Furong Huang

Sorry for spamming everyone on Twitter, with my sporadic phone signal, these updates probably seem as random as my travel situation. 😅 I’m still stuck trying to get to France to teach a class on Generative AI Agents at DeepLearn summer school. Yesterday, I spent 7+ hours sitting on the plane, only to be deplaned when the flight was canceled. Today, I’m back at the airport, once again waiting for departure while the departure time keeps getting pushed back. The trip also came with a fun little systems bug: my ticket had been upgraded from Basic Economy to Economy, but every system still thought I was in Basic Economy, so I couldn’t change flights, upgrade, or do much of anything. After a 50-minute call wait on the United 1K line (I’m oddly proud of my patience), it finally got sorted out. One small observation from all of this: tiered systems don’t just create different levels of service, they create very different levels of friction. When something goes wrong, the people at the bottom often have the fewest options and the hardest time getting help. Hopefully I’ll make it to France… eventually. 🇫🇷✈️ But I am excited about AI agents, self-improvement, long-horizon tasks, multi-agent coordination and competition! See you there!

Inspired by your trip, I recently also traveled quite a bit in the summer visiting AI labs in Silicon Valley and in China. I had a lot of unexpected observations. Maybe I should also find time to share my experience.

Policies may change, but universities are not powerless. With clear communication, careful planning, strong institutional support, and continued advocacy, we can reduce unnecessary disruption and help talented students finish the work they came here to do.

The United States has benefited enormously from international students. They conduct research, teach classes, build companies, strengthen laboratories, and contribute to communities across the country. A more administratively demanding system does not change their value or our responsibility to support them.

While FIFA has captured everyone’s attention, something consequential happened in academia this week, especially for international students in PhD programs. DHS issued a final rule changing how long F-1 students may remain in the United States. A thread on what it means and what students and universities can do. 🧵

Some observations on Kimi: 1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run. 2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China. 3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex. 4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business. 5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this. 6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.

Heard that some frontier models are basically a 48-layer transformer looped twice (48L x 2). Now we are introducing DeepLoop: Depth Scaling for Looped Transformers (arxiv.org/abs/2607.13491), making the loop transformer stable and scalable!

Wow, nice work @Kangwook_Lee!