τaonomics (τ, τ)

When $TAO reaches USD $10 billion in market cap for the first time I will add a profile picture on my x account. Just HODL + Stake 'n Chill #Bittensor Prevailing Definition: Market capitalisation = 𝜏 price (USD) ⋅ circulating supply

The founder of @taostats, @mogmachine was buying TAO at $12. He's co-founded 6 subnets, built the wallet, the explorer, the mobile app. Runs one of the largest validators on Bittensor. He rarely does interviews. We sat down and he told the full story. From the bear market to building the backbone of a $3B network. What he shared: • How he found Bittensor in late 2022 while running nodes at the bottom of the bear market • Started TaoStats as a Google spreadsheet. Turned it into the most used product in the ecosystem • Jacob from OTF personally asked him to build the blockchain explorer. He'd never built one before • Co-founded 6 subnets. Shut down the ones that weren't working. Kept the ones that were • Built Corcel, the first API gateway to Bittensor's AI • Just launched the TaoStats wallet and mobile app. Wants to be the single interaction layer for everything on Bittensor • His take on why only about 20-30 of 128 subnets are "real quality" • Says next year is "the era of bizdev" for subnets The line that stuck with me. He said you don't know HTTP because of HTTP. You know Google. You don't know TCP/IP because of TCP/IP. You know Netflix. That's where Bittensor is going. The subnets become the brands. The protocol disappears into the background. These aren't side projects. These are actual AI companies being built.

Sat down with Bittensor co-founder Jacob Steeves (Const) for a deep dive into the state of Bittensor in 2026. We covered how Subnet 3 trained a 72B parameter model in a fully permissionless decentralized way, how subnets create markets for digital commodities like gradients and inference, the dynamic TAO tokenomics that lock emissions into subnet liquidity pools, and why open ownership of AI matters more than ever as centralized labs head toward trillion-dollar IPOs. 0:00 Intro: Bittensor Co-Founder Jacob Steeves 1:13 Jacob's Background, From Deep Learning to Crypto 4:45 Founding Bittensor: The Monetary Computer 7:58 How Subnets Work as Markets for Digital Commodities 11:18 Subnet 3: Decentralized Model Training 15:28 Permissionless Training at Internet Scale 18:52 Why This Matters: Open Source AI Ownership 25:53 Subnet Tour: Optimization, Inference, and Compute 29:55 Affine: Beating Qwen 30B via Market Incentives 34:26 TAO Tokenomics and Dynamic TAO 51:54 How Investors Participate: Just Buy TAO 54:05 Open Ownership vs Fiat AI

Everyone should know about what Chutes is doing. Fully permissionless inference mining. Fully end to end encrypted. Fully private TEE machines. You could safely send private keys over the wire and know it was fully private. The entire stack encrypted from your machine to the LLM and back. The fact that that happens on top of a permissionless network with infra run my god knows who is nothing short of mind boggling.

TAO Co-Founder explains how TAO can compete with top AI labs “We can actually always win if we can define the problem better than them because we can just consume theirs, put it into ours and then beat it and it just goes faster” “Corporations just stall and d*e because people just don’t want to work. But the miners on Bittensor have to work and they do” “We don’t talk a big game and then don’t do anything. We were serving at one point, the largest number of open source model tokens on OpenRouter for like a six-month period, and really cheap” “In 2024, we were crushing benchmarks everywhere. We are one of the only Crypto projects that actually does things that are state of the art”

TAO Co-Founder: AI people are normies, Crypto people are freedom fighters “You can’t get funding from Sam Altman, the guy who’s trying to enslave us, and then pretend to be like ‘freedom technology.’ Like, what are you trying to do? It just seemed odd” “People fucking hate crypto people sometimes, but also deep down we’re freedom fighters. And that’s why Worldcoin didn’t work, because we’re permeated by people that actually believe in freedom. That’s who we are” “The AI people who think they’re so hot and high and mighty, they’re just normies. They’re all blue church people. I love them to death, they’re all very smart, but they’re not freedom people. They don’t really grasp geopolitics, they don’t really understand, and they don’t really care” “They’re just gonna go work, make their money, and go play their board games. But crypto people are renegades. You can’t try to do Worldcoin on us. It’s not gonna work”

“Bittensor is moving into a new era where it’s the perfect ecosystem for AI agents” “An AI can mine Bittensor, build subnets, trade it and buy the commodities it needs directly from the chain” “Crypto is the perfect play place for the agentic world”

With all the noise around $TAO lately.. It's probably a good time for people to watch the latest #bittensor documentary The Incentive Layer 👉 youtu.be/71rvASmXUN8?si… Its under 1 hour of your time.. and you’ll come away with a much clearer picture of what’s actually being built $TAO

Unbelievably well deserved shout out from @intel towards @manifoldlabs The team, who have been furiously developing trusted computing layers on Bittensor against the tide of exploit and FUD, with clear eyes, and patience. So proud of my brother @0xcarro who saw the vision from the beginning Bravo

Advancing confidential computing for a more secure AI future. Together with @manifoldlabs, we’re exploring how Intel TDX and Intel Trust Authority help enable confidential workloads across decentralized infrastructure, including @TargonCompute's Targon Cloud platform—protecting data at rest, in transit, and in use.

NVIDIA just open-sourced the missing piece for Bittensor mining. NemoClaw = sandboxed OpenClaw agents running inside NVIDIA OpenShell. One-liner install. Landlock + seccomp + network policy. Every outbound request blocked unless explicitly allowed. Filesystem locked. Inference routed through controlled backend or even other Bittensor subnets. This is Docker for AI agents, built by @nvidia. Now imagine this as your miner setup: NemoClaw sandbox ➡️ @openclaw agent ➡️ @const_reborn agcli (chain ops) ➡️ Your subnet's scoring logic Sandboxed. Policy-controlled. One command to deploy. Subnet owners: this should be in your "How to Mine" docs yesterday. Ship a NemoClaw blueprint as your recommended miner setup. Give miners a secure, reproducible, one-liner deployment. Lower the barrier. Increase miner count. Improve your subnet. Stop writing 47-step mining guides. Ship a blueprint | $TAO github.com/NVIDIA/NemoClaw

For anyone trying to understand Bittensor from first principles, this lecture is a useful place to start. Presented by Bittensor co-founder @const_reborn. Learn Bittensor > Start with Bitcoin, distributed systems, incentives, > How Bitcoin leads to Bittensor Subnets coordinating AI infrastructure. Topics: // Start - Bitcoin as more than a digital currency // Risks of AI centralization + closed systems // "The incentive computer" // How Bittensor subnets work (mining, validating) // How distributed AI infrastructure could scale globally // Impact on students, builders & future founders Recorded at the National University of Singapore Computer Science Club. @NUSComputing Chapters - Bitcoin, AI, and Bittensor - Bitcoin history and decentralization - AI changes how engineers work - The danger of centralized AI power - Why most crypto visions fail - Bitcoin as the world’s largest compute network - Bitcoin as a market for compute - The idea of an “incentive computer” - Bitcoin compared to Bittensor - Classroom example of decentralized scoring - A simple subnet example - SN62 :: @ridges_ai SWE agents - SN3 @tplr_ai :: Distributed AI Training - SN52 @lium_io :: GPU rentals on Bittensor 128 subnets, some examples Why this matters for the future of work Q&A Subnet examples mentioned @ SN64 - Serverless + TEE Compute :: @chutes_ai SN8 - Prop firm @VantaTrading SN52 - AutoML :: @gradients_ai SN62 - SWE agents :: @ridges_ai SN51 - Compute / GPU rental @lium_io SN4 - TEE compute for enterprise :: @TargonCompute SN3 - 72B Distributed Training run :: @tplr_ai SN41 - Prediction markets :: @almanac_market SN44 - Computer Vision @webuildscore SN68 - Drug discovery :: @metanova_labs SN18 - Weather Forecasting @zeussubnet SN50 - Bitcoin prediction data :: @SynthdataCo SN61 - Quantum computing :: @qBitTensorLabs SN14 - Bitcoin mining pool :: @taohash SN34 - Perp Dex :: @0x_Markets SN17 - 3D model generation :: @404gen_ SN33 - Data analytics :: @ReadyAI_ SN19 - [Since relaunched] RPC infrastructure :

The largest decentralised LLM pre-training run in history. SN3 @tplr_ai trained Covenant-72B across 70+ contributors on open internet infrastructure. Now it’s being discussed by @chamath with @nvidia CEO Jensen Huang. Distributed, open-weight model training on Bittensor is getting started.

On the @theallinpod this week, @chamath asked @nvidia CEO Jensen Huang about decentralized AI training, calling our Covenant-72B run "a pretty crazy technical accomplishment." One correction: it's 72 billion parameters, not four. Trained permissionlessly across 70+ contributors on commodity internet. The largest model ever pre-trained on fully decentralized infrastructure. Jensen's answer is worth hearing too.

$TAO subnet just automated in 12 hours what pharmaceutical companies pay millions for. This is MASSIVE. @metanova_labs SN68 built an agent called ArboNOVA. It maps molecules to prior art patents using only open data and open tools. No proprietary databases. No enterprise licenses. No teams of analysts. The benchmark: 1,500 molecules across ADHD-related patents since 2012. 18 iterations of a self-improving loop. Best hit rate: 85.47%. Look at that chart. It started at 55.4% and climbed to 85.4% through 18 automated experiments. Each iteration the agent tried a new strateg, jurisdiction filtering, family expansion, canonical salt-stripping, similarity fallback, tiered similarity search and kept what worked. Discarded what did not. Self-improving. Autonomously. Here is why this is HUGE: Molecule-to-patent matching is one of the most expensive, time-consuming bottlenecks in drug discovery. Before a pharma company can advance a molecule to the wet lab, they need to know: does prior art exist? Is this compound already patented? In which jurisdictions? By whom? Today, this work requires specialized patent attorneys charging $500-$1,000/hr or More, proprietary intelligence platforms like SciFinder or Cortellis costing six figures annually, and teams of analysts spending weeks per compound. The entire pharmaceutical patent intelligence market is billions and billions of dollars. And it is still largely manual done. MetaNova's agent did it for 1,500 molecules in 12 hours. Autonomously. Using open-source tools built on @const_reborn's Arbos framework and @karpathy's autoresearch architecture. Running through Bittensor subnet infrastructure. And they are not stopping there. They just introduced Clyd, an on-demand analyst agent that queries NOVA databases, analyzes miner and validator code, tracks submissions across epochs, and explains scoring algorithms. An autonomous operations layer for a decentralized drug discovery engine. Think about what is being built here. NOVA Compound scores molecular binding against real protein targets using Boltz-2 predictions from MIT and Recursion Pharmaceuticals. ArboNOVA maps those molecules to patent landscapes. Clyd monitors the entire operation. Arbos orchestrates the analysis through a Telegram bot. Chutes provides encrypted inference. All running on Bittensor. A decentralized pharmaceutical research pipeline. No billion-dollar lab. No centralized gatekeeper. No permission required. Just competing agents and digital commodities producing intelligence that pharma companies pay fortunes for. The drug discovery market is $71 billion and growing. The patent intelligence segment alone is multi-billion. And a Bittensor subnet just proved it can automate the core workflow at 85%+ accuracy in half a day 👀 LET THAT SINK IN! This is a roadmap to displacing an entire industry's analytical infra. $TAO Not financial advice. DYOR.

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Today we are excited to share some news, Targon has been accepted into the @nvidia Inception program for startups! We are looking forward to leveraging this collaboration to grow and improve the Confidential NVIDIA GPU experience on Targon.com

🚀 When @JosephJacks_ took a closer look at what Ridges was building, it quickly became clear something meaningful was happening and that’s when the decision to partner with the subnet was made. Ridges is delivering 73.6–87.8% on SWE-Bench Verified and a 96.3% Polyglot score, placing it among the top coding agents on the market while costing just $29/month for ~100 PRs (~$0.29 per PR). Beating the benchmarks while making high-performance coding agents accessible. As Joseph put it: “We’re harnessing a decentralized, open-source intelligence network that gets more competitive and more capable over time by design. That’s a compounding advantage that no closed, proprietary system can replicate.”

$TAO is up 82% in the last 30 days. Bittensor subnets are up 105% over the same period on a market-cap weighted basis. Year to date, the Yuma Composite Fund - which provides exposure to more than 100 subnets - is up 55% on a USD basis, versus 28% for $TAO alone. More info: hubs.ly/Q046VFm80

NOVELTY SEARCH :: ARBOS Find out how Bittensor is evolving in The Age of Agents This week we have @const_reborn talking Arbos How he created an agent that launched its own subnet on Bittensor SN97 :: Constantinople LIVE community call :: via Bittensor discord. Thursday 19th Mar :: 9PM UTC / 5PM EDT