I just finished Logan Wright's new book on the Chinese economy. It provides a great deal of data to support the claim that China’s enormous credit expansion is running out of room, leaving Beijing under growing pressure to rely on exports to keep economic activity from declining.

空置写字楼灯火通明 近日，一博主发布视频，夜间实地探访上海的写字楼，以了解这些写字楼的空置情况。 博主进入多层办公区域，发现一些楼层大面积没有公司入驻，部分办公室已经搬空。 一些写字楼从外面看仍然“灯火通明”，但进入楼内后却发现，不少亮灯区域只是走廊等公共区域，办公室内并没有人。 根据多家房地产咨询机构公布的2026年第二季度数据，上海甲级写字楼整体空置率约在22%至23.5%之间。 其中仲量联行（JLL）数据显示：上海甲级办公楼整体空置率 23.5%；其中中央商务区 19.3%，非中央商务区 27.1%，这意味着平均每四平方米左右的甲级办公面积中，就有超过一平方米处于空置状态。

The Trump Administration files a statement of interest in the copyright lawsuit brought by The New York Times against OpenAI, taking the position that it’s fair use to train an LLM on copyrighted text.

There’s a big, under-appreciated reason why people may have very different experiences and opinions about using AI for work — are they using it for tasks they’re already an expert at, or tasks they can’t do themselves? The former leads to a *growth cycle* and the latter leads to a *dependence spiral*. When I use AI to do something I’m an expert at, like coding, I treat it as a tool. I can build quickly, maintaining an understanding of the code, knowing that if necessary, I can fix the code myself. It feels empowering. It frees up my time to think about the complex, judgment-oriented parts of software engineering that I can’t or won’t delegate to AI. That means my own skills improve rapidly, and I get to climb the ladder of complexity and develop higher-level skills, much more so than when I write the code myself. I feel in control. I can lock in and achieve a flow state — when AI is working, I’m reviewing, building understanding, and planning the next steps. I never get the feeling that the tool is about to replace me. This is the growth cycle. (Of course, the growth cycle is not automatic. I still need to exercise agency to use AI responsibly. But it’s the same challenge with any productivity-enhancing technology, and those who’ve navigated such transitions before are well-equipped to navigate it with AI as well.) On the other hand, if I use it for tasks I don’t understand and haven’t learned to perform myself, I have no choice but to treat it as a superintelligence. If something breaks, the best I can do is ask AI to fix it and hope for the best. I generally can’t evaluate the quality of the output myself. The only way to find out if it's any good is if and when the work is ultimately reviewed by an actual expert. The experience is confusing, unsettling and disempowering. And forget about flow state. By over-relying on AI, I risk losing whatever skill I had at the task in the first place, even if it boosts productivity in the short term. This is the dependence spiral. It’s no wonder that entry-level workers and students preparing to enter the workforce find themselves in a bind. To compete with the AI-enabled productivity of more seasoned workers, they must adopt AI themselves, but doing so risks the dependence spiral. I have some thoughts on solutions that I will share in later posts, but I think having a clear diagnosis of the problem is a useful first step.

every chinese frontier model now uses linear attention (except deepseek) they all use (except kimi) sparse attention with similar indexer/compression designs to maximize efficiency they all use "fancy" residuals (mHC, attention residual, gated residual) to maximize signal propagation they all use Muon very exciting time for frontier (and efficient) oss models, the beauty of open research :)