Shizzy

If you are wondering what the $TAO use case is. It is sitting in the open, on every enterprise AI invoice. Run the math on a heavy agent workflow, 100M input and 10M output tokens a month: • Frontier closed models: $750 to $1,500 Scale it to 1B input tokens and the gap becomes $830 versus $7,500 to $15,000. Per workflow. Per month. Now multiply across a 10,000-person enterprise already paying $200K to $1M monthly just in per-seat AI licenses before a single token is metered. A 10-person startup recently reported $20K a month on AI tools and accidentally hit $30K. Industry is spending roughly $740 billion on AI infrastructure in 2026 while 95 percent of enterprise usage still runs on the most expensive frontier models. That is not a stable equilibrium. That is a repricing coming and waiting to happen. Open models are now good enough for the bulk of agent work. The only missing piece was infrastructure that serves them cheap, at scale, with verification. That is not a what if on Bittensor, it is live, it's competing on price and proof, permissionless: Engy SN53, GLM-5.2 verified inference, $0.68 input / $1.50 output per 1M tokens. @chutes_ai SN64, GLM-5.2 confidential inference, $1.40 input / $4.40 output per 1M tokens. Private RTX Pro 6000, 96GB, $1.80/hour. @TargonCompute SN4, Confidential H200 compute, $3.29 per GPU/hour @green_compute_ Compute SN110, RTX 4090, $0.40/hour. RTX 5090, $0.70/hour. @lium_io SN51, RTX 4090 listings roughly $0.18–$0.27/hour. RTX 5090 listings, roughly $0.38–$0.46/hour @Jason Calacanis already found it, 95% off his token bill, agents running hourly instead of daily. Enterprises are next, and they will not come because of the ideology. They will come because the bill. The use case has always been there in the invoice. $TAO / DYOR 🔗forbes.com/sites/jemmagre…