to train robots
• Architects and designers used it for interior design
• Clinical researchers created personalized immersive trigger environments for patients with OCD, acrophobia
• Someone used it to generate personalized yoga training spaces
The last application was particularly surprising. Li mentioned at the summit that OCD patients are triggered by very specific scenes, "for example, personally I am troubled by piles of dirty laundry, but everyone's trigger points are different." In Lenny's Podcast she added that after release, a friend called her overnight asking if Marble could be used to treat acrophobia. Building physical environments is extremely costly, while Marble only needs a prompt to generate various environments in minutes.
Plato's allegory of the cave is also the best entry point for understanding the 2D vs. 3D divergence.
Li used this allegory to explain: Prisoners tied to chairs can only see two-dimensional shadows projected on the wall. Current language models and video models are essentially those shadows, guessing 3D from 2D. The ambition of spatial intelligence is to create, reason about, and interact with the real world behind those shadows.
In terms of technical roadmap, she drew a clear boundary with a concise comparison:
"A car can be seen as a square robot moving on a two-dimensional plane, its goal is not to hit anything. A robot is a three-dimensional entity operating in a three-dimensional world; the goal of a general-purpose robot is to touch objects without breaking them. This is a higher-dimensional problem."
She also provided a timeline from personal experience: In 2006, she helped create the first self-driving car to travel 138 miles in the desert, predicting autonomous vehicles in 20 years. It wasn't until 2025 that Waymo began operating on city streets at scale.
"Seeing the North Star doesn't mean the journey will be short."
Casado added a more business-savvy observation in the a16z conversation: In the autonomous driving sector alone, the industry invested about $100 billion over 20 years to get where it is today. "Our original roadmap was to solve the world navigation problem first, but it turned out to be extremely difficult."
Li even shared a personal experience in the a16z interview to strengthen the point: About five years ago, she lost stereoscopic vision for several months due to a corneal injury. "Even though I knew very well how big my car was, roughly knew the size of my neighbor's parked car, and I had driven this road many years, I could not judge the distance between my car and the parked car very well. I could only drive at ten miles per hour to avoid scratching other cars."
A lifelong researcher of visual intelligence used her own firsthand struggle after losing depth perception to answer the question "why 3D is irreplaceable."
The Double-Edged Sword of Technology and the Measure of Civilization
Between technological optimism and doomsday rhetoric, Li chose a more restrained and actionable stance. She clearly expressed concern about polarized discourse at the Cisco AI Summit:
"The discussion online often tends to be black and white: either full-blown technological utopianism, ignoring that technology is a double-edged sword; or doomsday talk, as if human survival is at risk at any moment. For a technology so profound for human civilization, this way of discussion is irresponsible."
She didn't stop at criticism but offered a quantifiable anchor for value: electricity.
"If we rewind more than a hundred years, imagine how people then defined the success of electricity. I hope the vision then was: schools lit up, homes warm, machines empowered for industrialization, thereby extending human lifespans, allowing more children to be educated."
Then she applied this anchor to AI: "The definition of success should be that civilization becomes more beautiful, and civilization is composed of every individual pursuing happiness, prosperity, and dignity. That is the definition of success for AI and every technology."
At the end of Lenny's Podcast, she brought this concern down to specific people. She said wherever she goes, she is asked the same question: If I am a farmer, nurse, musician, will AI replace me? Her answer: "Ultimately, AI is about people. No technology should strip away human dignity. Human dignity and autonomy should be at the core of the development, deployment, and governance of every technology."
Looking back at the three interviews, a clear thread emerges.
Fei-Fei Li's thinking on spatial intelligence is not a rebellion against the wave of large models, but an extension built upon it. She saw the limits of language models earlier than most – what a lossy information compression format can do is ultimately limited. The problem spatial intelligence aims to solve is: evolving AI from "talking about the world" to "understanding the world," and ultimately to "acting in the world."
The World Labs team has about 30 people and has raised over $1 billion. Marble is the first-generation product, far smaller in scale than top language models. The scarcity of 3D data and the early state of model architectures determine this will not be a path achieved overnight. But Li said another thing in Lenny's Podcast, perhaps the best annotation for this patience:
"Our brains consume only about 20 watts, dimmer than any light bulb in the room, yet can do so much. The more I work in AI, the more I respect humans."
540 million years of evolution gave carbon-based life this 20-watt spatial intelligence. AI's evolution is being compressed to a few years.
Li did not give a timeline in the three interviews. She just repeatedly returned to that judgment extracted from evolution: perception precedes language, space precedes symbols. What is happening in Silicon Valley labs, Stanford labs, and World Labs offices is not a technological iteration, but an accelerated replay of evolution. (This article was first published on Titanium Media APP, author | Silicon Valley Tech News, editor | Zhao Hongyu)
Appendix: The text transcripts of the above three interviews are archived at 【ima Knowledge Base】 Fei-Fei Li Interviews https://ima.qq.com/wiki/?shareId=3f1d4b4c0d6cb2aeca250e2c5d068390e2d45895816ad607309820e25cb2e9c5
