
$TAO is not one AI network. It is roughly 100 of them competing for the same emission budget.
The ecosystem has some of the best deAI projects on the planet.
Eight subnets worth knowing, and why.
➟ Chutes (SN64)
Serverless AI inference. Deploy any model behind an API without touching a GPU cluster, at a fraction of AWS pricing. Highest emission share on the network and the only subnet with externally verified revenue in the low millions. If Bittensor has a product-market fit, this is it.
➟ Targon (SN4)
Confidential compute using Intel TDX and encrypted VMs. Manifold Labs co-authored a security architecture paper with Intel in March 2026 and raised a $10.5M Series A. It powers Dippy, a consumer AI app with millions of users. Healthcare and finance cannot use open inference. Targon is the only subnet built for buyers who need privacy guarantees.
➟ Templar (SN3)
Pre-trained Covenant-72B across 70+ nodes over commodity internet connections. No central cluster, no whitelist. It beat LLaMA-2-70B on MMLU. This is the strongest existing proof that decentralised training works at frontier-adjacent scale.
➟ Lium (SN51)
GPU rental. H100 and A100 clusters by the hour, with hardware specs and uptime verified by Bittensor validators instead of a provider's word. Useful if you want burst compute without a 12-month contract.
➟ Affine (SN120)
Winner-takes-all reinforcement learning. Only a model on the Pareto frontier can take the lead, and every winner becomes the new open baseline. Built by Const, a Bittensor co-founder, and hosted on Chutes. Worth watching because it composes other subnets rather than competing with them.
➟ Apex (SN1)
Macrocosmos' subnet for agentic LLM workflows and fine-tuning data generation. The oldest subnet on the network and one of the few teams shipping multiple subnets.
➟ Taoshi (SN8)
Decentralised prop trading. Miners submit trading strategies, validators score them on live performance. Real signal output, and the easiest subnet to judge because the returns either exist or they do not.
➟ Score (SN44)
Computer vision on football matches. Narrow, but it shows the incentive model works outside of LLMs.
Watch this space!

English




















