Latest in AI
OpenAI’s Decision API Opens a New Contest Over Fast, Cheap AI
OpenAI’s Decisions API brings fast, bounded AI choices to market, challenging Jev and forcing businesses to weigh latency, calibration, cost and the consequences of mistakes.
Anthropic’s Proof of Model Identity Raises a Bigger Trust Question
Anthropic’s FlashProve prototype uses cryptographic proofs to verify which model produced an AI response, but provenance cannot show whether the answer is accurate, safe or trustworthy.
AI Debate Systems Can Turn Agreement Into a Reliability Trap
Multi-agent AI debate can create confident consensus around wrong answers, exposing why agreement is not verification and why enterprise workflows need independent validation, observability and stronger safeguards.
Why AI Buyers May Start Counting Tokens Per Finished Task
AI buyers may need to measure tokens per finished task, not just price per million tokens, as Reflection’s Beam model highlights the hidden costs of reasoning, tools and retries.
Ripple-65B Puts Open Coding Models Into a Licensing Fight
Ripple-65B Preview enters a fight over whether its Apache 2.0 license or benchmark performance makes it a serious alternative for developers building private AI coding systems.
Uber’s AI-Agent Test Reveals the Limits of the Agentic Web
Uber’s early AI-agent data shows consumers rarely use assistants for routine rides and food orders, challenging Silicon Valley’s vision of an agentic web and raising questions about where autonomous commerce can deliver real value.
Meta’s Conductor Deal Points to a New Race for Parallel Coding Agents
Meta’s acquisition of Conductor highlights a new AI coding race focused on coordinating parallel agents, managing workspaces and making software development faster without multiplying errors.
MiniMax M3.1 Flash Makes Cheap AI a Test of Trust
MiniMax M3.1 Flash promises faster, cheaper AI agents, but hallucinated citations and uncertainty raise difficult questions about supervision, verification and trust in real-world workflows for businesses.
Anthropic’s J-Space Opens a New Window Into What Models Do Not Say
Anthropic says its J-space research can reveal and causally alter silent model concepts, offering new tools for detecting evaluation awareness, deception and hidden goals, while exposing major limits for AI safety monitoring.