Why We Invested in ScienceMachine
The Two-Person Team Building Biotech’s First True AI Agent
We meet a lot of startups claiming to build “AI for science.” Many are automating experimental workflows. Some are applying language models to research papers. Some are generating new insights in vertical application areas, such as drug discovery. But very few are rethinking scientific discovery itself.
When we met Lorenzo and Ben, the founders of ScienceMachine, it was clear they were doing exactly that. With just two people, no marketing, and a ruthless focus on product, they’ve quietly built one of the most powerful applications of autonomous AI we’ve seen in biotech: a fully autonomous agent that helps biotech teams get to results in minutes, not months - and teams are already lining up to use it.
A New Category: The AI Bioinformatician
ScienceMachine’s product, Sam, isn’t a tool. It’s a fully autonomous AI agent: a system that cleans, analyzes, and interprets experimental data, surfacing real scientific insights in a fraction of the time and cost.
In effect, Sam acts as a tireless bioinformatician - one that integrates seamlessly with existing databases and lab workflows, then continuously processes experimental data to find patterns, insights, and potential breakthroughs. It’s a leap forward for an industry that’s currently struggling to keep up with the pace and volume of biological data generation.
Why Now
This explosion of biological data has outpaced the ability of most teams to analyze it. Biotech companies are drowning in experimental output, and domain experts often lack the time or training to apply sophisticated data science.
Meanwhile, the rise of foundation models and agent frameworks has made it possible to build intelligent systems that don’t just assist, but autonomously reason, iterate, and discover.
ScienceMachine sits perfectly at this intersection: bringing the power of AI agents to one of the most high-impact, data-rich, and under-served domains in the world.
Why This Team
Lorenzo and Benjamin are a rare combination. Lorenzo is a YC alum and deeply technical product builder who understands the zero-to-one phase inside and out. Benjamin is a computational biologist who previously built internal AI tools at BenevolentAI and knows first-hand how pharma and biotech companies try (and often fail) to scale this kind of capability.
What impressed us most was their velocity. In six months, with no budget and no hype, they built what larger teams with 100x the resources are still trying to prototype. They launched, signed customers (entirely via inbound!), and got real results. That kind of execution is rare, and it’s exactly what this space needs.
What’s Next
We believe ScienceMachine has the potential to become a foundational company in biotech. Not just a useful product, but an infrastructure layer that helps the next generation of researchers make discoveries faster, cheaper, and more precisely than ever before.
We’re proud to be backing them alongside Nucleus Capital, Juniper VC, and a handful of incredible angels from the pharma industry.
If you’re a scientist, biotech founder, or investor curious about what the next wave of AI in science looks like, we suggest you keep an eye on ScienceMachine!