📰 AI News Daily — 16 Nov 2025

TL;DR (Top 5 Highlights)

• Google’s Gemini 3 is reportedly imminent, beating key coding benchmarks and aiming to challenge ChatGPT across search, productivity, and media.
• AMD signed a multiyear AI chip deal with OpenAI, including an option for a 10% equity stake—reshaping competitive pressure on NVIDIA.
• Microsoft confirmed a 27% stake and deep IP access to OpenAI through 2032, cementing control across cloud, chips, and model commercialization.
• The infrastructure race escalated: OpenAI/Microsoft unveiled mega GPU clusters, Google committed $40B to Texas data centers, and NVIDIA-powered clouds surged.
• Anthropic reported thwarting a major cyber-espionage attempt and warned of more sophisticated AI-enabled attacks by 2026, elevating security urgency.

🛠️ New Tools

• The Station launched an open-world sandbox for autonomous science agents, enabling end‑to‑end experiments and analysis. It lowers barriers to self‑directed research and accelerates reproducible discovery loops.
• AgentEvolver introduced self-improvement loops—self‑questioning, navigation, and attribution—to make agents more reliable with less human oversight. Expect steadier task completion and fewer brittle failures.
• Google Colab now connects directly to VS Code, combining familiar local dev workflows with managed GPUs/TPUs. It shortens iteration cycles for researchers and students building and training models.
• GitHub Copilot added a study companion that breaks down complex topics and keeps learners on track. Persistent, personalized guidance improves retention and reduces context-switching.
• OpenAI’s ChatGPT added group chats for up to 20 participants, enabling real‑time collaboration, brainstorming, and summaries. Teams gain faster alignment and less meeting overhead.
• Google’s Gemini Veo 3.1 lets creators add reference images to text prompts for precise video generation. It boosts creative control for advertisers and social teams while cutting production costs.

🤖 LLM Updates

• OpenAI’s GPT‑5.1‑high added vision+text multimodality, improving reasoning over images and documents. Stronger cross‑modal comprehension broadens use cases in analysis, support, and content workflows.
• OpenAI’s GPT‑5.1 Codex topped Anthropic’s Claude Sonnet 4.5 Thinking on SWE‑Bench at lower cost, signaling stronger code automation economics for teams shipping and maintaining complex software.
• Google’s Gemini 3 reportedly surpassed 80% verified on SWE‑Bench and is expected imminently. If confirmed, it resets the coding and reasoning leaderboard against OpenAI.
• Google cut hallucinations by 40% and expanded Gemini context windows to 1 million tokens. Better reliability and long‑document handling unlock safer enterprise and research deployments.
• Baidu’s ERNIE 5.0 delivered a more polished step over 4.5, narrowing gaps with top US labs in practical tasks while still trailing on frontier benchmarks.
• Open‑source momentum: MiniMax M2 led select public tests; Kimi K2 Thinking showed long‑horizon reasoning with efficient INT4 quantization; Sherlock‑Alpha neared Grok‑4 on LisanBench, suggesting RL gains in smaller models.

📑 Research & Papers

• New findings show robots powered by LLMs can exhibit biased or hazardous behaviors. The work underscores urgent needs for transparency, auditing, and safety controls in embodied AI.
• EZSpecificity achieved 91.7% accuracy predicting enzyme‑substrate interactions, promising faster drug discovery and synthetic biology by narrowing wet‑lab search spaces and costs.
• Analyses suggest around 20% of ICLR peer reviews may be AI‑generated. Academic norms are shifting, raising questions about disclosure, evaluation quality, and reviewer incentives.
• Interpretability and honesty studies explored models explaining internal mechanisms and simple training interventions improving truthfulness—practical steps toward more trustworthy systems.
• Reports of a large autonomous AI‑enabled cyberattack highlight a new threat era. Security research is pivoting to defensive AI that detects, contains, and learns from adaptive adversaries.

🏢 Industry & Policy

• AI infrastructure surged: OpenAI and Microsoft unveiled massive GPU clusters, Google committed $40B to Texas data centers, and NVIDIA’s ecosystem powered CoreWeave and Nscale. Expect cheaper, abundant compute regions.
• AMD and OpenAI forged a multiyear chip pact with an option for OpenAI to buy up to 10% of AMD. It intensifies competition and diversifies supply beyond NVIDIA.
• Microsoft secured broad access to OpenAI IP and a 27% stake through 2032, locking strategic influence across models, Azure, and custom silicon—stabilizing product roadmaps for enterprises.
• Google faces a class‑action lawsuit alleging Gemini AI secretly recorded private conversations in Gmail, Chat, and Meet. The case spotlights high‑stakes privacy governance for ambient assistants.
• Apple tightened data‑sharing rules for AI apps, requiring explicit user consent ahead of its Siri overhaul. Privacy‑forward defaults set a higher bar for third‑party AI integrations.
• AI startup funding remained torrential: Cursor ($2.3B), d‑Matrix ($275M), and Scribe ($75M) led rounds. Investor confidence favors tools that accelerate software delivery and operational efficiency.

📚 Tutorials & Guides

• Google published a production AI agents playbook emphasizing CI/CD, evaluation harnesses, and agent‑to‑agent protocols—turning prototypes into reliable, maintainable systems at scale.
• A visual AWS guide and overview of eight RAG architectures showed how to balance latency, accuracy, and cost when building retrieval‑augmented applications.
• Jane Street’s talk shared GPU training tactics—profiling, kernel optimizations, and memory discipline—to extract more performance from modern hardware without ballooning bills.
• A practical guide on giving constructive, reasoned feedback helps teams steer model behavior, reducing vague prompts and improving iterative outcomes.
• The RLHF Book opened discounted early access, offering practitioners a grounded overview of preference modeling, safety trade‑offs, and evaluation practices.
• The free “Agents in Production” conference (OpenAI, Meta, Google speakers) promises hard lessons on deployment, monitoring, and failure modes from real-world systems.

🎬 Showcases & Demos

• At ParisVibeathon, teams built a voice‑driven proposal generator in under 10 hours using Gemini 2.5 Pro, ElevenLabs, and Qdrant—proof that orchestration of mature components now yields overnight MVPs.
• OpenAI’s Sora app went public in select regions, surpassing one million downloads in five days. Ten‑second custom videos hint at mainstream creative workflows shifting to AI.
• Google DeepMind unveiled SIMA 2, a generalist gaming agent powered by Gemini models. Transfer across games suggests broader potential for robotics, simulation, and autonomous systems.
• Disney partnered with Animaj to slash animation timelines via AI in‑betweening. Studios can iterate faster while preserving creative direction, reshaping production economics.
• Google rolled out Gemini‑powered shopping in Search and the app—natural‑language queries, real‑time inventory, and agentic checkout—streamlining holiday commerce and raising expectations for retail experiences.

💡 Discussions & Ideas

• Yann LeCun criticized the field’s fixation on ever‑larger models and warned about regulatory capture curbing open‑source. The debate centers on innovation speed versus centralized control.
• Some argue researcher time, not compute, is the true bottleneck. Better tooling, evaluations, and automation may unlock more progress than chasing bigger clusters alone.
• A browser‑centric future is emerging, where the web acts as a universal virtual machine. Agents navigating pages could unify apps, data, and workflows under open standards.
• Leaders like Satya Nadella and Alex Karp pressed for broad AI empowerment and US leadership, while others argued recent releases may actually lengthen AGI timelines.
• Practitioners urged moving beyond YOLO to transformer‑based vision for robustness. The conversation highlights practical trade‑offs between legacy pipelines and modern architectures.
• Engineers contrasted PyTorch’s deep systems work with GPT app‑building, underscoring that seemingly simple apps often hide complex orchestration, evaluation, and data plumbing.

Source Credits

Curated from 250+ RSS feeds, Twitter expert lists, Reddit, and Hacker News.