The city-by-city playbook made sense in 2016. ⚡️
It doesn't anymore.
Autonomous systems need edge cases from all over the world, not just one city at a time.
Our May Progress Update dropped:
📹>176K Hours of Multi-Camera Footage
💹>6.78B $NATIX Staked
💎VX360 HODL Clubs launched
📺How video turns into intelligence
🌐Physical AI's geography problem
Full recap👇
https://t.co/Lx2SDWnIi4
Raw footage is only the starting point.
NATIX turns real-world visual data into structured spatial intelligence by extracting road signs, lane markings, road geometry, and infrastructure assets at scale.
This is how roads become machine-readable. 🌎
If an AV can drive in New York, it doesn't necessarily know how to drive in Tokyo.
Different roads mean different driving behaviors. 🛣️
The only way to bridge that gap before sending autonomous cars into the real world is to have enough training data.
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https://t.co/VMW26cP318
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This is where NATIX comes in.
Our decentralized camera network captures real-world driving data across countries, climates, and driving cultures.
That is how Physical AI gets the geographic coverage it needs to scale. 🌎
Autonomous driving is not just an algorithm race.
It is a geography problem. 🌎
Roads, signs, weather, and driving culture change everywhere.
That is why global data coverage matters 👇
https://t.co/nJdfEKHkqp
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Nowadays, AV systems are deployed city-by-city, but this kind of autonomy does not scale.
Every new market means new roads, edge cases, maps, and tuning. 🚗
The future needs a data layer that can teach a vehicle how to drive in a different geography before it gets there.
World models need more than pixels.
They need motion, context, uncertainty, and all the edge cases that happen outside the lab.
That is why real-world video matters 📹
Some edge cases we can deal with, but what about the ones we have never encountered? 🤡🐎
Autonomy has a long way to go until it catches up to the unpredictability of the real world.
Luckily, NATIX is on it 🚗
Raw video isn't a dataset.
It has to be ingested, cleaned, and tagged before it can train anything meaningful.
That pipeline is what separates footage from fuel. ⚡️
Cameras. HD map. Estimation module. Algorithms. AV system.
That's the traditional stack. Each box is hand-engineered, glued together, and maintained forever.
End-to-end collapses it all into one model trained on video. The bottleneck moved from code to data. 👀
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This is why NATIX is building an open-source multi-camera World Foundation Model with Valeo.
The footage is the starting point. The pipeline is what turns it into intelligence. ⚡️
Autonomous driving is no longer a sensor problem. It is a data infrastructure problem. 🚗
Raw video is not a dataset. What a self-driving system learns depends almost entirely on what happens after the recording ends 👇
https://t.co/HwPR3co2rS
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The harder problem is the long tail. The rare scenarios that decide whether a system is safe.
You cannot schedule edge cases. You catch them at scale, across regions and angles. VLMs surface them. Multi-camera footage shows the full scene. 🔍