Researchers at Georgetown University have analyzed thousands of procurement requests from China’s People’s Liberation Army. The documents reveal how broadly Beijing is already experimenting with military AI, from drone swarms and deepfake tools to autonomous decision-making systems.
Anthropic is capitalizing on OpenAI’s bad press with a new import function for Claude. A single prompt exports your saved context from ChatGPT or other chatbots, letting you transfer it straight to Claude’s memory.
Artificial Analysis has released version 2.0 of its AA-WER speech-to-text benchmark. ElevenLabs' Scribe v2 leads with a word error rate of just 2.3 percent, followed by Google's Gemini 3 Pro (2.9%) and Mistral's Voxtral Small (3.0%). Google's Gemini 3 Flash (3.1%) and ElevenLabs' older Scribe v1 (3.2%) are close behind. Notably, Google didn't specifically train for transcription—the strong results come from Gemini's general multimodal capabilities. OpenAI's popular open-source Whisper Large v3 (4.2%) lands mid-pack, while Alibaba's Qwen3 ASR Flash (5.9%), Amazon's Nova 2 Omni (6.0%), and Rev AI (6.1%) bring up the rear.
The results hold up in the separate AA-AgentTalk test for speech directed at voice assistants: Scribe v2 (1.6%) and Gemini 3 Pro (1.7%) pull well ahead, with AssemblyAI's Universal-3 Pro taking third at 2.3%.
The latest generation of large language models—from GPT-5 onward—still struggles when tasks are spread across multiple conversation turns. Researcher Philippe Laban and his team tested current models on six tasks covering code, databases, actions, data-to-text, math, and summarization. Performance drops significantly when information is split across several messages (sharded) instead of a single prompt (concat).
Newer models did slightly better—performance degradation shrank from 39 to 33 percent—but the issue is far from solved. The biggest gains showed up in Python tasks, where some models only lost 10 to 20 percent. Laban suspects real-world losses could be even worse, since the tests used simple user simulations. Users who change their mind mid-conversation would likely cause steeper drops.
Technical tweaks like lowering temperature values don't fix the problem, the original study found. The researchers recommend starting a fresh conversation when things go sideways, ideally by having the model summarize all requests first and using that summary as the starting point for a new chat.
Anthropic says it will take the US government to court after Secretary of Defense Pete Hegseth moved to classify the AI company as a supply chain risk, a designation previously reserved for foreign adversaries. Anthropic calls the classification illegal and says it will "challenge any supply chain risk designation in court."
We believe this designation would both be legally unsound and set a dangerous precedent for any American company that negotiates with the government.
Anthropic
Hegseth also implied military suppliers should no longer be allowed to do business with Anthropic. But according to Anthropic, there's no legal basis for that move: the classification under 10 USC 3252 only applies to the use of Claude in direct contracts with the Department of Defense. For private customers, commercial contracts, and access through the API or claude.ai, nothing would change.
The conflict traces back to a failed negotiation process. Anthropic refused to release Claude for mass domestic surveillance and fully autonomous weapons systems, arguing that current AI models are too unreliable for these purposes and that mass surveillance violates fundamental rights. OpenAI has since taken over the deal.
Anthropic's dispute with the Pentagon is now rippling through Google and OpenAI. According to the New York Times, more than 100 Google AI employees sent a letter to chief scientist Jeff Dean—who had previously voiced support for Anthropic's position—demanding that Google draw the same red lines: no surveillance of American citizens and no autonomous weapons without human oversight through Gemini. Separately, nearly 50 OpenAI and 175 Google employees published an open letter criticizing the Pentagon's negotiating tactics.
We hope our leaders will put aside their differences and stand together to continue to refuse the Department of War's current demands for permission to use our models for domestic mass surveillance and autonomously killing people without human oversight.
Quote from the open letter "We will not be divided"
According to the Wall Street Journal, OpenAI CEO Sam Altman told his employees that OpenAI is working on its own Pentagon contract that would include the same safety guidelines Anthropic is pushing for. Altman hopes to find a solution that works for other AI companies as well.
Meta has signed a multi-year, multi-billion dollar contract with Google to rent its AI chips—Tensor Processing Units (TPUs)—for developing new AI models. That's according to The Information. Meta is also looking into buying TPUs outright for its own data centers starting next year.
The deal takes direct aim at Nvidia, which dominates the AI chip market and has been Meta's go-to GPU supplier for AI training. Just days earlier, Meta had announced plans to buy millions of GPUs from Nvidia and AMD. Internally, Google Cloud executives have set a goal of capturing up to ten percent of Nvidia's annual revenue—roughly $200 billion—through TPU sales. Google has also launched a joint venture with an investment firm to lease TPUs to other customers.
Here's where it gets complicated: Google itself is one of Nvidia's biggest customers, since cloud customers still expect access to GPU servers. So Google has to keep buying Nvidia's latest chips to stay competitive in the cloud market, while simultaneously trying to eat into Nvidia's market share with its own silicon. OpenAI reportedly managed to negotiate 30 percent lower prices from Nvidia simply because TPUs exist as an alternative.
