$TIG and $TAO - Synergies That Make Them a Perfect Pairing TL;DR: TAO’s models would experience enormous gains in performance, sophistication, and efficiency were they to use better algorithms for their training and function. The best way to produce these algorithms is @tigfoundation. Consequently, both communities stand to benefit substantially from cooperation. They will accomplish more together than either ever could individually. Intro I’ve seen a number of people who’ve recently found out about @tigfoundation assert that it is similar to or a competitor of Bittensor. I wanted to quickly point out the key differences between the two + highlight why it makes so much sense for them to work together. Similarities $TAO is a decentralized, permissionless framework for LLM training that allows miners to contribute compute to its ‘subnets’ in exchange for TAO tokens. Each subnet is a partitioned component of the TAO network that caters to AIs being trained for and directed at specific usecases. This approach allows researchers to aggregate compute much more cheaply than centralized approaches. $TIG is a decentralized, permissionless framework for algorithm development that allows anyone anywhere to contribute to the development of state-of-the-art algorithms with either compute or code optimizations. They are paid for their contributions in $TIG tokens. $TIG allows scientists to propose ‘challenges’ for distinct algorithm usecases. They function in much the same way as TAO subnets. This allows $TIG to optimize algorithms for any usecase and do so at previously unforeseen speeds. Both are permissionless and globally accessible which means that both LLM training and the improvement of the algorithmic methods that underpin it are now open to all who can meaningfully contribute. Synergies $TAO’s LLMs use various fundamental (often widely available) algorithms for their training and improvement. The sophistication of these methods (along with the amount of compute delegated to them) dictates how much energy and time $TAO LLMs take to improve. It follows that, if TAO models want to compete with the state-of-the-art equivalents produced by centralized AI firms, it is in the best interest of TAO to involve itself in and perhaps even fund the fundamental research that improves existing methods. $TIG proved last week that it is the fastest means available for improving algorithms. Keep in mind that the performance improvements conferred by better algorithms are exponentially more significant than those produced by an increase in compute. If you’re invested in $TAO outperforming centralized competitors in any regard, this should be a lightbulb moment. The cheapest and most efficient means to attain and maintain an algorithmically-derived advantage for your LLMs is freely available to you and already proven to work. Challenges for fundamental AI processes like Hypergraph Partitioning and Non-convex Optimization are just now being introduced and it is only a matter of time before they become state-of-the-art. With this in mind, I would argue it more than makes sense for those working on and invested in the $TAO ecosystem to begin directing some of their resource at deploying and participating in $TIG Challenges. The eventual benefits for $TAO LLMs will certainly eclipse the labor and resource required.