We’re excited to double down for @arena’s $200M Series B. As AI models multiply, knowing how they actually perform in the real world is becoming increasingly important. Arena is also launching its Alignment Index, which measures how closely AI behavior aligns with human values in real-world settings, contributing to Arena's mission to measure AI through the experience of people who use it. We first partnered with the team at the seed. Since their Series A, Arena has exceeded $100M in annualized revenue, with millions of people helping evaluate frontier models through real-world use. That growth reinforces our original investment thesis that the more models there are, the more valuable independent signals about their performance become. Congratulations to co-founders @ml_angelopoulos, @infwinston, and @istoica05. As the model race gets more crowded, Arena’s work has been getting even more interesting. CC: @buckymoore, @jamesalcorn94, @ravi_lsvp, @prathikhosalli

We are introducing Beam today, a 500b open model that excels in coding, agentic, and scientific workloads. Beam is very token-efficient, pre-trained from scratch, and scaled with the largest RL run documented openly we are aware of. Getting here was challenging, rewarding, and personal. I immigrated to the USA during childhood when my parents got jobs as scientists at PNNL, a national lab located in a rural town in Washington state. Growing up, I developed an interest in physics because it was grounded in simple principles, was foundational, and had long-term impact. Physics was the foundational science of the 20th century. The first general computer (ENIAC), the transistor, GPS, and much of modern technology had its roots in physics. In 2016, during my graduate studies, I noticed the birth of a new foundational science - Artificial Intelligence. AlphaGo, the superintelligent Go agent developed by DeepMind, had come out and made me feel what was about to come. A neural network, trained to imitate human players, and then improve itself through trial and error, mastered the most intellectually challenging board game in the world. What surprised me about AlphaGo is how it was conceptually simple, general, and scalable. The system could in principle improve itself forever, it was just a function of compute. This is when I became “AGI pilled” as they say. I switched from physics to AI because I thought this would become the root node science of the 21st century, much like physics was the root node science of the 20th century. I believe that working on foundational science is not enough on its own. Foundational science must also remain open and widely accessible. We remember Newton for his contributions to physics, but he was also an alchemist. Alchemy was done in secret, because the artificial production of gold was deemed lucrative but also dangerous with the potential do destroy entire economies. Newton even wrote his alchemy notebooks in cypher. On the other hand, Principia was published in the Philosophical Transactions of the Royal Society, the knowledge was made widely available, passed down for generations in what we now call Newton’s laws. The computer, transistor, GPS were possible to build because physics as a science was open. Openness creates an ecosystem of innovation. The notion of openness, which is a core tenet of scientific discovery, has also become a guiding principle for how foundational technology gets built and distributed. The internet was built on open protocols. The most widely used operating systems are open. The field of cybersecurity exists because strong encryption protocols were made open in the 1990s. Open also means safer. The best defence is a distributed one. Commonly used open source software is robust and battle-tested because it is inspected and patched by a community of developers. AI is different but also similar to software in many ways, though with a much larger surface area of vulnerabilities. It is impossible for a small group of safety researchers working in secrecy to remediate all of the potential issues regardless of their intentions. It is like entrusting a handful of white blood cells to protect your body. AI is both foundational as a science and a technology, and the safest way that also ensures the benefits of AI are distributed evenly is by making it open. I’m excited for what developers and scientists build and learn from this model, and for Beam to play a small part in ensuring that AI is made widely accessible, safe, and beneficial to all.

We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next. Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon. This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials. Read our blog posts below.

Introducing VOX: a microphone you wear on your skin, made for private dictation. Works with low-volume speech (even whispers) and in loud environments. Remappable buttons for your shortcuts.