Joe Cole - e/acc

hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final ((1+xy)^3 z + y^2 (1+xy) (4+3xy), y + 3 x (1+xy)^2 z + 3 x y^2 (4+3xy), 2 x - 3 x^2 y - x^3 z): \C^3\to \C^3, has jacobian determinant -2, and sends (0, 0, -1/4), (1, -3/2, 13/2), and (-1, 3/2, 13/2) to (-1/4, 0, 0)

Claude Fable 5 has produced a hand-checkable counterexample to the Jacobian conjecture, an open problem dating to 1939. The conjecture says that a polynomial map with a constant non-zero Jacobian determinant must have a polynomial inverse. An 87-year-old problem was casually solved by Fable 5 on a sunday evening. Insane times to live in.

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.

Interesting take. The challenges today: 1. Most enterprises don't know how to make AI game changingly effective. As they embark on the journey, the use cases being addressed are "80%" single shot, semi deterministic use cases with multiple guardrails or humans in the middle. We are a little ways away from mass adoption of custom models. 2. More complex cases which require any multi agent orchestration and context retention are beginning to be conceived and tested. These cases will make model portability harder, requiring new evals and harnesses. One will have to commit to one structure and also commit to constantly updating and retraining your model.. 3. CIOs and CEOs aren't sure if the ultimate architecture is single stack, multi model - interoperable orchestration and context/harness/eval, or a custom model. Uncertainty causes slowdown on longer term decisions, which in this case is perhaps right. 4. Custom and Opensource come with the need to deploy on either your own GPUs or public cloud. "Interesting fact - if token prices fall as I hope - it will be cheaper to run frontier LLMs than open source on your own GPUs" 5. Generally horizontal solutions that can serve tens of thousands of customers make more money than vertical custom solutions, but maybe this time it's different? Even if we solve the model conondrum, the enterprises need to redefine workflows, collect more training data on each use case and rebuild the application in a simple UI flow, not everything will be done in a conversational window. But time will tell, this will continue to be a space to watch, lots of minds at work to solve this.

Today we're opening up the DoorDash CLI in limited beta. `dd-cli` lets you order DoorDash directly from your agent: search stores, find the best deals, check out, and more. Early access for US/Canadian macOS developers by waitlist. Excited to see what folks build!

Today, shift is launching free private chef services in San Francisco. Whether you want a steak dinner or a lobster lunch, shift is making it free.  Comment "shift" below and we'll DM you a priority booking link. ⬇️