🚨 THE MORE POWERFUL AI BECOMES. THE MORE IMPORTANT IT WILL BE. WHO CONTROLS IT $TAO I don’t think people have fully processed where this is heading. OpenAI reports that Astra can operate software, carry out complex professional tasks and discover previously unknown security vulnerabilities. Makes us question access and control. Astra Release openai.com/index/gpt-6-astra… As AI takes on more work, the people controlling access gain influence over what others can build, what it costs and who gets to participate. That should concern all of us. If the most capable systems become essential to earning a living, being priced out could mean losing opportunities. If businesses become deeply dependent on one provider, changing its prices or terms can affect thousands and eventually millions of people and businesses overnight. The FTC has already identified potential competition risks in major AI and cloud partnerships, including restricted access to resources and higher costs of switching providers. FTC findings ftc.gov/news-events/news/pre… And control reaches beyond the model. Models need chips. Chips need data centres and electricity. Applications need reliable access to inference. An open model helps, but people also need somewhere affordable to run it. This is why Bittensor, many will get priced out. Subnets provide a way to organize competing providers of compute, inference, storage and other digital services, with incentives for contributing useful work. We should be pushing that opportunity further: • Compute supplied by independent operators. • Models people can legally run and modify. • Research others can inspect and build on. • Inference with competing providers. • Applications that let users move their data and change services. Every workable alternative gives people more choice. But we have to hold subnets to that standard. A subnet can still depend on the same cloud companies, hardware suppliers or closed models. Owning a token alone does not give us control over those dependencies. We need to ask who runs the infrastructure, who sets the rules and whether users can actually leave. I don’t believe a future controlled by a handful of companies is our only option. Governments, researchers, businesses and ordinary users still have choices. That is exactly why we need to build useable alternatives now The people who control powerful models and the infra. behind them could have massive influence over our future. Intelligence is becoming power. No corporation should own the off switch. Build in the open. Distribute the compute. Open the research. Give people models and applications they can run independently. Build networks that are harder to shut down, harder to capture and harder to hold hostage through someone else’s terms and prices. Our future should not require permission. Now is the time to build that independence, while we still have room. $TAO | Bittensor

🚨 GOOGLE JUST PUBLISHED THE PAPER THAT EXPLAINS WHY $TAO ISN'T PRICED IN YET Read this slowly, because it's the whole idea in one result. Google Research just released WikiSkill. They split agent intelligence into three separate layers: raw experience, a persistent accumulated knowledge base, and the reusable skills built on top of it. Then they tested it. • Qwen-3.5-9B with evolved skills scored 47.4% • Qwen-3.6-27B, three times the size, with no skills, scored 39.4% • The smaller model won • Skills evolved by one model transferred to completely different model families • On ALFWorld, a skill built by a 27B model pushed a 9B model to 70.2%, beating the 63.4% that same model got using a skill it built for itself Read that again. A model didn't need to be bigger. It needed to inherit better accumulated knowledge from somewhere else. That's the finding that should be a wake up call: Biggest Cluster wins the narrative in AI right now. Google proved it in public, this week. Here's the part that should actually break your brain 🧠 Bittensor has subnets already running this exact mechanism as a live economy. Real markets. Examples: @oroagents SN15 turns judged agent trajectories into training data for smaller models. @ridges_ai SN62 runs the same loop for coding agents. Grail SN81 uses verified rollouts for RL post-training. Teutonic SN3 keeps one shared model and only promotes a challenger when it proves a real improvement. @heydittoai SN118 is the persistent memory layer so the knowledge does not die with the session. @_redteam_ SN61 and @bitsecai SN60 compile attacks into better defenses. @openroboto SN80 does it with robot trajectories. @metanova_labs SN68 and @theminos_ai SN107 do it with molecules and genomes. Same architecture Google just published: experience, persistent knowledge, reusable skills. These markets are already live. The market still values $TAO like a speculative L1 with an AI narrative bolted on. It's still asking What does the token do! Nobody's asking the real question, which is what the actual compounding asset in AI is going to be. Not the weights. Google just said that. The compounding asset is the accumulated, transferable knowledge sitting on top of the weights, the layer that lets a small model beat a big one and lets that advantage move between models that have never met. $TAO is the only asset on earth pricing ownership of that layer, permissionless, across 126+ competing subnets, right now. The market hasn't started pricing that in. It hasn't even started asking the question. $TAO | Always DYOR. Paper: arxiv.org/abs/2608.27454 Chat with Paper: academy.dair.ai/papers/wikis……