Today we’re introducing the Yuma Total Market Fund. One allocation to access $TAO and the full Bittensor subnet ecosystem. AI exposure is expanding beyond public equities and venture.

Whoa. A Bittensor subnet (60) BEAT FABLE: "Before the US government locked things down, Bitsec jailbroke Fable 5, Anthropic’s hardened security-resistant model, and pointed it at a paying client’s codebase (with permission). Then they ran Bitsec against the same code. Fable 5 found around 60 vulnerabilities, five of them high or medium severity. ****Bitsec found over 160 in total, including five criticals and ten highs that Fable 5 missed entirely,*** and it caught everything Fable 5 caught." taodaily.io/bitsec-sn60-argu…

Most AI detection benchmarks measure performance against known generators. Real-world environments are different. We evaluated our models against 300,000 images, including adversarial content specifically designed to evade detection. The goal was not to achieve a benchmark score. The goal was to understand how the system performs under pressure. Results like these are critical because attackers are constantly adapting. Detection systems need to adapt faster. 98% accuracy across a large-scale stress test is a reflection of that approach.

Spent a few days in DC last week to help educate lawmakers and their staff on decentralized AI, Bittensor, and digital assets. Seems especially relevant and timely given events of the last few hours. Some takeaways: 1. Every office—Democrat and Republican—is looking to learn more. There’s a universal recognition AI is here and Congress needs to get up to speed…fast. It reminded me of the early conversations about crypto when it was 99% education and 1% technical. 2. Americans’ relationship with AI is changing. Costs are increasing and access is not guaranteed. The “Uber” subsidization period is over. We are entering a new phase where Chinese models are growing in use and importance as Americans seek cheaper alternatives with 80-90% of the capabilities as leading frontier models. 3. This has significant national security concerns because the relative model substitution costs are so low. It becomes a backdoor for China to get access to Americans’ data when you plug in DeepSeek or Qwen for an OpenAI or Anthropic model. There is very little appreciation for this right now. Enterprise grade use will likely remain on the frontier but the looming threat is very real. It offers an opportunity to discuss AI privacy in new and important ways. 4. Instead of taking equity stakes in the large labs, we need to start talking about AI and model access as a public good in the same way we talk about Internet access as a public good. Models are, after all, trained on our collective data. Models should become open source—yes, fully open source—after 2 years to allow frontier labs to recoup costs while expanding access to leading technology as soon as possible. Truly open source AI will have tremendous benefits for innovation in America as entrepreneurs, small businesses, and everyday Americans get access to frontier intelligence and build novel applications and participate in distributed networks. The pharmaceutical industry provides a policy blueprint for an open source framework re: generic drug availability. 5. Familiarize yourself with the Great American AI Act by Congressman Obernolte and Congresswoman Trahan. It’s a discussion draft but it will form the basis of discussions on the Hill. This likely evolves heavily over time but it’s the most prominent AI bill and the one everyone is starting to wrap their heads around. Lots of focus on model safety, workforce, and creating a broad apparatus in government to facilitate AI development. 6. The changing nature of jobs and work is very real. There needs to be investment in people and communities to help everyone adapt to these changes. AI literacy needs to be a focus just as financial literacy has been. Overall, I was incredibly grateful and impressed by policymakers’ knowledge and open-mindedness to the issues AI presents. Daily events will drive these conversations but the need for access and alternatives to centralized labs has never been clearer. The moment for decentralized AI has arrived and it’s time to seize it.