Bittensor (TAO)

It’s AI (Subnet 32 – @ai_detection) is #Bittensor’s lie detector. Spotting whether something was written by a human or an LLM, and even flagging which sentences look fake They’re top of the class on benchmarks like RAID, beating other AI detectors on accuracy. Their model to scale is simple: metered API + SaaS. Paid API for platforms and AI engineers, plus tiers for schools, creators and enterprises that need bulk scanning, plagiarism checks and reporting. They’re also rolling out paid plans on the web app, browser tools and bulk/plagiarism features, all billed through a central validator‑run API Today you can already use their web app or plug the API into your own product to keep AI‑written spam, bots and ghostwritten homework in check. Another great user case and product that highlights #bittensor users case beyond just $TAO

Software *was* eating the world ... But then AI ate all the software. Now, AI is eating AI. Cost of AI races to the bottom: it's commoditized. Who wins? Whoever serves up AI commodities the cheapest. Bittensor $TAO. The edges have the edge.

I think the AI x Crypto narrative has finally grown up. 2024-early 2025 was mostly about AI tokens pumping on vibes. Anything with agent, LLM, or compute in the name flew, then corrected hard. Even Justin Sun said it out loud: crypto still hasn’t had its ChatGPT moment, no killer app that normal users actually touch every day. By early 2026, that phase is basically over. What I’m seeing now is a quiet but important shift: from narratives → infra, usage, and economics. Instead of AI coins, the real focus is: – Autonomous on-chain agents that can actually earn – DePIN networks for compute, data, bandwidth – Verifiable AI execution – Token models tied to demand Projects without real metrics are bleeding. Projects with GPU hours, subnet revenue, agent activity, or real users are the only ones still standing. My honest read on the current leaders: – @opentensor | $TAO - the purest long-term bet on decentralized intelligence. Subnets competing on real performance is the closest thing I’ve seen to an open AI market. – ERC-8004 - foundational. Agent identity, reputation, and trust on Ethereum sounds boring until you realize autonomous agents can’t scale without it. Tens of thousands of agent IDs minted already tells me devs are using it. – x402 - Machines paying machines via HTTP is exactly the kind of thing crypto is good at and TradFi can’t do cleanly. Millions of transactions, stablecoins, no subscriptions, no humans in the loop. – @virtuals_io | $VIRTUAL - Tokenized AI agents as onchain businesses sounded crazy a year ago. Now there are thousands of agents live, generating revenue, with people co-owning them. – @StoryProtocol | $IP - attacks the hardest problem of all: data and IP. Unscrapable, licensed datasets for AI training is exactly what enterprises care about. – @ASI_Alliance | $ASI (ex-FET) - agents, data, compute, orchestration, even its own language (MeTTa). I like the vision, but execution + adoption will decide everything. – @rendernetwork | $RNDR - boring in the best way. GPUs in, revenue out. It doesn’t need a new narrative every month, and that’s a strength. – @ionet | $IO - messed up early with inflation, but the 2026 tokenomics overhaul is the real test. If demand-driven emissions work, it survives. On the supporting side, I think @grass, @akashnet, @nosana_ai matter more than CT gives them credit for. Data, bandwidth, and cheap compute are the real choke points for A. The big pattern I believe in for 2026+: AI agents doing real work from DeFi, ops, trading, data tasks, etc. Crypto rails for ownership, payments, coordination. Tokens backed by usage, fewer projects, stronger survivors. Risks are still huge. Centralized AI is fast, cheap, and ruthless. Regulation is unclear. Most AI x crypto teams will still fail.

TAO/USD (Daily) TAO is testing major support around $150–$200. This is a historical demand zone. Structure has been making lower highs. Currently: fragile. https://t.co/Tlj697lHGp