Latest in AI
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.
Google’s Gemini 3.8 Flash Puts AI Defense on a Tighter Leash
Google’s Gemini 3.8 Flash Cyber limits access to trusted defenders as it targets AI-assisted vulnerability discovery and patching, raising new questions about cybersecurity risks, governance and independent validation.
Perplexity Wants AI Agents to Know What Must Stay on Your Mac
Perplexity’s hybrid AI system splits agentic tasks between cloud models and local Apple silicon models, aiming to protect sensitive files while raising questions about privacy gates, auditing and trust.
Claude’s Watermark Could Start a New Race to Prove Who Wrote the Text
Anthropic’s planned statistical watermark for future Claude models could help verify AI-generated text, but raises concerns about false positives, rewriting, interoperability and an escalating detection arms race.
A Private Exam for AI Models: Inside DeepMind’s Double-Blind Evaluation Pilot
Google DeepMind’s double-blind AI evaluation pilot uses confidential GPU enclaves, remote attestation and controlled outputs to protect secret benchmarks and proprietary models, while exposing the limits of secure testing.