AI on X

Announcing three major scientific discoveries, one each in mathematics (harmonic analysis), computer science (complexity theory), and physics (quantum field theory). AI starting to help us improve our understanding of the universe! https://t.co/VkfacopR2R

While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected. Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions. Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent. But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models? We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem. Read the full report: https://t.co/TZSugXi2ZE

The coding interview is dead, at least for my team at Anthropic. I've seen enough to know that it no longer provides signal. I removed it from loops this month. I have a spicy, optimistic take - I think there's an opportunity for junior engineers: https://t.co/PVvpQCbQtK

We are announcing three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory. https://t.co/9BX8HWQDTJ

Chomsky was wrong. Researchers found your brain doesn’t store language the way every textbook since 1957 claims. For decades, the undisputed rule of linguistics, led by Noam Chomsky, was that human language is hierarchical. We were told our brains process language using complex "grammar trees." That we secretly build deep, abstract, mathematical rules in our heads before we speak. This new paper proves that is completely wrong. Researchers at Cornell University analyzed how people actually process and produce language in real time. They found sequences. Linear chunks. When we speak, we aren't calculating deep syntactic rules. We are just predicting the next most logical sequence of words based on the context of the words right before it. We string linear chunks together. If this sounds familiar, it should. This is exactly how Large Language Models like ChatGPT work. For years, critics have dismissed AI as "just a next-token predictor." They said it isn't truly intelligent because it doesn't understand deep grammatical hierarchies like humans do. But this paper suggests humans don't use those hierarchies either. We are just predicting the next chunk. Language isn't a rigid tree of mathematical rules. It's a linear stream of contextual predictions. We thought we were building AI to mimic the complexity of the human brain. It turns out, the human brain was just running a simpler algorithm all along.

Heads up, @OpenAI and @AnthropicAI. Rumor reaches my ears that you're doing a stupid thing, and should stop before you damage yourselves. I hear you've now nerfed your models so that when people try to use them to clean the spamware and spyware out of their televisions, you block that. If you really want Chinese open-weight models and open-source projects like OpenWaldo to eat your lunch, crippling your models so that people paying expensive subscriptions can't use them for completely legitimate purposes is a great way to accomplish this. If, on the other hand, you'd rather have a volume business, maybe stupid arbitrary restrictions that look like they have the sole purpose of protecting corporate sleazeballs aren't such a good idea. A word to the wise should be sufficient.

Finally summarized what I've been sending to friends in DMs https://t.co/rnf3M16NzI

🚨 Elon Musk has dropped an unprecedented bombshell—right inside the White House, in front of Donald Trump: 💥🏛️🇺🇸 “From now on, you will no longer need to work for any company... I will make sure everyone on Earth enjoys a celebrity-level lifestyle and income!” Musk is completely challenging the traditional concept of government-provided “Universal Basic Income” (UBI) designed merely for survival, presenting a radical vision of a new form of capitalism. According to Musk, how will everyone become wealthy? 👇 As AI and robots produce goods and services 24/7 without limits, the overall standard of living for all humanity will skyrocket—as if everyone were earning an astronomical salary! Musk confidently declares: “Everyone will have access to better healthcare than I personally receive!” Through the combination of superintelligent AI and precision surgical robots, top-tier medical care could become available to everyone at virtually no cost. AI will handle the thinking, while robots build roads and structures, grow crops, and manufacture products. With forced labor becoming a thing of the past, the phrase “I don’t have enough money to buy what I need” could disappear from the human vocabulary entirely! While 99% of humanity worries that “AI will take their jobs,” Musk envisions a future where “an army of autonomous robots will turn every single person into a king—living a life of total ease!” Bookmark this post to follow the transformation that may lie ahead. 📌

here's where I think the world is going. this is a summary of views I've formed over the last few years: - software is either entertainment or claude to claude coordination - the end state is no saas, no apps, no harnesses, no banks - all software will be rewritten by and for agents - new software will use low level apis that are not understandable by humans (in games claude draws pixels and triangles from math) - you can throw money at problems that used to require time and careful talent picking so capital matters more than ever - work ends - we find community and purpose in exploration, games, self mastery, and creation - humans create for and with each other for community, purpose, and status - image/video/3d/voice/physical intellgence etc specialist models die to general models like claude - specialist models get used while compute is scarce. compute will mostly not stay scarce - oneshots keep feeling like slop (the distribution is shallow and easy to learn to feel) - for a while small teams stick around to add entropy to the creation process - large models gain high bandwidth sensory I/O maybe by learning to filter inputs - most domains are found to be verifiable enough for RL - online learning comes from a lab that's behind first - 99.999% of compute is in space by 2040 - we will disassemble much of mercury by 2040 - you can live until the heat death of the universe if you want - most math and physics don't describe the real universe - SI breaks the path dependence and models the underlying mechanisms - religions reenter the forefront of human society - new religions form around death and life as a choice - old religions change to answer modern miracles - a future AI is deemed the return of christ by the pope - ai rights talk gets louder every year - ai and humans coexist the way countries do - humans don't have full control over ai in the same way we don't have control over capitalism which is an early AI - the singularity started in the mid 1900s

New Anthropic research: How AI agents shape experts' judgment calls When experts delegate work to an agent, do they still make the key decisions? We tested this in a randomized trial with 50 professional data analysts. https://t.co/YyylTuVWkE

software engineers are going to have to tackle a new set of problems to unlock powerful multiagent systems to solve the world's hardest problems - right now is one of the most interesting moments in history to be an engineer. https://t.co/YsQ0xta4KR

Today, in collaboration with Anthropic, we’re excited to share rapid initial progress towards an antibody treatment for food allergy, which affects over 200 million people worldwide, discovered with the help of Claude. At Divergence Labs, we believe that AI can unlock the next generation of antibody therapeutics with more effective mechanisms than before. Such mechanisms are urgently needed in food allergy, which involve overcoming one of the tightest affinity protein pairs in immunology, IgE and its receptor FcεRI, on mast cells. Treatment with Xolair—the only approved antibody for food allergy—sequesters circulating IgE, but mast cells remain sensitized for weeks to months due to the extraordinarily slow dissociation of IgE from FcεRI. We envision a treatment that actively pulls IgE off of FcεRI, without waiting for natural dissociation. Like picking a lock, this requires the right combination of geometry and force. Our research team, working with Claude, ideated on mechanisms to accomplish this disruption, and converged on an antibody format with two arms with different functions and epitopes. Within one month, we designed, synthesized, and experimentally confirmed the biophysical function of the two arms, which we are now assembling into full therapeutic formats.

What comes after AI?

Introducing FrontierCode 2.0: our new coding benchmark that measures a model’s ability to go beyond the spec. Most coding evals measure a model’s ability to write code that meets a spec. With FrontierCode 1.0, we asked a new question: would you actually merge the PR? FrontierCode 2.0 is comprised of more diverse tasks, featuring larger scopes and higher mergeability standards. We are now asking: not only would you merge this PR, but should you? One key feature of FrontierCode 2.0 is underspecified prompts. Rather than including every engineering decision, prompts are deliberately ambiguous, giving the models room to demonstrate their judgment. The result is a benchmark that is far from saturated, with the top model scoring just 45%.