Browse AI news across models, agents, media, industry, and compute policy.
I read Renmin Univ. and Ant Group's Gilead paper on LLMs as diffusion models. It proposes a 2025-2026 industry roadmap replacing next-token prediction with diffusion processes.
Shanghai AI Lab beats DeepSeek in math via RL, not R1 distillation. I read the filing: ops trade-off is compute cost vs. raw reasoning gains for production models.
I watched leaders clash at the Paris summit over open versus closed models. While France pledged investment, I worry about creator rights in this new AI pulse.
Musk bids $71.18B for OpenAI. Altman retorts he'd buy Twitter for $7.11B instead. I read the filings; this is a valuation clash, not just drama.
I see US and Chinese clouds rushing to host DeepSeek via Microsoft and Nvidia. Meanwhile, OpenAI's $280B funding bid signals a desperate scramble for dominance in this rapidly consolidating AI infrastructure market.
I watched DeepSeek’s R1 drop, shaking up markets with open-source reasoning that rivals o1 at a fraction of the cost.
I watched $4T vanish from Nvidia as DeepSeek’s new model shook markets. My read: Efficiency threatens the hardware monopoly.
OpenAI's new Operator agent autonomously browses for bookings. I see this as a critical leap in AI autonomy, blending vision with action.
I read the Stargate announcement: $360B for OpenAI, SoftBank, Oracle infra. Ops take: CapEx is real; latency and power constraints will define the rollout speed.
CMU researchers use large models to optimize vision-language prompts without parameter access, a method I find promising for black-box scenarios.