I’m excited to share that I’ll be joining OpenAI and look forward to working with the exceptional team there. It was a difficult decision to move on. I’m incredibly proud of the amazing team at Google and everything we’ve built together. It has been an honor and a pleasure to work with all of you.

We've raised $65 billion in Series H funding at a $965 billion post-money valuation, led by @AltimeterCap, Dragoneer, @Greenoaks, and @sequoia. This investment will help us advance our research and expand our capacity to meet growing demand for Claude.

09:57:50 [Liang Wenfeng Reportedly Speaks Internally; DeepSeek V4 to Launch in Late April] According to multiple sources speaking to media, DeepSeek founder Liang Wenfeng recently revealed in internal communications that DeepSeek's next-generation flagship model, DeepSeek V4, will officially launch in late April. Recently, DeepSeek's web interface quietly introduced two interaction modes — "Quick Mode" and "Expert Mode" — supporting concise search and long-form, complex problem-solving, sparking industry speculation about the V4 model release. Related reports indicate that DeepSeek V4 will not only feature trillion-scale parameters and a million-token-class context window, but will also for the first time achieve deep optimization for domestic Chinese chips such as Huawei Ascend, marking a critical step in China's AI industry toward "de-CUDAification." Foreign media reports suggest that in anticipation of the V4 launch, domestic tech giants including Alibaba, ByteDance, and Tencent have pre-ordered hundreds of thousands of next-generation AI chips, planning to offer DeepSeek's new model via cloud services and integrate it into their own AI products. As a result, prices of new AI chip products have risen approximately 20% recently. As of press time, DeepSeek has not responded to inquiries. (Sina Tech)

Our run-rate revenue has surpassed $30 billion, up from $9 billion at the end of 2025, as demand for Claude continues to accelerate. This partnership gives us the compute to keep pace. Read more: anthropic.com/news/google-br…

Atoms. Http://atoms.co/vision

.@dylan522p gives a deep dive on the 3 big bottlenecks to scaling AI compute: logic, memory, and power. And walks through the economics of labs, hyperscalers, foundries, and fab equipment manufacturers. Learned a ton about every single level of the stack. 0:00:00 – Why an H100 is worth more today than 3 years ago 0:24:52 – Nvidia secured TSMC allocation early; Google is getting squeezed 0:34:34 – ASML will be the #1 constraint for AI compute scaling by 2030 0:56:06 – Can’t we just use TSMC’s older fabs? 1:05:56 – When will China outscale the West in semis? 1:16:20 – The enormous incoming memory crunch 1:42:53 – Scaling power in the US will not be a problem 1:55:03 – Space GPUs aren't happening this decade 2:14:26 – Why aren’t more hedge funds making the AGI trade? 2:18:49 – Will TSMC kick Apple out from N2? 2:24:35 – Robots and Taiwan risk Look up Dwarkesh Podcast on YouTube, Apple Podcasts, or Spotify. Enjoy!