What Comes After the LLM: Human-Centered AI, Spatial Intelligence, and the Future of Practice

In a recent episode of High Signal, we spoke with Dr. Fei-Fei Li about what it really means to build human-centered AI, and where the field might be heading next.

Fei-Fei doesn’t describe AI as a feature or even an industry. She calls it a “civilizational technology”—a force as foundational as electricity or computing itself. This has serious implications for how we design, deploy, and govern AI systems across institutions, economies, and everyday life.

Our conversation was about more than short-term tactics. It was about how foundational assumptions are shifting, around interface, intelligence, and responsibility, and what that means for technical practitioners building real-world systems today.

The Concentric Circles of Human-Centered AI

Fei-Fei’s framework for human-centered AI centers on three concentric rings: the individual, the community, and society.

At the individual level, it’s about building systems that preserve dignity, agency, and privacy. To give one example, at Stanford, Fei-Fei’s worked on sensor-based technologies for elder care aimed at identifying clinically relevant moments that could lead to worse outcomes if left unaddressed. Even with well-intentioned design, these systems can easily cross into overreach if they’re not built with human experience in mind.

At the community level, our conversation focused on workers, creators, and collaborative groups. What does it mean to support creativity when generative models can produce text, images, and video at scale? How do we augment rather than replace? How do we align incentives so that the benefits flow to creators and not just platforms?

At the societal level, her attention turns to jobs, governance, and the social fabric itself. AI alters workflows and decision-making across sectors: education, healthcare, transportation, even democratic institutions. We can’t treat that impact as incidental.

In an earlier High Signal episode, Michael I. Jordan argued that too much of today’s AI mimics individual cognition rather than modeling systems like markets, biology, or collective intelligence. Fei-Fei’s emphasis on the concentric circles complements that view—pushing us to design systems that account for people, coordination, and context, not just prediction accuracy.

Spatial Intelligence: A Different Language for Computation

Another core theme of our conversation was Fei-Fei’s work on spatial intelligence and why the next frontier in AI won’t be about language alone.

At her startup, World Labs, Fei-Fei is developing foundation models that operate in 3D space. These models are not only for robotics; they also underpin applications in education, simulation, creative tools, and real-time interaction. When AI systems understand geometry, orientation, and physical context, new forms of reasoning and control become possible.

“We are seeing a lot of pixels being generated, and they’re beautiful,” she explained, “but if you just generate pixels on a flat screen, they actually lack information.” Without 3D structure, it’s difficult to simulate light, perspective, or interaction, making it hard to compute with or control.

For technical practitioners, this raises big questions:

What are the right abstractions for 3D model reasoning?
How do we debug or test agents when output isn’t just text but spatial behavior?
What kind of observability and interfaces do these systems need?

Spatial modeling is about more than realism; it’s about controllability. Whether you’re a designer placing objects in a scene or a robot navigating a room, spatial reasoning gives you consistent primitives to build on.

Institutions, Ecosystems, and the Long View

Fei-Fei also emphasized that technology doesn’t evolve in a vacuum. It emerges from ecosystems: funding systems, research labs, open source communities, and public education.

She’s concerned that AI progress has accelerated far beyond public understanding—and that most national conversations are either alarmist or extractive. Her call: Don’t just focus on models. Focus on building robust public infrastructure around AI that includes universities, startups, civil society, and transparent regulation.

This mirrors something Tim O’Reilly told us in another episode: that fears about “AI taking jobs” often miss the point. The Industrial Revolution didn’t eliminate work—it redefined tasks, shifted skills, and massively increased the demand for builders. With AI, the challenge isn’t disappearance. It’s transition. We need new metaphors for productivity, new educational models, and new ways of organizing technical labor.

Fei-Fei shares that long view. She’s not trying to chase benchmarks; she’s trying to shape institutions that can adapt over time.

For Builders: What to Pay Attention To

What should AI practitioners take from all this?

First, don’t assume language is the final interface. The next frontier involves space, sensors, and embodied context.

Second, don’t dismiss human-centeredness as soft. Designing for dignity, context, and coordination is a hard technical problem, one that lives in the architecture, the data, and the feedback loops.

Third, zoom out. What you build today will live inside ecosystems—organizational, social, regulatory. Fei-Fei’s framing is a reminder that it’s our job not just to optimize outputs but to shape systems that hold up over time.