Participated · Seed · Jun 2026

Radical Numerics develops large-scale biological AI to read and write whole genomes and to detect AI-generated pathogens. The founding team previously trained Evo and Evo 2, described as 40B-parameter models trained on trillions of DNA tokens and released as fully open source. The company previewed Omnii, which it says surpasses Evo 2 and is being further trained with additional modalities. Radical Numerics positions itself with a dual mandate: advancing biological design for human health and building technologies for biosecurity. It has announced partnerships to apply Omnii for early cancer detection with a diagnostics company and to pilot pathogen detection with a U.S. national lab. The company raised a $50 million Seed round to scale its models, grow the team, and accelerate its mission.

Led · Equity · Apr 2026

Founded out of the Harvard University Quantum Computing Lab in 2017, Zapata Quantum develops hardware-agnostic quantum software tools aimed at translating theoretical quantum advantage into practical utility. The company has worked across industries including pharmaceuticals, finance, materials science, defense, and cryptography, and has supported Fortune 500 companies and public sector organizations. A recent collaboration with Dana-Farber Cancer Institute, University of Toronto, and Insilico Medicine produced a quantum-enabled drug discovery study targeting KRAS mutations that was recognized among the top scientific papers of 2025 by Nature Biotechnology. Zapata plans to expand its platform and grow scientific, engineering, product, and commercial teams while deepening its AI-driven development approach. It is also pursuing formal algorithm validation partnerships such as with the University of Maryland. Financially, Zapata recently completed an oversubscribed $15 million financing as the final stage of a year-long restructuring, positioning the company to accelerate growth and move customers from experimentation to production-ready quantum solutions.

Participated · Series A · Mar 2026

Gimlet Labs has developed what it calls the first multi-silicon inference cloud, software and an API that slices and orchestrates agentic AI workloads to run simultaneously across CPUs, GPUs and high-memory systems. The company claims its approach can speed AI inference by 3x to 10x at comparable cost and power, and can split model execution across different chip architectures. Gimlet has partnered with chip makers including NVIDIA, AMD, Intel, ARM, Cerebras and d-Matrix and targets large model makers and data centers rather than individual developers. The startup publicly launched in October with reported eight-figure revenues and says its customer base more than doubled in four months, now including a major model maker and a very large cloud computing company. Gimlet employs around 30 people and was founded by Zain Asgar, Michelle Nguyen, Omid Azizi and Natalie Serrino, who previously worked together at Pixie (acquired by New Relic).

Participated · Series A · Mar 2026

Axiom develops a "Verified AI" approach that combines formal mathematics and artificial intelligence to generate and then mathematically verify outputs such as code and scientific claims. The company aims to automate the two-step discovery process of hypothesis generation followed by formal proof to reduce incorrect or unreliable outputs from generative AI. Axiom plans to extend verification beyond pure mathematics into software development and other technical fields that require validated outputs. Its stated goal is to tighten the generation-and-verification loop so AI systems produce more reliable results and accelerate technical breakthroughs. The company will use the newly raised capital to expand its team and speed development of its verification technology. Financially, Axiom completed a $200 million Series A at a valuation above $1.6 billion, with both new and existing investors participating.

Led · Series A · Feb 2026

Efficient Computer is building the Electron E1, touted as the world’s most energy-efficient general-purpose processor, based on the company’s proprietary spatial data-flow "Efficient Fabric" architecture. The design minimizes data movement and architectural overhead, delivering accelerator-like performance per watt while preserving full programmability for AI, signal-processing and control applications. By targeting edge, embedded, and data-center environments, the platform aims to solve the energy constraints that hamper the deployment of intelligence in the physical world. The company complements its hardware with developer-friendly software to ease adoption across infrastructure, automation, space, defense, and wearable markets. Fresh capital will fund expansion of its engineering and developer teams and accelerate the product roadmap toward broader commercialization. To date, Efficient Computer has raised $76 million in venture financing, underscoring investor confidence in its approach to energy-aware computing.