Insider Brief
- Autoscience raised $14 million in seed funding led by General Catalyst to develop an AI-powered virtual laboratory that automates machine learning research and development.
- The platform uses autonomous AI scientists and engineers to generate, test, and deploy new models, addressing the growing bottleneck in human-led experimentation.
- The funding will support scaling to enterprise clients and expanding its team, as the company aims to accelerate AI innovation by compressing years of research into months.
PRESS RELEASE — Autoscience announced it has raised $14M in seed funding to automate the research and development of new machine learning models. The round was led by General Catalyst, with participation from Toyota Ventures, Perplexity Fund, MaC Ventures and S32. The company has created a virtual AI laboratory with non-human AI Scientists and Engineers that can invent, validate, and deploy specialized, state-of-the-art machine learning models.
For many machine learning teams, the primary bottleneck in artificial intelligence development is no longer compute or data, but the human capacity to create and test new ideas at scale. With more than 2,000 machine learning papers published every week, no human research team can effectively evaluate and implement every new breakthrough while advancing their own original hypotheses. Autoscience addresses this problem using two core AI systems: automated scientists that ideate and test new algorithmic hypotheses and automated engineers that optimize and deploy those validated inventions into the real world. Autoscience’s first deployments target high-stakes financial applications, manufacturing and fraud detection, enabling companies to benefit from the output of a fully-staffed research division without the headcount.
Autoscience first gained recognition when its autonomous lab became the first AI system to produce a peer-reviewed scientific research paper (ICLR 2025 workshop). Soon after, its system secured a Silver Medal in a machine learning competition (Kaggle Santa 2025) against 3,300 teams, marking the first time a fully-autonomous system has placed in a live, featured Kaggle competition.
“We’ve reached a point where human intuition is no longer enough to navigate the complexity of algorithmic discovery,” said Eliot Cowan, CEO of Autoscience. “We’ve built a research organization where the researchers are AI systems. We aim to compress a decade of machine learning research into months, unlocking new AI capabilities for scientists and forming a competitive edge for our customers.”
“We believe Autoscience is tackling an increasingly important challenge in machine learning: the pace and scalability of experimentation,” said Yuri Sagalov, Managing Director at General Catalyst. “As research output continues to grow, teams are looking for ways to more efficiently test, validate, and translate new ideas into production systems. We’re excited about their progress in advancing autonomous R&D to scale that workflow.”
The $14 million in funding will be used to scale Autoscience’s offering to a select group of Fortune 500 and large private companies who are training specialized models in high-stakes environments. This managed service deploys hundreds of automated AI Research Scientists that continuously generate and ship improvements to their machine learning models at the same time, enabling companies to discover, test, and serve better models. The capital will also support the expansion of Autoscience’s engineering team as they accelerate AI research.
About Autoscience Autoscience is an applied research lab based in San Mateo, California. The company builds AI systems that conduct machine learning research and deploy the results, effectively automating the role of the AI researcher and engineer. By combining academic rigor with AI speed, Autoscience helps organizations invent and implement proprietary machine learning breakthroughs. The team is singularly focused on building the systems that enable autonomous AI research to be the new standard for how the world’s best models get built.
