NVIDIA: Rewriting the Future of Health and Entertainment

In 2016, rendering production frames for a Toy Story film pushed the limits of GPU memory. Ten years later, NVIDIA’s Blackwell GPUs can handle 99% of Pixar’s shots with room to spare. That’s not just faster rendering, it’s a profound shift in what’s possible for storytellers.

NVIDIA’s Nemotron open model suite brings that same leap to healthcare by powering the discovery of new drugs, medical devices and digital health tools. While generic models are powerful on their own, real clinical intelligence is needed to train and refine the models so it can work in messy, real world conditions. 

As someone who used to run half and full marathons, I know the toll firsthand. I twisted my ankles (my Running Room instructors were surprised that it didn’t sideline me) and battled shin splints at the end of each race. Back then, recovery meant foam rolling and replacing shoes because the cushioning was gone; pickleball players deal with the same situation. If Syntilay’s AI driven 3D printing had existed, a quick foot scan could have created a custom recovery footwear tailored to my needs (exact geometry, pressure points, and movement patterns), instead of relying on generic solutions.

Now imagine linking this with Abridge’s listening technology, powered by NVIDIA’s Nemotron models. The AI doesn’t transcribe the conversation between doctor and patient; it becomes an active support tool. In my situation, it would analyze my physio session and recommend the best 3D printed lattice structure so my new recovery shoes would have the custom cushioning needed to support my feet after the long runs or pickleball games.

Finally, let’s take this further with NVIDIA’s Omniverse and data analytics. With user permission to leverage my data, the system could create truly personalized experiences. Sitting at home in my custom recovery shoes, I turn on a streamer and see relevant ads such as electrolyte freeze pops, foam rollers, shoes or clothing that’s targeted to my profile and exact location. Even better? A one click link showing the real time inventory at my local store.

That’s the future I see in GeoAI x media futures where infrastructure, personalization, storytelling comes together to help us recover, move and live better.

What’s missing?

A duck standing on the edge of a wooden fence, with green foliage in the background.

Everyone is ignoring the one thing in the room when it comes to AI videos. 

That thing (no, it’s not software nor GPUs)? 

Geography.

That’s why Chinese AI video companies (Seedance, iQIYI) are winning in the short term. By using cultural geography to create content that resonates with Chinese audiences at home AND the Chinese diaspora overseas.

In Hollywood, actors like Matthew McConaughey have seen the AI future. He’s trademarked his voice and likeness with the USPTO to create (in his words) “a clear perimeter” and trademarking with the USPTO while investing in ElevenLabs which produces a Spanish-language version of his newsletter Lyrics of Livin’.

That’s geographic intelligence applied to personal brand.

And that’s why geographic intelligence is the missing layer.

The AI Trust Gap: Why Your Zip Code Matters More Than You Think

It’s 8pm somewhere in the world and two Tubi TV subscribers are logging into their accounts. One is from Canada, the other in Australia. Each expects a personal 1-1 interaction with the Tubi AI to find something to watch. What they don’t see? That the Tubi AI is delivering that exact same personal experience to their 100 million subscribers at the same time.

Even as AI chatbots grow in popularity to help subscribers find the movies and shows they want to watch, their trust in AI chatbots hasn’t kept pace. Why? The LLMs are trained on data that goes stale fast. Ask a chatbot what to watch tonight and it might give you an answer based on information that’s months old. That’s why 50% of viewers still trust traditional search over AI, a 2 to 1 gap.

What a Tubi TV subscriber wants to watch in Mexico City is different from the subscriber in Toronto, Canada. Not only are the content options different, from licensing, language, cultural context and local events (example, Cinco de Mayo, that shapes what that Mexican subscriber wants to watch on that day), but the first party (1P) data is siloed or stale. And the AI chatbot doesn’t know what to recommend to the subscriber. The result? The same generic recommendation is given to both of them, that misses the mark entirely. That’s why the trust gap exists. Both subscribers are fact checking the AI recommendation, not because it was entirely wrong, but because it wasn’t right for them, where they are, right now.

The AI chatbot doesn’t know if you’re in Mexico City or Toronto. It doesn’t know it’s Cinco de Mayo. And right now, that gap between what the AI assumes and what you actually want is costing streaming platforms your trust. Here’s my question to you:

Do you use an AI chatbot to find something to watch? And do you trust it? Let me know in the comments.