The reason large AI companies with virtually unlimited capital, compute, and computer science talent have not transformed biology is very simple. They do not understand biology. In biology, the hardest problem is not accessing information. We already have enormous amounts of publicly available data, papers, databases, omics datasets, and experimental results. Much of it remains underutilized. Most of it can be converted into computable formats straightforwardly. The difficult part is knowing which questions matter. What do we actually understand? What don’t we understand? Which hypotheses are worth testing? Which experiments would meaningfully reduce uncertainty? Where are the conceptual bottlenecks preventing progress? These are scientific judgment problems, not database problems. What biology lacks is not data. It lacks enough biologists with strong quantitative and computational skills who can identify important questions and use available tools to answer them. Biology is different from coding. The challenge is not retrieving information. The challenge is deciding what information is worth generating in the first place. Once AI companies recruit enough biologists who know how to identify important questions and how to use quantitative tools to address them, the opportunities will be enormous. The bottleneck is not the databases. The bottleneck is biological insight.

Together with my co-founders Michael @MichaelPoli6, Stefano @Massastrello and Armin @athmsx, I am excited to announce @RadicalNumerics is emerging from stealth with a $50M seed round to build general biological intelligence. We’re also sharing an early preview of our new model Omnii, the most powerful genome language model to date. Omnii preview link: radicalnumerics.ai/blog/radi… At Radical Numerics, our mission is to master the code of life, and to drive the frontier of biological AI for both design and defense. This is our dual mandate, which comes from something our own team helped make possible. Our founding team trained Evo and Evo 2, the largest biological AI models (40B params) trained on DNA sequences. Trillions of tokens across all of life, from microbes to mammals. It’s fully open source, and created the field now known as generative genomics. Last year, scientists used Evo to generate the world’s first complete genome from scratch using AI. Turns out it was a bacteriophage—a type of virus. It functioned in the real world, and in this case it was harmless. But for us, it was a clear turning point. It showed that AI is no longer just analyzing biology. It is on the cusp of generating functional lifeforms. Eventually, AI will have the power to design and control life itself. That should make all of us incredibly excited, and incredibly uneasy. (Anyone can design DNA with a new function, and have it synthesized and delivered, like something from Amazon Prime). The same technology that will help us cure cancer is the very technology that might create the next global pandemic, or worse, allow the creation of bioweapons that can wipe out populations. We believe these forces are inseparable. If you work on the frontier of biology, you have to build technology to safeguard it from its misuse. Existing biosecurity tools are sorely losing the arms race, relying on outdated “have I seen this exact thing before?” style algorithms. We founded Radical Numerics to turn the tide. And we can’t do that by training on textbooks and natural language. We must understand the language of biology from the raw physical data itself, to reason across every molecule and modality, from DNA to proteins. The next frontier for AI goes far beyond chatbots or video generators to models that can understand and engineer life. Today, we’re previewing Omnii, which is already far surpassing Evo 2, and will continue improving as we scale and add new modalities (training now). 1. For human health, Omnii can read and write whole genomes (more on writing later). It’s state of the art (SOTA) on detecting causal variants for disease, and can rank Alzheimer's mutations zero-shot. We’re partnering with a diagnostics company to use Omnii for early cancer detection (pancreatic and multi-cancer). 2. For defense, Omnii is SOTA at detecting AI-generated pathogens. We benchmarked existing detection tools, and they simply can’t detect the AI-generated ones (“deepfake viruses”). We’re partnering with a US national lab to pilot Omnii for detecting the next pandemic, both natural and AI-generated. We have a data center full of Blackwells in construction now to build the most powerful biological AI models ever. This mission takes a new kind of AI lab that can actually scale on physical, biological data: new alignment research (mid/post training), scaling long context, building out mech interp teams to dissect what these models learn, new architectures and systems designs, all from the ground up. Our team is made up of AI researchers and scientists from top labs and institutions (e.g. Stanford, MIT, Google DeepMind), but more importantly, we all share the belief that this is the most important challenge of our lifetime. If you feel similarly, we are hiring. We aim to bring the brightest minds in AI and science together to save lives. Thanks to our partners on this journey, led by Emergence Capital @emergencecap, with Obvious Ventures @obviousvc, Triatomic @TriatomicCap , and Patrick Collison @patrickc. Our advisors include Eric Horvitz @erichorvitz, CSO of Microsoft, Chris Re @HazyResearch of Stanford, George Church @geochurch of Harvard, and Andrew Weber @AndyWeberNCB, former Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense Programs. Fortune article: fortune.com/2026/06/15/exclu… Jobs: radicalnumerics.ai/join-us

The next generation of young people who change the world will almost certainly be the people who are most adept at making long-running multi-stage multi-team agent tasks work extremely well, and at high volume and across every part of their personal and work lives.

The Magnetobiology Episode: A company in San Francisco, called @NonfictionBio, is building proteins (like antibodies and enzymes) that can be controlled using small magnets. In this episode, co-founder Maria Ingaramo and scientific advisor Andrew York explain how they engineered a protein, MagLOV, that responds strongly to magnetic fields, why most prior attempts have failed to replicate, and how the mechanism of magnetically-controlled proteins actually works. They also get into the “dream” use cases, like cancer drugs that activate only at the tumor, which might have a lower toxicity inside the body. This podcast is made possible by @AsteraInstitute. I'm happy with how this episode came out. I think my interviewing skills are improving, and I'm getting better at building up context throughout the episode. Enjoy! Search for "The New Biology" on YouTube, Spotify, and Apple Podcasts. Timestamps: 00:00 - Opening 00:54 — Introduction 01:35 — The dream 05:38 — Why magnets vs. light or ultrasound 10:05 — The physics 17:48 — On the name "magnetogenetics" 21:25 — Birds and cryptochromes 27:09 — Why is the field filled with so much junk? 29:51 — Adam Cohen's molecule 33:24 — Markus Meister’s debunking 38:06 — The experiment 46:22 — Finding the LOV domain 54:11 — Singlets, triplets, and cysteine 56:54 — What the magnet is actually doing 1:05:13 — The conformational-change red herring 1:12:46 — The Quantum Biology Institute 1:19:31 — Founding Nonfiction Labs 1:24:38 — How to convince skeptical investors 1:29:39 — What a magnetogenetic medicine might look like 1:38:50 — First clinical indications 1:45:12 — The regulatory path 1:48:01 — What the field needs 1:54:30 — Appendix: Whiteboard lecture

Today marks a historic milestone for the type 1 diabetes (T1D) community: the @US_FDA has approved Tzield for use in individuals ages 8–17 with stage 3 T1D. This is the first disease-modifying therapy ever approved for stage 3 T1D! Tzield preserves beta cell function in newly diagnosed individuals, which has been shown to result in fewer hypoglycemic events, lower insulin use, reduced burden of care, and improved A1c. We'll keep working to ensure people with stage 3 T1D everywhere, not just in the U.S., have access to therapies that help them live healthier lives. Breakthrough T1D and the T1D Fund have supported the development of this therapy for over 30 years, and we're grateful to the FDA for recognizing the urgent unmet need in T1D and expediting Tzield's approval through the accelerated approval pathway. We also thank @sanofi for their continued research and the T1D community for advocating for therapies that change the course of this disease. Read more here: breakthrought1d.org/news-and…

oh you don't believe in the acceleration of biology? ask yourself why: - Moderna and Merck's personalized mRNA cancer vaccine just cut melanoma recurrence by 49% over 5 years. presented at ASCO 2026 this week. the same mRNA tech from COVID is now fighting cancer - CAR-T therapy used to take weeks in a specialized lab. in vivo CAR-T is now a single injection. off the shelf. moving into autoimmune disease and cardiac fibrosis - Ginkgo Bioworks' Nebula is the world's largest autonomous lab. running 36,000 reactions and generating 150,000 data points per loop. 24 hours a day. no human required. Amazon plugged them in as the wet lab backbone for their Bio Discovery platform - Isomorphic Labs raised $2.1B to design drugs with AI. the lab is becoming software - the first reverse-aging drug was injected into a human this week. Life Biosciences. David Sinclair is bullish this is june 2026 and as if thats not enough, we have the Superhuman Fund II actively backing the infrastructure that makes all of this possible at scale if you are building in this space, let's talk bio/acc

Fable on API is about $600/hour lol, our jobs might be safe for a while.

In a series of 3 papers and preprints, we’re thrilled to share with you the working laser phase plate. In collaboration with research led by Holger Müller at UC Berkeley, this is a huge innovation in imaging to make small and faint objects inside cells visible. bit.ly/4vIPC1U

Researchers say they have used a precise genome-editing technique called base editing to alter the genome of human embryos for the first time go.nature.com/3RLdK5v

New Science Blog: Why has AI advanced faster in coding than in biology? To agents, bio databases are like cities built before cars—maddening to drive in because they're designed for different traffic. How do we build infrastructure agents can use? anthropic.com/research/agent…

"People do not realize that there is only one bridge remaining." To solve disease accelerate, we need to make disease solvable using compute. Data is the limiting reagent. This is why @precigenetic is essential for the future of biological superintelligence.