Subnets / SN72
StreetVision by NATIX SN72
StreetVision by NATIX (subnet 72) is a Bittensor subnet held by 1,149 unique coldkeys. Its liquidity pool holds 6.1k τ, with the alpha token trading at 0.0021 τ. Roughly 4% of tracked holders are in profit — most bought in above today's price. Seller exhaustion is elevated: 88% of recent sellers have emptied their wallets, thinning the supply overhang. Full insider flow, whale tracking and the complete proprietary factor set for StreetVision by NATIX are available to SubnetStats subscribers.
ALPHA PRICE
0.0021 τ
IN TAO
HOLDERS
1,149
UNIQUE COLDKEYS
POOL DEPTH
6.1k τ
TAO IN POOL
7D MOMENTUM
-0.8%
ALPHA vs TAO
Project activity
Tesla's new robotaxi safety page counts 3.1M autonomous miles 🚕
The split says more than the total: 862K after dark, 113K in rain, 575K in rush hour.
Miles only count for the conditions they cover. Night, rain and traffic are where driving data earns its keep 🌧️ https://t.co/3G
KITTI came out in 2012 and gave self-driving research its first shared benchmark 🏁
BDD100K, nuScenes and Waymo Open followed. Today's world models need more than they were built for: recent multi-camera video from many places.
What they taught us 👇
https://t.co/SSKIOm4gxe
DePIN taught crypto to crowdsource maps, WiFi and compute.
DePAI points the same idea at physical AI: everyday people collect the real-world data robots and self-driving cars learn from, and get rewarded for it.
NATIX drivers do it with the cameras built into their Teslas. http
Goldman Sachs puts general-purpose robots about where self-driving was in 2017 or 2018 🦾
Its estimate: they’ll need around 10 million hours of high-quality physical interaction data.
Self-driving learned this first. The models got good once the real-world data caught up.
October was the month demand showed up 📈
A 3,000 hours data sale to a world model lab, over 931M $NATIX burned in total, and the VX360 Desktop App open beta live in the U.S.
Our October recap is here 👇
https://t.co/89sQZRnrFM
The real line between ADAS and L4 is not capability. It is who catches the mistake when the model is wrong.
Answer that and the training data question mostly answers itself.
Full breakdown on coverage, labels and edge cases 👇
https://t.co/wR07LQVTxP
More world models, more demand for real driving data 📈
This week Rivian bought Third Dimension to turn real footage into 3D worlds, and Baidu is about to reveal its own world model.
Whoever builds the model needs the footage. NATIX has over 200,000 hours of it. https://t.co/DYe
Autonomous driving shouldn’t be locked inside a few companies 🔓
The Autoware Foundation builds open-source self-driving software, backed by members like AMD, AWS and Arm.
As a Premium member, NATIX supplies multi-camera data for Autoware’s open-source end-to-end model 🤝 https:/
Over 200,000 hours of driving footage, and none of it came from a test fleet 🚗
It was recorded by drivers in the NATIX network, on their everyday routes.
That’s the network behind every dataset, partnership and world model NATIX powers 🌍 https://t.co/aQLWhc2oDu
Video shows what happened. Telemetry shows what the driver did.
Our Safety-Critical Driving Events Dataset has both: GPS, wheel angle, pedals and brake state, 45 events in ten U.S. states 🛑
Working on ADAS or world models? It’s on Hugging Face.
https://t.co/wafjBvF8aj https://
More data from the same city won't teach a model how another city drives.
Two quick headlight flashes mean thanks in Hungary and a warning in Italy. Same signal, opposite meaning 💡
Why validation data has to come from roads a model has never seen 👇
https://t.co/kzf9QUI4G0
Driving video is just storage cost until you can find the moment you need.
With VLMs, NATIX can search its multi-camera data in plain language: describe a scene and get the matching clips.
Hours of manual review become a query that takes seconds 🔍 https://t.co/ORddCSIaCO
World Foundation Models learn the world from video 🌎
But if the footage only looks forward, the model only understands what’s ahead.
Autonomous systems need the full scene. That’s why NATIX builds on real-world multi-camera footage, not just a front view 👀 https://t.co/Dn9wDtBb
Models are getting cheaper. Compute is getting easier to buy.
Real-world data is still the bottleneck 🔍
Robots and autonomous systems need the long tail of real roads, seen from every angle. NATIX captures it at scale with multi-camera footage from drivers worldwide 🚗🌎 https://
Models are getting cheaper. Compute is getting easier to buy.
Real-world data is still the bottleneck 🔍
Robots and autonomous systems need the long tail of real roads, seen from every angle. NATIX captures it at scale with multi-camera footage from drivers worldwide 🚗🌎 https://
Physical AI labs are paying for the one thing they can’t generate: real-world data 🌎
That’s what NATIX has been building, one car camera at a time 🚗
And as @AlirezaGhods2 hinted, something is cooking 👀
A better model alone won’t get autonomy to scale 🚗
It also needs rare road cases to learn from, every camera angle to see the full scene, and real-world footage to test and validate against.
That’s the golden trio. NATIX’s multi-camera data covers all three 🔍 https://t.co/6KPlA
Real data. Real revenue. Real burns 🔥
Drivers collect multi-camera footage, AI teams buy it, and every sale becomes protocol revenue that buys back $NATIX and burns it.
The more Physical AI needs real-world data, the more this loop runs 🌎 https://t.co/0GJ5feofP7
World Models simulate reality, but if you feed them nothing but internet data, how much reality are you actually capturing?
Real-world multi-camera driving data is the key to unlocking the next generation of autonomous systems. 🗝️
That's how autonomy scales 📈 https://t.co/DGTkk
Most World Models today are built on front-facing driving videos. ➡️
But reality comes at us from all sides. That's why multi-camera video data is extremely valuable for autonomous driving simulations. 🔁
That's the data NATIX offers 📹 https://t.co/BDZdecNdGs
Missed our latest token burn? We've got you covered 😉
224,971,878 $NATIX burned across Q2 and Q3 🔥
Our drivers collect data. Companies pay for it.
Real demand = Value accrual to the token.
Over 931M tokens were already permanently removed from the supply in total 💪 https://
NEW AI CUSTOMER BUYS 3,000 HOURS OF NATIX DRIVING DATA 🚗
We've signed a six-figure deal with an AI lab pioneering general world models. Real demand for DePIN on @solana.
Not a research collab. Not a free dataset. A paying customer.
September Progress Update just dropped:
📹>198K Hours of Multi-Camera Footage
💹>8.4B $NATIX Staked
🌐VATIX World Model unveiled by Valeo team using NATIX data
🔓Third open-source dataset released on @huggingface
Full recap👇
https://t.co/uiZlz4tDqi https://t.co/efwRJwImPT
Simon gets the asymmetric setup. Zoom in on @NATIXNetwork for a second.
World Foundation Models are becoming the substrate Physical AI actually runs on. NATIX data is turning into the benchmark those models train against.
Orbis 2 (University of Freiburg / nxtAIM) used NATIX as
Autonomous driving often breaks down where it has been before.
A module taught how to drive in New York will most likely fail in Japan. 🛣️
That's why the variety of data matters no less than the amount of data. https://t.co/Iatn5kssz0
SELLERS DRAINED
88%
28D SELLERS EMPTIED OUT
CAPITULATION
+0.05
BOUNCE COMPOSITE (z)
HEAT 7D
0.70×
VOLUME ÷ POOL DEPTH
HOLDERS IN PROFIT
4%
TRACKED WALLETS · ENTRY BELOW PRICE
WHAT'S CARRYING THE PRICE · 30DORGANIC
30D RETURN
+3.6%
ALPHA vs TAO
WITHOUT CHAIN BUYS
+3.6%
SAME 30 DAYS, PROTOCOL BID REMOVED
CHAIN SHARE OF BUYING
0%
CHAIN BUYS vs TRADERS' NET
CHAIN BUYS / DAY
0 τ
PROTOCOL BUYING ITS ALPHA
See who's buying and selling StreetVision by NATIX — every trade classified miner / validator / owner / outsider, plus holder cost basis and the full 23-factor screener.
OPEN THE INSIDER TAPE →