[AINews] Jensen Huang resigns after 33 years leading NVIDIA

Congrats to Gemini 4 Hydrogen on their launch and it seems Anthropic’s IPO is well under way, but today’s headline was only ever going to be about one thing, the most earthshaking leadership change in AI (after Demis/Jeff/et al at Google DeepMind). Yesterday we covered reports that NVIDIA’s board was preparing to replace Jensen Huang following an NYT report of an affair with an accused Chinese spy . Following news of a second affair , today NVIDIA announced his resignation (correlation/causation is debatable here), presumably forced by the board: View post on X ↗ There is also some discussion of the extent that Chinese models can demonstrably be shown to be trained on Blackwell GPUs that they should not have access to... though the scale is debated . After 33 years, Jensen Huang has stepped down as CEO, president and boardmember. Colette Kress, NVIDIA’s CFO since 2013, will keep that job while serving as interim CEO... leading into what will presumably the best attended earnings call in the history of $NVDA next week. AI News for 11/11/2026-11/12/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews' website lets you search all past issues. As a reminder, AINews is now a section of Latent Space . You can opt in/out of email frequencies! AI Twitter Recap Gemini 4 Hydrogen: Google's Fast Gemini 4 Tier Ships Broadly Launch : Google DeepMind released Gemini 4 Hydrogen, a speed-first model positioned alongside Carbon and Argon. Google says it completes tasks 2.5x faster than Gemini 3.8 Flash and edges Argon on the Artificial Analysis Intelligence Index, 51 vs 50 at default thinking and 54 at the new xhigh level ( DeepMind , Koray Kavukcuoglu ). Specs and pricing : The model offers four thinking levels (low, medium, high and xhigh) and a 1M context window. It costs $1.50/$7.50 per million input/output tokens, with a 95% discount on cached input ( Phil Schmid , Artificial Analysis ). Official claims : Google reports 57% on Terminal-Bench 4.0 and more than 3x Gemini 3.8 Flash on Terminal-Bench 4.1. It also claims more than 5x fewer fabricated claims, #1 on Vals Tax Agent Bench and leading results on AutomationBench-AA and Agents' Last Exam ( Google ). Availability : Hydrogen is available on all Gemini API tiers, including a free tier in AI Studio. It is the default model in Search AI Mode, and free Gemini app users get it through auto-selection. Paid Pro and Ultra subscribers can use it in Antigravity ( Sundar Pichai , GeminiApp , Search ). Orchestrator multi-model mode : In Antigravity, Carbon plans while Hydrogen writes and tests code. Google says this reaches near-Carbon success on Terminal-Bench 4.0 with 24% less quota and 19% faster completion. It is rolling out to Ultra subscribers ( Google , Mohan ). Artificial Analysis measurements : The independent results show a mixed profile. Intelligence and cost : At xhigh, Hydrogen scores 54, one point behind Claude Sonnet 5.5 (max) and GPT-6.1 Sol (max). Its cost is about 32% of Sonnet's but about 2.5x Sol's. Cost per task is $3.16 at xhigh and $1.87 at high. Output speed is about 200 tok/s ( AA ). Strengths : Hydrogen has an 8% hallucination rate on AA-Omniscience, versus 43% for Sonnet and 52% for Sol. It also shares the #1 score on GDP.pdf visual reasoning. Weaknesses : It scores 54% on Terminal-Bench 4.0 at both high and xhigh, below Sonnet at 64% and Sol at 56%. Third-party leaderboards : Agent Arena : Hydrogen debuts at #5 with +14.8% net improvement across 6.5k sessions. It is the fastest model in the top 10 at 5.3 minutes per task median, with a median cost of $1.6 per task ( Arena ). Code Arena WebDev : It ranks #10 with 1703 points ( Arena ). Vals Index : It is #6 at 68.5%, 2 points behind Carbon at 38% of Carbon's price ( Vals ). Coding agents : CursorBench rises to 54.3% from 39.6% for 3.8 Flash ( Cursor ). Cognition's FrontierCode 2.0 shows 38.2%, just behind Sol ( Cognition ). SWE-sweep shows 13.5%, versus 9.5% for Astra ( KLieret ). Caveats from users : Runaway reasoning : One tester saw xhigh spend 131K thinking tokens and about $1.08 without answering a jersey-number image question. The same tester reports budget exhaustion on 1.8% of a text benchmark ( skalskip92 ). Weak spots : Hydrogen scored 1% on MazeBench ( patience_cave ). Pricing structure : Simon Willison says Hydrogen is only a 25% discount on the Carbon/Argon token price, though those models reportedly double in price on Dec 1 ( simonw ). Speed : Theo disputes the "built for speed" framing based on his own timings ( theo ). Open-Weight Releases: Inkling 2 Small Leads Inkling 2 Small : Thinking Machines released the weights under Apache 2.0 ( thinkymachines , Mira Murati ). Size : It is a 594B-total / 17B-active MoE with text and image input and up to 1M context ( HF ). Architecture : New components include attention residuals with a q/k/v split, gated attention, a dynamic attention sink, dynamic shortconv and value norm ( Songlin Yang ). Experts use NVFP4 in layers 0–39 and MXFP8 in the last eight, with SWA widened to 1k and softcaps on the LM head and SwiGLU ( eliebakouch ). Day-0 serving : On vLLM, it fits on one GB200/GB300 node and reaches up to 630 tok/s per user at TP4. Its native DSpark speculator has an acceptance length of about 3.5 at T=1.0 ( vLLM , details ). SGLang reports 576 tok/s per user at batch 1 on Vera Rubin and text-plus-vision RL on 8 Blackwells ( SGLang ). SkyRL validates MXFP8, BF16 and LoRA RL ( erictang ). Evals : It is the #1 US open model on Code Arena WebDev at 1647 points ( Arena ). On Agent Arena it is #2 among open models, with a median cost of $0.26 per task ( Arena ). It also holds the top open-model spot on MathArena's BrokenArXiv ( MathArena ). Edge0-145B/120B : These are device-native open-weight models. The developer claims 86.5% on Terminal-Bench 2.1, versus 78.2% for Claude Opus 4.6, in about 29GB of memory at 75+ tok/s decode ( Samuel Zeng ). These figures are vendor claims. Perplexity Decider v1.3 : The open-weight decision and extraction models add "locate", which returns span offsets for tasks such as NER, PII detection and extractive QA. Perplexity says the release is 6.54 points more accurate than its production classifiers on 63 tasks, and the models are free on OpenRouter for one week ( Aravind Srinivas ). Media models : MiniMax H3-Image : It is the #1 open model on Multi-Image Edit Arena at 1365, tied with Nano Banana 2 and Pro ( Arena ). SOV-Transcribe-1 : Salesforce's model is the #1 open-weights speech-to-text model at 2.3% AA-WER, with 4.6B active parameters ( AA ). Checkpoint Forensics, Chinese Compute and Export Controls GPU fingerprinting claim : Will Depue says initialization patterns in released weights identify the GPU type that produced them ( report ). Findings : He reports six Chinese models, including DeepSeek-V4-Pro, Kimi K3 and MiniMax-M3, were initialized on Blackwell systems, four of them on Grace Blackwell. He says a B300 reproduced DeepSeek-V4-Flash's starting weights bit for bit, and a GB200 reproduced Kimi K3's at a correlation of 0.999995. Validation : He reports a 24-for-24 match on models with publicly disclosed hardware and match strengths of 49–1,385 sigma. He says Kimi K3's weights were created by at least 64 processes, all on Grace Blackwell ( rebuttal ). Scale estimate : He estimates 2–3K Blackwells each for the largest runs. He says senior OpenAI and Anthropic researchers verified the work but are not on record ( request for verifiers ). Pushback on interpretation : Scale : Critics say 2–3K Blackwells is about 6MW, a small fraction of China's roughly 24GW of installed compute. On that view, the result could indicate export controls are working rather than failing ( teortaxesTex , scale note ). Ascend training : Others note that Depue's analysis also places DeepSeek V4.1 Flash and LongCat on Ascend chips ( zephyr ). Novelty : Some researchers say Blackwell use by Chinese labs was already widely known ( eliebakouch ). Proposed Comm