BUYER BEWARE This extremely meticulous article will now be used to start Phase2 of “shut down open source” because “if we can’t control our closed models, imagine what happens to everyday life when anyone can just allow their open source models to proliferate”. Some will use this example and point to future grid outages, utility outages, transportation chaos, bioweapons, uncontrolled cyber swarms and the like to try and make the case for yet another attempt to limit US AI model control to a few folks. Then, one layer beneath the propaganda, one sees that these things are always written and promoted by a web of well organized existing shareholders or shareholder-adjacent of the closed frontier labs and groups of folks organized by a shared ideology roughly rooted in them knowing best. This article, more than anything else, should demarcate the beginning of a well organized effort to stand up a Closed Model Industrial Complex. These cartels exist in other markets as well and it now exists in AI. Bottom line, read everything carefully before signing up for the consequences. How we react will define the next hundred generations of Americans and their lives. Do not give up your rights or sovereignty because of fear of the unknown from articles that, at the root, are as much about the hidden incentives of an emergent cartel and their proxies. If you take the time to understand the incentives, you will be less likely to fall for it and overreact and have intelligence metered from a few suppliers. Bottom line: Keep Calm. Carry On.

In what respect was characterizing LLMs as "stochastic parrots" insightful, given that the paper seemed to vastly underestimate LLM capabilities?

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution

OpenAI's Mark Sellke and Mehtaab Sawhney with a16z's Lisha Li, on the state of AI and mathematics: Before OpenAI released GPT‑6 Astra last week, the model was already doing original mathematics. Recorded before the launch, this conversation tells the story of how it got there. It began with GPT‑5. Mehtaab Sawhney pasted in an Erdős problem still listed as open. Five minutes later, the model surfaced a paper that had solved it. The exercise eventually uncovered published solutions to 10 more problems thought to be open. Then Astra went further. Told to "go have fun" with a high-dimensional sphere-packing problem, it improved a bound that had stood since the 1970s. Mehtaab had spent six months on the same problem in graduate school and made "absolutely zero progress." Another Astra result established that non-sofic groups exist with a roughly 15-page proof. A related human breakthrough took 250 pages and machinery from quantum complexity theory. OpenAI’s Mark Sellke and Mehtaab Sawhney join a16z’s Lisha Li on why wrong ideas pollute a human’s context window, why polished papers hide how mathematics is actually made, why a breakthrough can stop one prompt early, and what math rewards once proving stops being the bottleneck. 00:00 Intro 02:44 Cracking an Erdős problem in 5 minutes 06:20 Why a human quits and a model doesn't 08:50 Wrong ideas pollute your context window 11:45 Why math papers are bad training data 16:20 Nobody knows how to stack spheres in high dimensions 18:28 The orange-stacking proof 21:04 The 1970s Russian paper nobody could beat 24:14 The function that won a Fields Medal 27:09 How Astra beat the sphere-stacking record 29:20 Why error correction is sphere packing in disguise 35:15 The breakthrough Astra almost didn't bother with 39:22 Solving harder problems means it has better taste 41:00 One model for taste, one for the grind 44:40 Astra found an infinite group no finite one can imitate 52:00 250 pages of quantum complexity, or 15 of group theory 56:18 Only humans write 200-page proofs 1:00:38 What changes when proving stops being the bottleneck 1:02:44 The problems AI may never solve YouTube: youtube.com/watch?v=1JvyLGd2… @mehtaab_sawhney @MarkSellke @OpenAI @lishali88