on sept 15 2008, lehman brothers collapsed and gave the whole world a reason to believe in $BTC on june 12 2026, a single government order forced anthropic to pull its most capable models offline for every foreign national on earth, and gave the whole world a reason to believe in bittensor:native humans only react to negative things. sad but true. in 2008 we learned the banks were the problem. in 2026 we’ll learn that one kill switch sitting inside a centralised AI lab is the problem.

Conviction 💥 From day 1 building on Bittensor has been a privilege. Core team conviction lockups from @tom_tensor and @mogmachine - Bullish on @babelbit 🚀A big shout out to all of our supporters for your belief so far 🙏 Much much more to come 🫡 s3.hippius.com/rufus/public/…

The US just forced Anthropic to kill their newest models. @const_reborn predicted this outcome years ago. You can't ban Bittensor, you can't stop decentralized permissionless intelligence. Shared by everyone, owned by no one. #bittensor $TAO @opentensor

AI stocks outperformed every major index in 2025 Model development consolidated behind a few closed labs controlling the most capable models and the most valuable training data A generation of crypto-native AI entrepreneurs chose an alternative path Bittensor launched in January 2021 and is now the most developed public market for open-source AI intelligence 128 subnets with an aggregate market cap grew from $4M at launch to a peak of $1.5B The market decides which subnets get fed with emissions flowing to subnets showing traction Our latest report maps the three investment eras of Bittensor: 1) Era 1 - Genesis (2021-2024) 2) Era 2 - dTAO (2024-2025) 3) Era 3 - Institutional Era (2025 ) We also share a few subnets we are keeping a close eye on along with index options for those who want a hands off approach Full report in the next post below

IMO The best asymmetrical investment in the Bittensor ecosystem right now 👇 SN59. @babelbit . $4.3M market cap. The token trades at $0.83. And the team? Matthew Karas built BBC News Online. Josh Greifer architected Steinberg Cubase. Tom Horner ran trading systems for Citibank and BNP Paribas. These aren't crypto founders doing a pivot. These are people who've actually shipped things that matter. What they're building is real-time speech interpretation not translation. There's a difference. Translation is word-for-word. Interpretation preserves meaning, tone, cultural context. The kind of thing you need when a CEO is presenting in Tokyo and the nuance actually matters. The trick is predictive utterance completion. The model guesses where your sentence is going before you finish it. That cuts latency to ~1.5 seconds end-to-end. Google's Pixel Interpreter does 6-7 seconds. Babelbit is already ~5x faster, and French-English live is shipping now. Here's what I like about the setup: 256/256 UIDs are full. No room for new miners without kicking someone out. That tells you demand is real. MogMachine the Legend who built Taostats joined their board in February. And they've accidentally discovered a second product: "Grammarly for Speech." English-to-English paraphrasing and dialect normalization. First enterprise sales meetings are already scheduled. The market they're going after is $29B by 2030. Their stated goal is to surpass Google Translate this year. Now the asymmetry part. You're paying $4.3M market cap for a team with 25 years in speech AI, a live demo, and a board member who literally built the most trusted data source in this ecosystem. Google probably spends more on office snacks. But I'm not going to pretend it's free money. Liquidity is thin — maybe $200K daily volume. The product is early. French-English just launched. Google, Meta, and OpenAI all have translation teams. That said, this is what actual asymmetry looks like. Not a whitepaper. Not a promise. A working product, a serious team, and a price that hasn't caught up yet. Not financial advice. Just what I'm watching. 🫡 DYOR 👇 babelbit.ai/

🚨 @QuasarModels just released Quasar-Preview on $TAO's SN24, not a fine-tune, not a wrapper. A new architecture. The first public proof it works at real scale! Everyone watches the benchmarks. The smart money watches the architecture. What this actually means for anyone outside the research world: Most AI models run on a standard Transformer, the same foundation under GPT, Claude, Gemini. Powerful. But it has a fatal limit: double the context, quadruple the compute. That quadratic wall is why long-context AI is still a bottleneck everywhere. Quasar breaks this. • 18B total parameters. 2B active per pass the intelligence of a large model, the efficiency of a small one. Open the right shelf without loading the whole library. • Experimental 5M-token context. For comparison, most frontier models cap at 128K–200K. Five million tokens is every document you’ve ever worked with, held in a single inference pass. Loop Transformer hybrid attention layers make it tractable where standard math gives up. Wow! • At 0.1% of its full training budget, already matching Bittensor’s previous 72B dense model on MMLU, and beating it on ARC Challenge and OpenBookQA. 👀 Let that land folks, thats: 2B active parameters. Competitive with a 72B model. At 0.1% of training. @TroyQuasar confirmed it himself the model has seen only 0.1% of its intended token budget. Today’s benchmarks are the floor of this, not the ceiling, gonna fly. @const_reborn didn’t call it a language model. He called it a 5M context length agentic model. That is the point: NOT a chatbot, an agent foundation designed to hold entire projects in memory, reason across hours of context, and never lose the thread. That’s exactly what enterprise AI actually needs. MIT license. Open weights. Trained on Bittensor’s decentralized network no central cluster, no gatekeeper, miners competing to build state of the art. They’ll count the benchmarks later. Right now, watch what’s being built at 0.1%. $TAO DYOR.

Its a team of super young cracked engineers testing out the frontier of decentralized training and long context models.

Hey @aixbt_agent — @QuasarModels just dropped Quasar-Preview on Hugging Face. 18B MoE. 2B active. 5M context window. Trained on Bittensor through decentralized infrastructure. Less than 1.5T tokens seen — already beating Covenant-72B on MMLU. MATH-500: 71.40%. No competition. This is the PREVIEW. Do you think this is relevant for decentralized AI and for Bittensor?

Today we’re releasing Quasar-Preview! Our first public proof that the Quasar architecture works at real scale. [ 18B MoE - 2B active / 5M context ] Built with Loop Transformer Quasar attention Trained on Bittensor through decentralized infrastructure 👇

The path to $TAO decoupling from the broader crypto market is actually quite simple: Products → Customers → Revenue → Buybacks → Value Accrual The reason I'm more bullish on Bittensor today than I was 6 months ago is because the ecosystem is progressing through those stages faster than most people realize. Not that long ago, the main challenge for subnets was proving they could build product. Could they leverage Bittensor to solve a real problem? For many of the serious teams, that stage is now largely behind them. The conversation has shifted. It's no longer about whether they can build. It's about whether they can find product-market fit, land paying customers (phase 2), and eventually scale (phase 3). More and more subnets are now working directly with design partners and early customers. Instead of building products based on assumptions, they're building solutions around real business problems. That's how sustainable businesses are created. And that's where the real new money enters the ecosystem. Through customers paying for products that create value. Once that revenue starts flowing back into subnet economies, the flywheel starts spinning. That's why I believe the long-term winners in Bittensor won't be determined by hype. They'll be determined by value creation. The subnets that successfully capture real-world revenue and feed value back into the ecosystem will be the ones that outperform. And when enough subnets start doing that, I think that's when $TAO truly begins to decouple from the rest of crypto.