Microsoft is introducing AI token budgets for employees, making the cheaper GPT-5.6 its default internal model and telling engineers to focus on business results rather than maximizing AI usage. 404 Media reports: "As we accelerate our use of GitHub Copilot to deliver on our goals, we all need to be aware of how we consume tokens," Jay Parikh, an executive vice president at Microsoft said in an email to Microsoft employees. GitHub is owned by Microsoft, and GitHub Copilot is an AI coding tool. "Tokenmaxxing is not what we are optimizing for. I want all of us focused on maximizing outcomes that move the needle for our customers and our business." "As such, we are updating our internal guidance and managing token spend with the same discipline we apply to every other critical resource," Parikh said in the email. Parikh's email says that in an effort to "get greater value from our token investment" Microsoft is making OpenAI GPT-5.6, which is cheaper to use than other models, the default model for internal use. His email also links to updated internal Copilot guidelines stating that, as of July 2026, Microsoft divisions will have an "AI token budget target," and that employees can track their individual AI spending. "While there is no target spend value being shared at this time. The data shows that many engineers spend in the range of hundreds of dollars a month to a few thousand dollars in tokens," the guidelines say. They also say that some decisions may place further restrictions as they monitor spend. [...] Parikh's email said Microsoft will keep learning and adjusting its AI policies as models and products evolve, and stressed that he doesn't want to slow down the company's progress towards becoming "AI-first." "We are not optimizing for fewer tokens," he said. "We are optimizing for more impact per token.

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Accusé par Apple d'avoir volé des informations confidentielles via d'anciens employés, OpenAI publie sa propre défense, messages internes à l'appui.

An anonymous reader quotes a report from Forbes: The cost of producing new results on ten longstanding mathematical problems just fell to $2,000, according to OpenAI, which says its Astra model generated machine-checkable proofs for questions that had resisted human progress for decades. OpenAI published the work on August 1 and used it to give its next major model family a name: Astra. The results run across group theory, high-dimensional geometry, coding theory, quantum complexity, lattice cryptography and extremal combinatorics. They arrived as a 249-page manuscript collection and, alongside it, something the field has not seen attached to an AI claim before at this scale: a machine-checkable certificate for every single result. The problems were not textbook exercises dressed up as discoveries. Each had been open for at least ten years, most of them far longer, and several sit at the center of their subfields: - A construction establishing the existence of non-sofic groups, a question that has occupied group theorists for years. - A disproof of Connes's rigidity conjecture, a long-standing problem in the theory of von Neumann algebras. - An improvement to the general upper bound on sphere-packing density in high dimensions, a bound that had stood since 1978. - Three problems come from the catalogue of open questions left behind by Paul Erdos. The announcement follows another result from May, when OpenAI used a similar reasoning model to produce an original mathematical proof disproving a famous unsolved conjecture in geometry, which was first posed by Paul Erdos in 1946.

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