FILE 00127KI did not lose faith in open AI, open science, or decentralized infrastructure. I lost faith in the assumption that every important coordination problem deserves an investable token. That was the economic reckoning. Then five mornings at Stratford added the political one: every registered claim has an owner, the owner takes sides in an emergency, and the strongest monetary-economics argument of the decade — Alden's — quietly assumes the emergency never comes. This is the essay the authoritarianism series kept promising: what a bearer asset is actually for.
FILE 00233KA chain promising decentralized machine intelligence now runs on the most human mechanism ever invented: capital, delegation, founder influence, validator politics, and control over who receives emissions. At least some of the builders are sincere. The work is at least sometimes genuine. The architecture asks the powerful to surrender what the architecture lets them keep.
FILE 00319KIn the same month, Jack Clark and Chamath Palihapitiya independently flagged the same thing: a 72-billion-parameter model trained across 160 GPUs by anonymous participants coordinating through a blockchain. Neither is a Bittensor insider. Both recognized what it means when the ability to create foundation models stops being a privilege of five organizations.
FILE 00417KSomeone on X pointed out that Covenant-72b can't count the R's in strawberry. They're right. But so were the people who laughed at GPT-4 for the same mistake two years before it started passing the bar exam. The interesting question was never whether the model fails. It's why, what that reveals about intelligence, and what happens next.
FILE 00521KTony Hawk spent thirteen years trying to land a trick the world said was impossible. Within a decade, teenagers were doing it on YouTube. Steven Kotler calls this the 'seeing it done' effect. Covenant72B, the largest model ever trained on a fully decentralized network, is that same moment for AI. The impossible just became the starting line.
FILE 00644KIn September 2025, Anthropic settled a $1.5 billion lawsuit for pirating seven million books. In January 2026, music publishers sued them for $3 billion over 20,000 torrented songs. In February, Anthropic accused DeepSeek of 'industrial-scale distillation.' The pattern is older than the internet. It is older than copyright itself.
FILE 00729KPre-training gives AI knowledge. Post-training teaches it judgment: what to refuse, how to reason, what to value. This is the phase where alignment happens, and while decentralized efforts existed, weight sync over public internet made them impractically slow. This week, a research paper from Grail demonstrated that the bandwidth barrier keeping RL post-training centralized was 99% redundant, an artifact of how we were moving data rather than a physical constraint. The implications extend far beyond compression ratios.
FILE 00829KFor eighty years, the most powerful technologies have required concentration: co-located machines in fortress datacenters, tightly controlled by those who could afford the infrastructure. This week's research breakthrough from Templar marks something different, a technical path toward intelligence as genuinely distributed public infrastructure, where your home GPU can train frontier models alongside Google's datacenters.
FILE 00938KDario Amodei's October 2025 statement on 'American AI Leadership' strips away any remaining pretense that centralized AI development serves universal human welfare. Instead, it reveals the naked truth: AI monopolies are aligning with military-industrial interests and nationalist agendas.
FILE 01018KThree years ago, I wouldn't have believed I'd be training for a half marathon while helping coordinate a decentralized AI protocol. But both journeys, the solo morning miles and the collective effort to democratize artificial intelligence, follow the same philosophy.
FILE 01110KWhy a technical breakthrough in gradient compression could reshape who controls the future of artificial intelligence
FILE 01212KA correction to my March 2024 fractal markets analysis, acknowledging how Score SN44's computer vision infrastructure approach was unfairly characterized as a prediction service—and what this means for applying Mandelbrot's framework.
FILE 01314KA deep dive into Synth SN50's methodology reveals how one Bittensor subnet is successfully implementing Benoit Mandelbrot's fractal market insights through probabilistic forecasting rather than traditional price prediction.
FILE 0147KMeet the cryptopunk miners of Templar training AI models on their gaming rigs, challenging the assumption that only trillion-dollar companies can build artificial intelligence.
FILE 0159KA forensic analysis of the Bittensor network incident, examining why the blame game misses the bigger picture about decentralization and sovereign infrastructure.
FILE 01611KAn analysis of Bittensor's prediction subnets through the lens of Benoit Mandelbrot's fractal market theory, examining the fundamental challenges and opportunities in AI-powered market forecasting.
FILE 0174KAn examination of how TaoHASH's mining hashrate marketplace creates value misalignment with Bittensor's core mission of incentivizing AI development.
FILE 0187KAn analysis of how non-commercial subnets are essential for maintaining true innovation in Bittensor's dTAO ecosystem, drawing parallels with successful open-source projects.
FILE 0195KIn an era defined by technological rivalries and corporate secrecy, a disruptive force is reshaping artificial intelligence. Last week's emergence of DeepSeek—a Chinese AI model matching the capabilities of leading proprietary systems—represents more than just another milestone in AI development. It heralds a renaissance in open-source collaboration that could fundamentally transform how humanity approaches technological innovation.
