Sep 9, 2026 · 4 min listen · Last updated September 9, 2026
From storyflo. This is your daily audio brief. Hey, it's Theo. September 9th. Five things in tech that mattered this morning — let's start with the one that surprised me most. Let's get into it. First, from The Decoder. OpenAI's millennium proof dispute raises the question of whether researchers can trust AI labs.
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OpenAI's millennium proof dispute raises the question of whether researchers can trust AI labs
The fight over an AI-generated proof of a millennium problem is heating up. Mathematician Tristan Buckmaster accuses OpenAI of academic fraud, CEO Sam Altman rejects the allegations. Terence Tao warns that cases like this could "reverse centuries of tradition in open science." The article OpenAI's millennium proof dispute raises the question of whether researchers can trust AI labs appeared first on The Decoder.
Anthropic scientist puts the odds of AI destroying humanity above ten percent this decade
Jacob Coxon, a former pretraining researcher at OpenAI and Anthropic, has quit and accuses both companies of knowingly risking human extinction. Anthropic colleague Evan Hubinger puts the odds of a misaligned superintelligent AI wiping out humanity within the next decade at more than ten percent. The article Anthropic scientist puts the odds of AI destroying humanity above ten percent this decade appeared first on The Decoder.
Deepmind's AlphaGenome Atlas maps every possible DNA change in the human genome
Google Deepmind has used the AlphaGenome Atlas to predict what each of the roughly nine billion possible single-letter changes in the human genome could do. The dataset spans one petabyte, more than 30 times the size of the AlphaFold database. In one epilepsy case, the atlas helped pinpoint a previously overlooked variant as the likely cause. The article Deepmind's AlphaGenome Atlas maps every possible DNA change in the human genome appeared first on The Decoder.
Samsung taps Mistral AI models for semiconductor manufacturing
The collaboration aims to address the increasing complexities in AI chip design and manufacturing. Samsung’s CEO of the Device Solutions Division highlighted the need for ongoing innovation in semiconductor technologies. Mistral will provide specialized software tools that will help streamline the design and production of processors.
Samsung plans to use these AI models for specific tasks like automated defect detection and optimizing machinery within its manufacturing plants. This approach is expected to speed up development cycles, enhance manufacturing accuracy, and stabilize production yields, particularly in advanced memory and logic chips. Additionally, Samsung's investment in Mistral’s Series D funding round signifies a commitment to fostering long-term collaboration between semiconductor manufacturers and AI developers. This partnership is part of a broader trend of integrating AI into complex technological processes, aiming to improve efficiency and innovation in the industry.
What OpenAI’s latest controversy tells us about the future of math
OpenAI recently announced a solution to the Navier-Stokes existence and smoothness problem, a significant milestone in mathematics. However, this achievement has been overshadowed by controversy over whether they appropriately credited NYU mathematician Tristan Buckmaster and Anthropic employee Levent Alpöge, who had been working on the problem using AI models. OpenAI claims their work was inspired by rumors of Buckmaster and Alpöge's efforts, but there are questions about whether they accessed or utilized their research.
The Navier-Stokes equations are crucial in understanding fluid dynamics, and the implications of this solution could reshape the landscape of mathematical research. While Buckmaster and Alpöge made substantial progress over nearly a year, OpenAI's team reportedly solved the problem in a matter of days using a powerful internal model, running thousands of agents concurrently at a significant cost. This raises concerns about the future of mathematics, as it appears that only a few AI companies possess the resources to tackle these complex problems.
The situation highlights a troubling trend: as AI companies dominate mathematical research, human mathematicians may find themselves sidelined. Experts worry that this could stifle collaborative efforts that have historically driven the field forward. Terence Tao emphasized the importance of human-directed exploration in mathematics, suggesting that AI's rapid solutions might hinder the development of new ideas and approaches. The balance between human intuition and AI's computational power remains a critical conversation as we look to the future of math.