“If you want to understand what life is, you need to first build life.” quantamagazine.org/for-the-f…

The video of my Stanford CS25 guest lecture, From Language Models to Native Multimodal Intelligence, is now online. I discussed how the core ideas behind LLMs has shaped multimodal AI, from architectures to training paradigms and scaling, and where the next challenges may lie. 🧠🌐 🎥: youtube.com/watch?v=NDdc39KY…

Cool.

Went to the @boltz_bio model launch party tonight. Two new state-of-the-art models - BoltzMol-1 for small-molecule hit discovery and BoltzProt-1 for protein design - plus the Boltz API, a fast, low-cost way to run every Boltz model in production. It was good.

Today, we are excited to announce a major partnership with @GSK to deploy the latest Boltz models across GSK’s research organization!

With Claude Science, Anthropic Targets Another Application. aibusiness.com/generative-ai…

Introducing Claude Science, a new app designed with every stage of research in mind. Artifacts traced to their code, environments managed on demand, and 60 optional scientific databases that you can connect. Available now in beta.

We’re excited to share at #BIO2026 that Achira is collaborating with @NVIDIA to build molecular world models for agentic discovery in the life sciences. Our physics-grounded AI x simulation engine generates evidence for molecular design in new spaces. #BIO2026 #WorldModels

Today, we expand zero-shot drug design beyond binding to the design of multifunctional medicines, the intracellular proteome, and state-of-the-art atomic precision with our model, JAM-2. In a new report (below), we show: 1. The first drug-grade, fully computationally designed multispecific antibodies against five peptide-MHCs: Routine picomolar T-cell activation/cell-killing EC50s, >100-fold selectivity, and drug-like developability 2. The first fully generatively designed, drug-grade dual-variant KRAS G12 multispecifics: They recruit primary T-cells from human donors to kill G12V and G12C presenting cells at pM to single-digit-nM potency, completely sparing wild-type. 3. Atomic accuracy, from sequence alone: Angstrom-level agreement between Cryo-EM and JAM-2 de novo designs, requiring only target sequences (not structure) as input. 4. Unrivaled speed with an AI-native in-house wet lab: Designed, built, and tested five programs in one parallelized campaign, end-to-end in-house in ~6 weeks. 5. A higher validation bar for AI-generated drug candidates: In a field increasingly rife with hype and uneven standards of proof, we provide the highest quality public wet-lab validation of AI-designed antibodies to date. We share experimental methods in full, and invite folks to adopt and build on these standards. Truly individualized therapies will be the most important contribution of AI in drug design. These advances help accelerate this future.

Healthcare & bio is a killer app for AI.

Today, we’re excited to announce our $50M Series B, led by @GreenfieldVC (formerly TPG Capital), with participation from @lightspeed and @notablecap. 🚀 At @PatronusAI, we develop simulations and evals to train and improve AI. The first phase of AI was built on static benchmarks, but that era is over now. As agents are used to solve longer and longer tasks, they need to practice in dynamic, living worlds to get better. Simulations are the critical infrastructure powering this next phase. As a company, we’re behind the most influential research and products in AI evaluation, like FinanceBench, Lynx, and Percival. And things have moved at the speed of light since. ⚡ We partner with the world's leading frontier AI labs and enterprises, and our revenue has grown more than 15x over the past year. Additionally, today, we’re introducing a preview of the first Digital World Model for AI agent training and simulation: Patronus-DWM. Digital World Models are language diffusion world models that predict realistic environment behaviors and steer agent actions across digital workflows. Just as physical world models predict how objects move through space, we’re developing the equivalent for the digital world: predicting how agents act in digital workflows, then using that to scale the creation of high-quality training data for LLMs. Digital World Models help us push the frontier of ultra long horizon workflows, and unlock a new class of self-improving RL environments. This is our scalable approach to simulating all of the world’s intelligence. The round was also joined by @datadoghq, @SamsungVentures, @gokulr, @factorialcap, and a large cohort of amazing AI leaders and researchers across @AnthropicAI, @OpenAI, @GoogleDeepMind, @nvidia, @Recursive_SI, and more. ✨ It has been the ride of a lifetime. But we’re just getting started. The best is yet to come. "Do not go gentle into that good night, Rage, rage against the dying of the light" - Dylan Thomas (1954)

D-proteins have an obvious therapeutic rationale — their mirror-image structure confers protease resistance and immune stealth inherently. This has been hypothesized for decades.   The bottleneck was computational. Every major protein design tool  has been trained on natural L-protein data. With these models, D-protein design simply isn't tractable. @Abiologics_Inc built a generative model to solve that problem — enabling the design of D-protein therapeutics that penetrate deep into tumor tissue and persist for days.   Read our latest Substack post: bit.ly/4gAxymg

Today, I’m excited to formally announce @mirendil with my amazing co-founders Harsh Mehta, Shayan Salehian, and Tara Rezaei! We’re fortunate to work with @a16z and @kleinerperkins, who led our seed round of $200M, followed by a major investment from NVIDIA, among others. Mirendil exists to accelerate science and technology, and through them, to help solve humanity's most pressing problems. Self-accelerating AI R&D is the most direct path to delivering on AI's broader promise, which is why we believe the most important application of AI is AI itself. Get this loop right, and it compounds. It fundamentally changes the rate of progress itself across all domains. We believe this capability should be democratized. It should be used to power all scientific efforts trying to innovate at the frontier. There are far more important problems—and broader ones—than any single lab can take on, so more groups should be able to pursue them. This pulls concentration of power away from a few labs: businesses and science labs can own their AI and infrastructure, keep their margins, and control their own destiny instead of ceding it all to a single AI lab. We’re a small team with a singular focus. Our founding team consists of 20 researchers and engineers from frontier institutions including Anthropic, xAI, Google DeepMind, and OpenAI, united by a passion for science and a drive to build the technologies that move it faster. If you want to build the system that builds systems, join us! @HarshMeh1a, @shayan_, @tararezaeikh

Delighted to be joining @TheCrick as Chief AI Scientist, alongside my work at Cambridge. I look forward to helping build an ecosystem where AI advances biology, and where biology’s hardest questions inspire the next generation of AI. Link: crick.ac.uk/news/2026-06-24_…

CSHL: AI in Biology centuryofbio.com/p/cshl Since 1933, @CSHL has hosted an annual Symposium on Quantitative Biology. At first, "quantitative biology" meant the use of chemical, physical, and mathematical techniques. This conference became the Schelling point for the pioneers of molecular biology. Watson first presented the structure of DNA at the 1953 Symposium. This year, the topic for this famous conference was AI in Biology. For five days, the top researchers in this field gathered from around the world to present their latest work. I went, and have done my best to summarize some of the major themes and results from the Symposium. It was a lot of fun attempting to synthesize ideas from superstars including @pushmeet, @Avsecz, @zhou_jian, @Micro_Yunha, @pkoo562 @anshulkundaje, @Prof_Lundberg, @recursus, @ZhongingAlong, @marinkazitnik, @lecong, and more! I hope you enjoy reading—it was one of my favorites conferences I've ever been to. Some truly beautiful research on display.

It has been an absolute privilege and pleasure to build up @UCL_DARK with @egrefen, @robertarail and @jparkerholder over the past eight years. Yesterday, the UK government announced not just one but two national academic fundamental AI research labs. I am extremely excited to announce that @UCL_DARK will be sunsetted and merge with @FLAIR_Ox, @whi_rl, @UCL_LASP and AIRL, to form the British Open-ended Learning and Discovery (BOLD) Lab — @BOLD_Lab_AI. This is a huge moment for academic AI research in the UK. Backed with £30m by @UKRI_News and @EPSRC, it provides a unique opportunity to attract leading international academic talent to the UK, and equip them with the computational resources to do groundbreaking exploratory AI research (more on the computational resources soon). It also creates a mentorship network of academics, industry leaders and entrepreneurs to educate young talent on how to translate fundamental AI research into real world impact. I want to thank all the students who made @UCL_DARK successful, in particular our PhD alumni @MinqiJiang, @_samvelyan, @zhengyaojiang, @_robertkirk, @akbirkhan, @LauraRuis, @YingchenX, @PaglieriDavide, and the work of our honorary faculty @egrefen, @robertarail and @jparkerholder who were generously contributing to mentorship and research in their free time.

Excited to be a day 0 launch partner for BioNeMo, NVIDIA's new, fully-open agent toolkit for scientific workflows! All 10 BioNeMo NIMs are available in our model library. Learn more in our announcement: baseten.co/blog/nvidia-bione…

In new work, we lay out a vision for a high-level programming language for generative biology, called Proto. Proto composes generative and predictive models spanning DNA, RNA, proteins, ligands, and their interactions, which we use to design complex biological functions. 1/n

1.1 billion predicted protein structures. That's the largest application of AI to protein biology to date. And it's fully open. But ESM Atlas is not a structure repository. It is an agent you can ask: what is this protein, what does it look like, what might it do? Explore ESM Atlas: bit.ly/4dJcF6G