310.ai

High-Level Overview

310.ai is a technology company developing generative AI for molecular programming in life sciences, specifically building an AI operating system and foundational models to enable programmable biology. Their core product, the Molecule Programming Model (MPM4), generates novel protein sequences from text descriptions, addressing the design of novel biomolecules—a breakthrough poised to transform biology research and applications.[1][2][4] Targeting scientists, researchers, and biotechs, 310.ai solves the challenge of engineering proteins beyond nature's 4 billion years of evolution by leveraging GPUs for rapid, programmable generation, making biology more accessible for real-world problems like therapeutics in immunology, cancer, and cardiometabolic areas.[1][2][3]

The company has assembled a team from top institutions, including former roles at Facebook, Amgen, and Stanford, with early momentum from pioneering text-to-protein generation and partnerships like AMD Instinct accelerators.[1][4]

Origin Story

310.ai emerged from the belief that generative AI and biology's convergence represents a historic inflection point, prompting founders and team members to leave leading research institutions, tech companies, and biotechs.[1] Key founders include Koosh, a serial entrepreneur and former Staff AI Engineer at Facebook, where he led Ads Ranking; he previously founded Concertboom (a top event ticket search engine) and Kookoo.ai (an AI for abstract paintings), with early academic acclaim via an Erdos number of 2 in cryptography.[1] The other highlighted founder holds a Stanford Ph.D. and postdoc in computational protein design under David Baker, with Amgen experience in structure prediction, protein engineering for therapeutics, and launching the AmgenFold platform; she is a multiple patent holder published in *Science*, *PNAS*, and *eLife*.[1]

The idea crystallized around compressing nature's evolutionary timeline using AI, with pivotal early traction in developing the MP model for text-to-protein generation.[1][2]

Core Differentiators

• Text-to-Protein Generation: MPM4 model converts natural language descriptions of protein functions into novel sequences, enabling programmable biology far beyond natural evolution.[2][4]
• Generative AI Engine for Biomolecules: Focuses on the most impactful AI task in biology—designing novel biomolecules—using GPUs to accelerate what nature lacked time for.[1][2][3]
• Expert Team from Elite Backgrounds: Combines AI engineering (e.g., Facebook TechLead) with deep biology expertise (Stanford Ph.D., Amgen protein design), supported by advisors like Larry Grace and investors including Dan Vahdat.[1]
• High-Performance Compute Integration: Leverages AMD Instinct accelerators for scalable training of foundational models, revolutionizing biomolecular design speed and accessibility.[4]

Role in the Broader Tech Landscape

310.ai rides the generative AI + biology trend, where models like theirs enable "programmable biology" by generating functional proteins on demand, a convergence amplified by advances in compute like GPUs and accelerators.[1][2][4] Timing is critical amid post-AlphaFold structure prediction breakthroughs, shifting focus to sequence generation for novel therapeutics—market forces like rising demand for AI-driven drug discovery (e.g., immunology, oncology) favor them, with biomolecule design flagged as AI's top decade-defining impact.[3] They influence the ecosystem by democratizing biology, empowering biotechs to iterate faster and solve unmet needs, potentially accelerating programmable matter in healthtech.[1][3]

Quick Take & Future Outlook

Next for 310.ai involves scaling MPM4 and successors for broader applications, like custom therapeutics or synthetic biology tools, fueled by GPU advancements and ecosystem partnerships.[4] Trends like multimodal AI-bio integration and compute democratization will shape their path, evolving their influence from niche protein engineering to foundational infrastructure for life sciences. As pioneers in text-to-protein, they stand to redefine biology's programmability, echoing their origins in betting big on this inflection point.[1][2]