According to NEWSCENTRAL, the growing interest in artificial intelligence capable of accelerating scientific discoveries is shaping a new wave of investment in the AI for Science segment. Against the backdrop of slowing growth in traditional language models, venture funds are shifting their focus to infrastructure where AI not only analyzes data but generates it through physical experiments. One of the most notable players in this direction is Lila Sciences, which raised $115 million from a group of investors led by Nvidia Ventures, increasing its valuation above $1.3 billion.
The total investment in Lila has reached $550 million, including $350 million in the Series A round. Participants in the new round include Flagship Pioneering, General Catalyst, and a division of the Abu Dhabi Investment Authority. Founded in 2023, the company aims to create a scientific superintelligence that combines artificial intelligence with fully automated laboratories.
According to NEWSCENTRAL, the funds raised will be directed toward developing AI factories – robotic laboratory complexes where AI independently designs and conducts experiments. Lila’s recent lease of 235,500 square feet in Cambridge, Massachusetts, became one of the region’s largest lab deals, confirming the company’s ambitions to expand its experimental capacity.
The company is also preparing a platform for corporate clients, providing access to laboratory resources and AI models. Interest in collaboration, according to NEWSCENTRAL, comes from companies in the energy, semiconductor, and pharmaceutical sectors, although specific names have not yet been disclosed.
Unlike most labs that train models on internet data, Lila relies on its own scientific data. CEO and co-founder Jeffrey von Maltzan, a partner at venture firm Flagship Pioneering and a leading bioengineering developer, notes that the future of scientific AI will be determined not by the size of data centers but by the scale of laboratories where models can independently obtain and interpret results. According to NEWSCENTRAL, this approach has the potential to accelerate the pace of discoveries and change the very nature of the scientific method.
According to NEWSCENTRAL, the Lila platform has already contributed to thousands of discoveries in materials science, chemistry, and the life sciences. The company does not plan to conduct clinical trials or commercialize research results itself – this will remain the domain of partners and startups operating on the platform.
Lucas Grant, an analyst in the semiconductor industry and production strategies, told NEWSCENTRAL that Nvidia’s participation in the round is not just an investment but a strategic move to strengthen the company’s position in scientific computing infrastructure. He noted that Lila’s approach can shorten the development cycle for new materials, which has direct relevance for the semiconductor industry. “Lila is creating a new type of research ecosystem where experiments are governed by AI and instantly integrated into production chains. For Nvidia, this is a logical step to connect computational and physical processes,” the analyst added.
According to NEWSCENTRAL, interest in AI for Science is growing rapidly. The startup Periodic Labs, founded by former DeepMind and OpenAI researchers, recently raised $300 million to develop similar technologies. This confirms the trend of shifting focus from software solutions to hybrid scientific-engineering platforms.
NEWSCENTRAL forecasts that in the next three to five years, this segment could become one of the fastest-growing categories of technology venture capital. Large companies, including Nvidia, Alphabet, and Microsoft, are likely to continue integrating AI into physical research pipelines. The potential of such systems lies not only in accelerating discoveries but also in reducing R&D costs, making them strategically important for high-tech industries.
NEWSCENTRAL notes that Lila’s continued success will depend on its ability to demonstrate the robustness of its laboratory architecture and prove the commercial viability of its approach. If the company validates its model, it could become a benchmark platform for a new era of scientific research, where AI acts not merely as a tool but as an active participant in the discovery process.