If you’re like me, you can love technology and still worry about what it’s doing to us.

If that tension feels familiar, this episode is for you.

Today’s guest is Glen Slater, a Jungian psychologist and the author of Jung vs. Borg (as in cyborg).

This book completely blew my mind. It’s officially the most highlighted book in my Kindle.

It explores what increasingly feels like an inevitable trajectory: the deeper merging of human beings and machines, and what happens to the psyche as more of our attention, identity, and inner lives become mediated by technology.

On one hand, it’s one of the more unsettling books I’ve read. On the other, it’s one of the more hopeful, because Glen lays out a path for preserving the best parts of the human experience while using technology to expand consciousness (rather than diminish it).

In this conversation, we get into:

* What Jungian psychology can teach us about the digital age

* Why technology may be disrupting our “inner ecology”; meaning, emotion, imagination, embodiment, and self-knowledge

* Why technology is not neutral and how the tools we build can ultimately reshape us

* The myth of progress and why technological advancement alone does not guarantee human advancement

* Why embodiment may be essential to consciousness

* Whether AI itself could help us become more self-aware

This ended up being one of the most thought-provoking conversations I’ve had.

You’ll leave with a few potential answers to the core question we explore here on Dream Machines: how do we keep advancing technologically without losing the most nourishing parts of the human experience?

A thread we’ll continue to pull, together.

So with that, enjoy my conversation with Glen Slater.

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Today’s episode is brought to you by… Bifrost.AI

Bifrost is a leading simulation and evaluation platform for Physical AI.

It helps robotics teams build high-fidelity simulations, test models against the edge cases that are hard to capture in the real world, and generate perfectly labeled synthetic data along the way.

If you’re building robots and want to understand where your model fails before it reaches the field, visit bifrost.ai.

World models have become one of the most hyped frontiers in AI — and for good reason. The technology is reaching a tipping point, it’s attracting some of the world’s best AI talent, and many believe world models could be the unlock for both AGI and general-purpose robotics.

But ask five people what a “world model” actually is, and you’ll probably get five different answers.

That’s because there isn’t one approach. There’s a whole spectrum of world models being built today, each trying to understand and simulate the physical world in a different way.

To cut through the ambiguity, I sat down with Matthias Niessner, CEO of SpAItial, a London + Munich-based startup building one of the leading world models for physical AI.

In this episode, we cover:

The spectrum of world models — from pixel-first video models, to 3D spatial representations, to systems that learn physics and cause-and-effect in latent space.

Why video models may be hitting a wall — particularly around spatial consistency, long time horizons, and real-time interaction.

How world models are actually trained — including the role of massive video datasets, data curation, multimodal signals, and learned representations.

“Looks right” vs. “behaves right” — and why generating a photorealistic world is very different from generating one that behaves according to real physics.

Why 3D consistency matters — and why a world that changes as you move through it isn’t really a persistent model of the world.

How physics enters the picture — from relatively simple rigid-body dynamics to far harder problems like fluids, deformation, and objects shattering.

The limits of video game data — and why learning from existing physics engines can only get you so close to reality.

World models for robotics simulation — including a future where a photo of a home, factory, or workspace can become a training environment for a robot.

Closing the sim-to-real gap — and why increasingly realistic learned simulations could fundamentally change how robots are trained.

The path to general-purpose robotics — and how better models of the physical world could accelerate the arrival of machines that can operate reliably almost anywhere.

I learned a ton in this episode — and walked away even more optimistic about how quickly we may be approaching general-purpose robotics.

So with that, I bring you Matthias Niessner.

Today’s guest is Will McDonald, co-founder and Chief Product Officer of Miris.

Miris is rethinking how high-fidelity 3D content gets delivered at scale. Their thesis: the future of 3D depends entirely on simplifying distribution.

In that vein… we talk about why pixel streaming falls short, how Miris uses volumetric data instead, and why that matters for everything from e-commerce and gaming to robotics and simulation.

Will and I worked together for many years at AWS, so we also dive into lessons learned from Amazon’s customer obsessed culture, what it gets right, and experience of going from big tech back into startups.

If you care about spatial computing and simulation, and where the 3D internet is heading, this one’s worth the time.

With that I bring you, Will McDonald.