As someone who has gone pretty deep in to robotics over the last 9 years I skipped right to the physical AI portion, and wasn't impressed.
This has been stated on HN in most robotics threads, but the core of what they show, once again, is content generation, a feature largely looking for an application. The main application discussed is training data synthesis. While there is value in this for very specific use cases it's still lipstick ("look it works! wow AI!") on a pig (ie. non-deterministic system being placed in a critical operations process). This embodies one of the most fallacious, generally unspoken assumptions in AI and robotics today - that it is desirable to deal with the real world in an unstructured manner using fuzzy, vendor-linked, unauditable, shifting sand AI building blocks. This assumption can make sense for driving and other relatively uncontrolled environments with immovable infrastructure and vast cultural, capital and paradigm investments demanding complex multi-sensor synthesis and rapid decision making based on environmental context based on prior training, but it makes very little sense for industrial, construction, agricultural, rural, etc. Industrial is traditionally all about understanding the problem or breaking it in to unit operations, design, fabricate and control the environment to optimize the process for each of those in sequence, and thus lowering the cost and increasing the throughput.
NVidia further wants us to believe we should buy three products from them: an embedded system ("nano"), a general purpose robotic system ("super") and something more computationally expensive for simulation-type applications ("ultra"). They claim (with apparently no need to proffer evidence whatsoever) that "all robotics" companies need these "three computers". I've got news for you: we don't, this is a fantasy, and limited if any value add will result from what amounts to yet another amorphous simulation, integration and modeling platform based on broken vendor assumptions. Ask anyone experienced in industrial, they'll agree. The industrial vendor space is somewhat broken and rife with all sorts of dodgy things that wouldn't fly in other sectors, but NVidia simply ain't gonna fix it with their current take, which for me lands somewhere between wishful thinking and downright duplicitous.
As for "digital twins", most industrial systems are much like software systems: emergent, cobbled together from multiple poor and broken individual implementations, sharing state across disparate models, each based on poorly or undocumented design assumptions. This means their view of self-state, or "digital twin", is usually functionally fallacious. Where "digital twins" can truly add value is in areas like functional safety, where if you design things correctly you avoid being mired in potentially lethally disastrous emergent states from interdependent subsystems that were not considered at subsystem design, maintenance or upgrade time because a non-exhaustive, insufficiently formal and deterministic approach was used in system design and specification. This very real value however hinges on the value being delivered at design time, before implementation, which means you're not going to be buying 10,000 NVidia chips, but most likely zero.
So my 2c is the Physical AI portion is basically a poorly founded forward-looking application sketch from what amounts to a professional salesman in a shiny black crocodile jacket at a purchased high-viz keynote. Perhaps the other segments had more weight.