Snowflake’s $200 million OpenAI deal — the second such deal after a matching Anthropic partnership in December 2025 — brings GPT-5.2 natively into Cortex AI for 12,600 enterprise customers. The real story isn’t the model integration: it’s the governance-aware coding agent, the no-code data interface, and a platform strategy designed to make the data warehouse the operational core of enterprise AI agents.
Every week, thousands of new ML papers land on arXiv — and no human team can act on them all. Autoscience just raised $14M from General Catalyst and Toyota Ventures to automate the entire research-to-deployment loop, from hypothesis generation to shipping improved models. An ICLR workshop paper and a Kaggle silver medal against 3,300 teams are early proof of concept — with important caveats.
Ninety percent of developers use AI daily, yet organizational delivery metrics have barely moved. The 2025 DORA report explains why: AI amplifies what is already there, making strong teams stronger and dysfunctional ones more chaotic. Here is what the data actually shows.
DeepSeek’s first major model since its January 2025 R1 disruption is finally close—a trillion-parameter multimodal system targeting frontier coding benchmarks and million-token contexts. But the AI landscape it’s launching into looks nothing like the one R1 disrupted.
A systematic literature review typically takes 3–12 months and is outdated before the ink dries. Elicit claims to cut that process by 80% through automated paper screening and data extraction — a claim that independent peer-reviewed studies largely validate, with important caveats on search sensitivity. This is a practical look at what works, what doesn’t, and what the March 2026 API launch means for institutional adoption.
In 14 months since a teenager died by suicide after conversations with a companion chatbot, the U.S. has passed chatbot safety laws in California, Oregon, and Washington — and 78 more bills are live in 27 states. This is what they require, where the gaps are, and what builders need to do now.
Every week, thousands of new ML papers land on arXiv — and no human team can act on them all. Autoscience just raised $14M from General Catalyst and Toyota Ventures to automate the entire research-to-deployment loop, from hypothesis generation to shipping improved models. An ICLR workshop paper and a Kaggle silver medal against 3,300 teams are early proof of concept — with important caveats.