- TAO surges 12% daily, 71% monthly, now pressing key $302 resistance zone.
- Repeated higher lows show strong buyers absorbing supply as breakout pressure builds.
- A break above $302 may trigger a fast rally since little resistance exists above.
TAO is up nearly 12% in the last 24 hours, trading around $304 at the time of writing. The move extends a 71.5% gain over the past 30 days, as the altcoin claimed a market cap of $3.27 billion.
Despite the rally, TAO remains more than 80% below its all-time high near $757. Thus, traders are now watching a breakout above $302 that could trigger further upside.
TAO Tests $300 Resistance, Eyeing Breakout Zone
The recent rally has pushed the Tao token into a major resistance zone between $300 and $305, which has acted as a supply in previous moves. Meanwhile, the TAO token price is forming higher lows, showing growing buying pressure
Crypto analyst Ardi highlighted $302 as a key breakout level. The price has tested this level multiple times, with shallow pullbacks indicating continued buying interest.
If TAO breaks above $302 and holds, the move could accelerate due to limited resistance above the range. This setup points to a potential continuation of the bullish trend.
On the flip side, if TAO fails to break above the $300–$305 zone, the first support level appears near $260. A deeper pullback could extend toward $220, where the last accumulation base formed.
These levels remain important for traders watching short-term direction.
AI Narrative Supports Demand
It is important to note that Bittensor is being largely discussed as an AI-layer asset rather than a simple store of value.
The project follows a similar structure to Bitcoin with a 21 million supply cap and halving cycle, but replaces proof of work with “proof of useful work.” That means network incentives are directed toward AI computation instead of pure security.
NVIDIA CEO Jensen Huang compared the network to a modern distributed computing system similar to folding@home. The comparison highlights real output from the network, including decentralized model training.
Recent examples include a distributed run on Subnet 3 that trained a 4 billion parameter model across multiple contributors. This validates the core thesis of decentralized AI compute
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