The pace of research in artificial intelligence is exploding. Thousands of new machine learning papers are published every week. But here’s the real problem. Human teams can’t test everything and build new things.
This gap is exactly what Autoscience aims to solve.
The San Mateo-based startup has raised $14 million in seed funding to build what it calls a fully automated AI research lab. The round was led by General Catalyst, with support from Toyota Ventures, Perplexity Fund, MaC Ventures, and S32.
The new funding will be used to expand AutoScience’s platform and offer it to a select group of large companies, including Fortune 500 companies.
The startup also plans to further its automated AI research and expand its engineering team.
Build an AI lab without humans
AutoScience takes a bold approach. Instead of hiring more researchers, we are building AI systems that function like researchers.
“We’ve reached a point where human intuition is no longer sufficient to navigate the complexity of algorithmic discovery,” said Elliott Cowan, CEO of AutoScience. “We’ve built a research organization where researchers are AI systems. We aim to compress a decade of machine learning research into months, unlocking new AI capabilities for scientists and shaping the competitive edge for our customers.”
The company has developed a virtual lab that utilizes “AI scientists” and “AI engineers.”
These systems can generate new ideas, test them, and turn successful ideas into real-world machine learning models.
Simply put, we are trying to automate the entire research cycle. This is important because the bottleneck for AI is no longer data or computing power. It’s a human ability. Teams can no longer test ideas quickly enough.
Autoscience’s system divides the work into two parts. One AI system will focus on generating and testing new algorithmic ideas. The other focuses on improving and deploying them. Together, they aim to reproduce what entire research teams do much faster.
The startup is already showing early signs of what this approach can do.
Its autonomous system became one of the first systems to produce a peer-reviewed research paper at the ICLR 2025 workshop. They also won a silver medal in a Kaggle competition where they competed against over 3,300 human teams.
It means change. AI no longer just supports researchers. We’re starting to compete with them.
Target high-stakes industries
The company is already focusing on real-world use cases where better models can directly impact business outcomes.
Its early deployments are aimed at financial services, manufacturing, and fraud detection. These are areas where even small improvements in the model can lead to large gains.
“We believe AutoScience is addressing an increasingly important challenge in machine learning: pace of experimentation and scalability,” said Yuri Sagalov, Managing Director of General Catalyst. “As research output continues to grow, the team is looking for ways to more efficiently test, validate, and deploy new ideas into production systems. We are excited about their progress in advancing autonomous research and development to scale workflows.”
