Microsoft has unveiled “Microsoft Discovery,” an enterprise-focused artificial intelligence platform poised to reshape scientific and engineering research. Announced during its Build 2025 conference, Discovery equips researchers with specialized AI agents and a sophisticated graph-based knowledge engine. The platform aims to dramatically shorten innovation timelines from initial hypothesis to experimental validation. Aseem Datar, Microsoft’s vice president of product innovation, outlined the platform’s ambitious scope: “Our goal is to bring the power of AI to scientists and engineers to transform the entire discovery process, from advanced knowledge reasoning and hypothesis formulation to experimental simulation and iterative learning.”
The platform’s significant potential to compress research timelines was demonstrated by an internal Microsoft project: identifying a novel, non-PFAS (Per- and polyfluoroalkyl substances) datacenter coolant prototype. This breakthrough was achieved in approximately 200 hours—a task traditionally taking months or years. Microsoft Chairman and CEO Satya Nadella highlighted this, explaining that Microsoft is “bringing together the full tech stack to help speed up science itself.” He noted that Discovery “uses agents to generate ideas, simulate results, and learn,” citing the coolant as “a great example … that doesn’t rely on forever chemicals.”
Jason Zander, Microsoft’s Corporate Vice President of Strategic Missions and Technologies, told VentureBeat the framework screened 367,000 potential candidates for this coolant, which was then synthesized by a partner. This rapid result, which Microsoft clarified in its announcement is an “experiment” laying groundwork for future developments, underscores the ambition to transform R&D into a dynamic, AI-assisted endeavor.
Built on Microsoft Azure, Discovery is designed for extensibility, integrating proprietary models with tools from partners like NVIDIA and Synopsys, and open-source solutions. Microsoft is cultivating a collaborative ecosystem, with early involvement from companies such as GSK in healthcare and The Estée Lauder Companies in consumer goods. This signals a strategic push to embed AI deeply within varied industrial research workflows and positions Discovery alongside other “AI co-scientists” and science-focused research tools from competitors like Google and OpenAI.
A New Agentic Approach to Discovery
Microsoft Discovery operates on a paradigm of “agentic AI,” embedding intelligent agents at each stage of the scientific method. Researchers can customize these AI agents using natural language, tailoring them to specific domains like molecular simulation or literature review. A central Microsoft Copilot assistant orchestrates these agents, leveraging a comprehensive catalog of tools, models, and knowledge bases.
At Discovery’s heart is a powerful graph-based knowledge engine. This engine constructs nuanced maps of relationships between an organization’s proprietary data and vast external scientific research, rather than just retrieving facts. This capability allows the platform to offer deep contextual understanding of complex, sometimes contradictory, scientific data, all while maintaining transparency through detailed source tracking.
Zander emphasized the platform’s accessibility for scientists, even those without coding backgrounds, explaining from his own experience: “My PhD is in biology. I’m not a computer scientist, but if you can unlock that power of a supercomputer just by allowing me to prompt it, that’s very powerful.”
Early Adopters and Strategic Partnerships
The platform’s real-world application is already being explored by several key partners. GSK plc intends to utilize Microsoft Discovery for advanced prediction and testing in developing new medicines. Similarly, The Estée Lauder Companies plans to integrate Discovery into its innovation pipeline to accelerate creating personalized skincare and cosmetics, building on an earlier AI lab partnership with Microsoft. Kosmas Kretsos, VP of R&D and Innovation Technology at The Estée Lauder Companies, explained that Discovery will help leverage their extensive research data for “fast, agile, breakthrough innovation.”
Key technology collaborations are set to expand Discovery’s capabilities. NVIDIA will integrate its ALCHEMI and BioNeMo NIM microservices, designed for NVIDIA-accelerated Azure AI infrastructure, to boost materials and life sciences research.. Synopsys is partnering to bring its AI-powered design solutions to accelerate semiconductor engineering.
Raja Tabet, Senior Vice President at Synopsys, noted this collaboration aims to “re-engineer chip design workflows” and “supercharge engineering productivity.” PhysicsX is also a launch partner, integrating its physics-based AI foundation models. CEO Jacomo Corbo described the platform as representing a “seismic shift in how AI can accelerate scientific discovery and engineering.” System integrators like Accenture and Capgemini are also involved to help scale custom deployments.
Navigating the Evolving Landscape of AI in Science
Microsoft’s introduction of Discovery occurs within a rapidly evolving landscape where AI is increasingly pivotal. Competitor Google has been prominent with its “AI Co-Scientist” initiative for generating research hypotheses. The company has also released specialized tools like TxGemma for drug discovery and AlphaEvolve for optimizing algorithms. Microsoft itself previously contributed with models like BioEmu-1 for protein dynamics. The broader trend includes potential high-capability AI research agents from OpenAI.
However, the increasing sophistication of AI in scientific research brings important considerations. A study highlighted by Winbuzzer in April 2025 revealed that advanced AI models could outperform experienced virologists in complex lab procedures, raising dual-use concerns about potential misuse. Seth Donoughe, a study co-author, expressed that the findings made him “little nervous.” This has spurred calls for robust governance frameworks.
Microsoft emphasizes that Discovery’s design prioritizes trust, compliance, and transparency, keeping researchers in control. Yet, the broader scientific community and AI developers continue to grapple with ensuring the reliability and ethical deployment of these transformative technologies. DeepMind’s own prior research, for example, acknowledged that “until model speed is improved and hallucinations are completely resolved, tools like symbolic engines will remain essential for math applications.” This highlights that human expertise and traditional scientific validation remain crucial in the age of AI-accelerated discovery.
