Lila Sciences continues its rapid ascent in the artificial intelligence landscape with a new $115 million extension to its Series A financing. The company revealed to Reuters that Nvidia’s venture arm and several other prominent backers joined the round. This infusion of capital pushes Lila’s valuation above $1.3 billion and strengthens the company’s position among the fastest-growing AI startups in scientific discovery.
Lila now holds $350 million in total Series A funding and $550 million in overall capital raised. Investors continue to flock to the company because they believe Lila can transform how scientists generate knowledge. Lila’s approach diverges from the dominant model of scaling general-purpose large language models. Instead, Lila builds specialized systems and pairs them with robotic laboratories that can run experiments continuously. The company frames its mission around “scientific superintelligence,” a vision in which AI drives every stage of discovery at a pace that traditional research teams cannot match.
A Vision for Scientific Superintelligence
Co-founder and CEO Geoffrey von Maltzahn founded Lila in 2023 under the umbrella of Flagship Pioneering, the venture studio that has launched several breakthrough biotech companies. From the beginning, von Maltzahn and his team focused on a specific gap in AI development. Many labs train models on internet-scale datasets, but these datasets grow stale and finite. Rather than depend on the open web, Lila generates streams of proprietary scientific data through automated experiments. This strategy gives Lila a renewable and exponentially expanding data asset, which can feed increasingly powerful AI models.
Von Maltzahn sees this as the logical next step in the evolution of scientific research. Humans historically advanced discovery by improving tools—from microscopes to high-throughput sequencing to large supercomputers. Lila now aims to push the scientific method into a new era by enabling AI models to design experiments, run them in physical labs, analyze the results, and repeat the cycle. “We’re going to use those resources very productively in a way that benefits almost everyone on the planet,” von Maltzahn said. He believes this approach will unlock entire classes of breakthroughs that human scientists would need decades to uncover.
Building AI Science Factories
Lila calls its automated facilities “AI Science Factories.” These centers combine advanced robotics, sensor-rich instruments, and AI systems that direct every action. The company treats each factory as a discovery engine that never sleeps. Robotic arms manipulate samples. Instruments gather streams of data. AI models interpret results in real time and decide what to test next. Lila believes this continuous loop of experimentation gives it a unique advantage over competitors that depend solely on computational models.
The new funding enables Lila to expand the scale and capabilities of these factories. The company recently signed a 235,500-square-foot lease in Cambridge, Massachusetts—one of the largest lab leases in the Greater Boston area this year. The site gives Lila the physical footprint to increase the number of robotic instruments, grow its team, and run larger batches of experiments across biology, chemistry, energy science, and materials research.
Von Maltzahn argues that the future of AI-driven science hinges on these physical labs. He frequently contrasts Lila’s philosophy with the broader AI sector’s focus on bigger data centers and larger models. In his view, the companies that win the race for scientific superintelligence will own the largest, most capable automated laboratories. They will control the generation of new experimental data, which no competitor can replicate simply by scaling GPUs or purchasing cloud compute.
A Platform for Industry Partners
Lila plans to open its AI Science Factory platform to commercial users. The company will offer enterprise software that gives customers access to its models, its laboratory automation systems, and its full discovery workflow. Firms in energy, semiconductors, and biopharmaceuticals already expressed interest, according to Lila, although the company has not revealed specific names. The platform aims to let external customers run rapid cycles of experimentation without building their own automated facilities.
Von Maltzahn does not intend for Lila to develop drugs, scale new energy devices, or manufacture materials on its own. He sees Lila as an engine for discovery rather than a vertically integrated operator. “Partners of Lila and startups on a Lila platform will bring molecules into clinical trials or scale up new energy breakthroughs,” he explained. In this model, Lila functions as the scientific backbone for entire ecosystems of research and commercialization.
Early Signs of Discovery at Scale
Lila claims that its platform already produced thousands of discoveries across life sciences, materials science, and chemistry. The company has not yet disclosed specifics, but it notes that these discoveries span areas such as molecular design, new reaction pathways, and novel material behaviors. These early achievements serve as proof of concept for Lila’s core idea: AI systems can generate and interpret experimental data at a scale that human researchers cannot match. The company believes its platform will accelerate timelines for everything from drug development to semiconductor fabrication to energy storage.
The company’s supporters see enormous commercial potential in this approach. Flagship Pioneering, General Catalyst, the Abu Dhabi Investment Authority’s subsidiary, and now Nvidia’s venture arm all view specialized AI for scientific discovery as a transformative market. The funding momentum across the broader sector reinforces this trend. Periodic Labs, another AI science startup founded by former Google DeepMind and OpenAI researchers, recently raised $300 million to pursue a similar goal of building an AI scientist. Investors now treat scientific discovery as the next major frontier for AI investment, with expectations that the field will deliver breakthroughs beyond software—from new therapeutics to advanced materials to renewable energy innovations.
The Race for AI in Science Intensifies
In the global race to build AI systems that reason about scientific problems rather than text or images, Lila positions itself as a pioneer. The company believes that AI must interact with the physical world to reach its full potential. Many AI labs rely exclusively on digital corpora, but Lila’s strategy gives its models dynamic, ever-growing scientific datasets. Von Maltzahn argues that this difference will define the next era of AI leadership. Companies that integrate models with automated labs will uncover scientific laws and patterns that static datasets cannot reveal.
Lila’s rapid growth signals the beginning of a new competitive landscape. Tech giants continue to develop general-purpose models, but startups like Lila and Periodic Labs focus on deep scientific reasoning. Venture capital funds now invest aggressively in these specialized platforms because they see a route to fundamental breakthroughs rather than incremental improvements in consumer AI.
A New Form of the Scientific Method
Von Maltzahn believes Lila’s work represents more than a technological advance. He believes it reshapes the scientific method itself. Instead of human-driven cycles of hypothesis, experiment, and analysis, Lila directs this cycle through AI systems that work continuously and iteratively. Human scientists guide goals and interpret high-level results, but AI takes over the generative and experimental workload.
“It will set in motion the scientific method in a new form,” von Maltzahn said. In his view, this shift gives society access to discoveries that would otherwise take decades or remain entirely out of reach.
As Lila scales its factories, attracts more partners, and grows its data streams, the company aims to become the central engine of scientific innovation for industries around the world. If its vision succeeds, AI-powered laboratories may rewrite the pace and structure of discovery across every scientific field.
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