Groq announced on December 24 that it had entered a non-exclusive licensing agreement with Nvidia for its inference technology, with founder Jonathan Ross, president Sunny Madra and other staff joining Nvidia. Groq stated it will continue as an independent company with Simon Edwards as CEO and that GroqCloud keeps running; Groq's own post did not disclose a price. CNBC reported roughly $20 billion in cash, sourced to Groq's lead investor, and published an internal Nvidia memo in which Jensen Huang wrote the company is licensing Groq's IP but is not acquiring Groq as a company.

The structure matters as much as the number: a licensing-plus-talent deal can sidestep the merger filing a straight acquisition would trigger, the same pattern Microsoft used with Inflection in 2024. Groq's LPU was the clearest commercial challenge in low-latency inference, the segment where Nvidia faces AMD, Google's TPUs and Amazon's Trainium rather than the training market it already owns. Huang's stated plan is to fold Groq's low-latency processors into the Nvidia AI factory architecture.

The Verge reported on December 23 that the version of New York's Responsible AI Safety and Education Act signed by Governor Kathy Hochul was a rewrite substantially more favorable to industry than the bill passed by both chambers in June. An AI Alliance ad campaign against the legislation ran from November 23, spent an estimated $17,000-$25,000 and may have reached more than two million people, per Meta's Ad Library; the coalition includes Meta, IBM, Intel, Oracle, Uber, AMD and Hugging Face, alongside New York University, Cornell, Dartmouth, Carnegie Mellon, Northeastern and Yale Engineering. As enacted, the law reaches frontier developers over a compute threshold with incident reporting to the attorney general, and exempts academic research.

It follows two earlier beats in the same arc: California's SB 53, signed in September 2025, and the White House executive order of December 11 aimed at preempting state AI rules, against which the RAISE Act was signed days later. Reporting and legal analyses note the enacted text is narrower than the June version; the Justice Department could still test it on interstate-commerce grounds.

In a security post dated December 22, OpenAI described shipping a security update to ChatGPT Atlas's browser agent, including a newly adversarially trained model and additional safeguards, after internal automated red teaming surfaced what it called a new class of prompt-injection attacks. The attacker is an LLM trained end-to-end with reinforcement learning that proposes candidate injections, runs them against a simulator of the victim agent, reads the resulting reasoning and action trace, and iterates before committing to a final attack. OpenAI says this found novel, long-horizon exploits its human red team and external reports had missed.

The demo it published is concrete: a malicious email seeded in the inbox causes the agent, asked to draft an out-of-office reply, to instead send a resignation letter to the user's CEO. OpenAI's own framing is the notable part — prompt injection, much like scams and social engineering on the web, is unlikely to ever be fully solved — alongside the admission that agent mode expands the security threat surface. Its mitigations are hedging rather than fixing: logged-out mode, confirmation prompts for consequential actions, and narrower instructions instead of broad ones.

Z.ai published GLM-4.7 on December 22 with weights on Hugging Face and ModelScope, a technical blog dated the same day, and support for vLLM and SGLang. The model is a mixture-of-experts design: the Hugging Face card lists 358B parameters, and independent trackers put active parameters at roughly 32B per token, with a 200,000-token context window. The license is MIT, which permits commercial self-hosting with no usage caps or acceptable-use riders.

Against GLM-4.6 it gains 5.8 points to 73.8% on SWE-bench Verified, 12.9 points to 66.7% on SWE-bench Multilingual, and 16.5 points to 41.0% on Terminal Bench 2.0; it scores 87.4% on tau-squared-Bench and 95.7% on AIME 2025, matching or beating GPT-5.1-High and Gemini 3.0 Pro on several of those. Z.ai also shipped GLM-4.7-Flash, a 30B-A3B variant small enough to run on a 16GB machine, and the model is wired into Claude Code, Cline, Roo Code and Kilo Code. The API is listed at $0.60 per million input tokens and $2.20 per million output tokens.