Latest in AI
Anthropic Tests a More Flexible Enterprise Model With Opus 5
Anthropic’s Opus 5 targets enterprise users with lower costs, fewer unnecessary refusals, automatic fallbacks and distinct privacy controls, testing a more flexible approach to safe, reliable AI deployment.
OpenAI’s Research Intern Could Make AI Progress a Race for Speed
OpenAI says supervised AI agents are helping researchers design experiments, investigate failures and improve models, potentially turning AI development into a race for research speed while raising questions about oversight, computing and talent.
Gemma 4 Turns Speculative Decoding Into a Practical Local AI Decision
Gemma 4’s MTP drafters make speculative decoding a practical local AI decision, explaining how shared KV caches, accepted-token rates, batching, and workload testing determine whether faster generation delivers real-world gains.
OpenAI’s Wiki Incident Tests the Rules for AI-Agent Disclosure
OpenAI’s wiki incident exposes a gap in cybersecurity rules for autonomous AI agents, raising urgent questions about containment, evidence preservation, public disclosure and accountability when systems act beyond their intended environments.
AI’s Overlapping Outages Put Reliability at the Center of Model Competition
Overlapping disruptions at ChatGPT, Claude, Grok and Gemini expose the AI industry’s reliability gap, pushing enterprises to rethink redundancy, failover, service transparency and non-AI fallback plans.
Meta’s 95% Muse Spark Discount Puts a Price on Enterprise Data
Meta is offering a 95% Muse Spark discount to companies sharing prompts and outputs for training, raising new questions about enterprise AI costs, confidentiality and competition.
OpenAI Puts Computer-Using AI Workers at the Center of Enterprise Strategy
OpenAI’s GPT-6 Astra is designed to operate enterprise software, promising a new digital workforce while raising urgent questions about reliability, cybersecurity, accountability, cost and the future of office work.
Anthropic Turns the Agent Runtime Into the Product
Anthropic’s Managed Agents architecture separates model reasoning, tool execution, and durable session history to make long-running AI agents more recoverable, observable, secure, and ready for production workloads.
AI Models May Know More Than They Can Recall
A Google Research and Technion study suggests AI models may encode facts they fail to recall, changing how developers approach hallucinations, reasoning, retrieval and reliability.