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So Claude launched Fable - which is a nerfed version of Mythos We're finally starting to see what we have predicted for a long time. The beginnings of managed stagnation. The most powerful version stays in-house, everyone else gets a throttled copy. Select "trusted" teams got access to Mythos while everyone else got Fable (and not for long - it appears that it will be available only via extra usage from June 22) meaning even the nerfed version will cost $$$ We're starting to see rich / whatever "trusted" means in this context get access to the latest and best models while everyone else gets access to a worse one They also added "novel" safeguards - including limiting what it does when used for frontier LLM development! It limits what the model does (i.e. further nerfs it) without the user even know via methods such as prompt modification. e.g. you give it a task, it returns you an output but behind the scenes if it didn't like what you asked, it changes your prompt into something it does like, and returns that without you being aware!!! And they mention LLM development as a specific aspect that's blocked! This is like Microsoft shutting down the PC when it sensed Linus Torvalds coding Linux. This is managed stagnation in its early form. One company holds the frontier and rations who gets it. The best model goes to a trusted few, a weaker one to everyone willing to pay, and nothing at all to the rest. That is exactly what open algorithms break. You can't nerf math that is already in the open. This is the fork in the road. Either algorithms go open in the next year or one company owns the future and never gives it back.

Summary of last weeks community call! Google found a powerful new algorithm and locked it away. Within months, a swarm of strangers on the internet, teenagers included, rebuilt most of it from scratch. That should not be possible. Closed labs are supposed to win.

At TIG, we always knew we’d make some surprising allies as we pursue our mission. The Pope, however, was not on our 2026 bingo card. During the first encyclical of his tenure, @pontifex was clear: algorithms must henceforth be included in the basket of ‘goods that are universally intended for everyone’. Today at 5PM BST, tune in to hear @Dr_JohnFletcher and @0x_Asuka discuss how TIG ensures that frontier technologies will remain accessible to all. See you there!

TIG builds an IP portfolio that only ever grows.

A couple of points on the recent OpenAI math result and why TIG is built for exactly this. First of all, this is a genuinely impressive result. It's a longstanding famous problem and many mathematicians are impressed with it. Where TIG comes in is the difference in how its algorithm challenges are verified vs how proofs like the one from OpenAI are verified. The AI did the proof with over 125 pages in its chain of thought. BUT It took nine mathematicians multiple weeks to verify it actually worked. The raw output required polishing (missing definitions, scrambled logic) and ultimately needed a human-edited, reorganised, clearer version as the final proof. So the AI produced something but humans had to do all the heavy lifting to figure out if it was real. That is the whole problem with AI doing maths. Generating gets faster, verifying stays slow and expensive. One of the nine mathematicians flagged this directly, worried that even experts will struggle to verify future proofs. TIG sidesteps this entirely. The problems on TIG are asymmetric. Hard to solve, trivial to verify. If an algorithm is better (eg runs quicker), it does not matter at all what the contents of the algorithm are. You run it. It either works or it doesn’t. If it works, you can tell immediately if it was better. No expertise needed. This is what the miners (benchmarkers) in the TIG network do. When an algorithm is submitted, benchmarkers run and then adopt the best one. So with the increase in AI x Maths, TIG works not only on challenges of economic importance, but on the exact shape of problem where AI-generated work can actually be trusted at scale.