An AI workflow is the end-to-end sequence used to turn a problem and its data into a functioning artificial intelligence system. Depending on the application, it may include data collection, cleaning, labeling, model selection, training, evaluation, deployment, monitoring, and feedback. Generative AI workflows can also add retrieval, prompt construction, tool calls, guardrails, and human approval steps. Mapping the workflow makes dependencies and failure points visible: poor inputs can undermine a strong model, while missing monitoring can allow quality to decline unnoticed. Well-designed workflows define ownership, measurable acceptance criteria, data-handling rules, and fallback behavior for cases the system cannot handle safely.
Anthropic’s revenue per employee dwarfs Pinterest, DocuSign and Zoom. The bigger risk may be old software’s pricing power as AI platforms take on more work.
Anthropic’s IPO filing puts Dario Amodei’s 2025 security bill at $2.75 million, about 4.2x his salary. Daniela Amodei’s protection cost another $1.29 million.
OpenAI will pool ChatGPT and Codex usage on Plus, Pro and Business plans in December. Instant chat is exempt, with credits available for eligible features.
OpenAI has about $11 billion in commitments against an original $30 billion goal, WSJ reports. Its revised $20 billion round remains open.
House Democrats want Trump’s AI conversations preserved after reports he consulted Grok about Venezuela, raising questions about military advice and oversight.
Some employers are restoring roles after AI-led cuts as experience, oversight and the work outside a job description prove harder to replace.
Google ran Gemma 4 on an orbiting TPU and received a haiku back. The Suncatcher test now moves toward radiation, heat and networking challenges.
Recall Foundation plans to burn 358.6 million RECALL as it winds down. Token holders will receive no buyback or treasury distribution.
Some Amazon engineers reportedly ran unnecessary AI tasks to climb usage rankings. The company is warning against turning token consumption into a target.
FOMAT means Fear of Missing Agent Time: the pressure to keep coding agents working while you step away. More runtime does not always mean more progress.