Microsoft launched a brand new enterprise platform that harnesses artificial intelligence to dramatically speed up scientific research and development, potentially compressing years of laboratory work into weeks and even days.
The platform, called Microsoft Discovery, leverages specialized AI agents and high-performance computing to assist scientists and engineers tackle complex research challenges without requiring them to write down code, the corporate announced Monday at its annual Build developer conference.
“What we’re doing is actually taking a take a look at how we will apply advancements in agentic AI and compute work, after which on to quantum computing, and apply it within the really necessary space, which is science,” said Jason Zander, Corporate Vice President of Strategic Missions and Technologies at Microsoft, in an exclusive interview with VentureBeat.
The system has already demonstrated its potential in Microsoft’s own research, where it helped discover a novel coolant for immersion cooling of information centers in roughly 200 hours — a process that traditionally would have taken months or years.
“In 200 hours with this framework, we were capable of undergo and screen 367,000 potential candidates that we got here up with,” Zander explained. “We actually took it to a partner, they usually actually synthesized it.”
How Microsoft is putting supercomputing power within the hands of on a regular basis scientists
Microsoft Discovery represents a major step toward democratizing advanced scientific tools, allowing researchers to interact with supercomputers and complicated simulations using natural language slightly than requiring specialized programming skills.
“It’s about empowering scientists to remodel all the discovery process with agentic AI,” Zander emphasized. “My PhD is in biology. I’m not a pc scientist, but in the event you can unlock that power of a supercomputer just by allowing me to prompt it, that’s very powerful.”
The platform addresses a key challenge in scientific research: the disconnect between domain expertise and computational skills. Traditionally, scientists would want to learn programming to leverage advanced computing tools, making a bottleneck within the research process.
This democratization could prove particularly worthwhile for smaller research institutions that lack the resources to rent computational specialists to enhance their scientific teams. By allowing domain experts to directly query complex simulations and run experiments through natural language, Microsoft is effectively lowering the barrier to entry for cutting-edge research techniques.
“As a scientist, I’m a biologist. I don’t know learn how to write computer code. I don’t wish to spend all my time going into an editor and writing scripts and stuff to ask a supercomputer to do something,” Zander said. “I just wanted, like, that is what I would like in plain English or plain language, and go do it.”
Inside Microsoft Discovery: AI ‘postdocs’ that may screen a whole bunch of hundreds of experiments
Microsoft Discovery operates through what Zander described as a team of AI “postdocs” — specialized agents that may perform different facets of the scientific process, from literature review to computational simulations.
“These postdoc agents try this work,” Zander explained. “It’s like having a team of oldsters that just got their PhD. They’re like residents in medicine — you’re within the hospital, but you’re still ending.”
The platform combines two key components: foundational models that handle planning and specialized models trained for particular scientific domains like physics, chemistry, and biology. What makes this approach unique is the way it blends general AI capabilities with deeply specialized scientific knowledge.
“The core process, you’ll find two parts of this,” Zander said. “One is we’re using foundational models for doing the planning. The other piece is, on the AI side, a set of models which can be designed specifically for particular domains of science, that features physics, chemistry, biology.”
According to an organization statement, Microsoft Discovery is built on a “graph-based knowledge engine” that constructs nuanced relationships between proprietary data and external scientific research. This allows it to know conflicting theories and diverse experimental results across disciplines, while maintaining transparency by tracking sources and reasoning processes.
At the middle of the user experience is a Copilot interface that orchestrates these specialized agents based on researcher prompts, identifying which agents to leverage and establishing end-to-end workflows. This interface essentially acts because the central hub where human scientists can guide their virtual research team.
From months to hours: How Microsoft used its own AI to unravel a critical data center cooling challenge
To show the platform’s capabilities, Microsoft used Microsoft Discovery to handle a pressing challenge in data center technology: finding alternatives to coolants containing PFAS, so-called “perpetually chemicals” which can be increasingly facing regulatory restrictions.
Current data center cooling methods often depend on harmful chemicals which can be becoming untenable as global regulations push to ban these substances. Microsoft researchers used the platform to screen a whole bunch of hundreds of potential alternatives.
“We did prototypes on this. Actually, after I owned Azure, I did a prototype eight years ago, and it really works super well, actually,” Zander said. “It’s actually like 60 to 90% more efficient than simply air cooling. The big problem is that coolant material that’s on market has PFAS in it.”
After identifying promising candidates, Microsoft synthesized the coolant and demonstrated it cooling a GPU running a video game. While this specific application stays experimental, it illustrates how Microsoft Discovery can compress development timelines for firms facing regulatory challenges.
The implications extend far beyond Microsoft’s own data centers. Any industry facing similar regulatory pressure to interchange established chemicals or materials could potentially use this approach to speed up their R&D cycles dramatically. What once would have been multi-year development processes might now be accomplished in a matter of months.
Daniel Pope, founding father of Submer, an organization focused on sustainable data centers, was quoted within the press release saying: “The speed and depth of molecular screening achieved by Microsoft Discovery would’ve been inconceivable with traditional methods. What once took years of lab work and trial-and-error, Microsoft Discovery can accomplish in only weeks, and with greater confidence.”
Pharma, beauty, and chips: The major firms already lining up to make use of Microsoft’s recent scientific AI
Microsoft is constructing an ecosystem of partners across diverse industries to implement the platform, indicating its broad applicability beyond the corporate’s internal research needs.
Pharmaceutical giant GSK is exploring the platform for its potential to remodel medicinal chemistry. The company stated an intent to partner with Microsoft to advance “GSK’s generative platforms for parallel prediction and testing, creating recent medicines with greater speed and precision.”
In the buyer space, Estée Lauder plans to harness Microsoft Discovery to speed up product development in skincare, makeup, and fragrance. “The Microsoft Discovery platform will help us to unleash the facility of our data to drive fast, agile, breakthrough innovation and high-quality, personalized products that can delight our consumers,” said Kosmas Kretsos, PhD, MBA, Vice President of R&D and Innovation Technology at Estée Lauder Companies.
Microsoft can also be expanding its partnership with Nvidia to integrate Nvidia’s ALCHEMI and BioNeMo NIM microservices with Microsoft Discovery, enabling faster breakthroughs in materials and life sciences. This partnership will allow researchers to leverage state-of-the-art inference capabilities for candidate identification, property mapping, and artificial data generation.
“AI is dramatically accelerating the pace of scientific discovery,” said Dion Harris, senior director of accelerated data center solutions at Nvidia. “By integrating Nvidia ALCHEMI and BioNeMo NIM microservices into Azure Discovery, we’re giving scientists the flexibility to maneuver from data to discovery with unprecedented speed, scale, and efficiency.”
In the semiconductor space, Microsoft plans to integrate Synopsys’ industry solutions to speed up chip design and development. Sassine Ghazi, President and CEO of Synopsys, described semiconductor engineering as “amongst essentially the most complex, consequential and high-stakes scientific endeavors of our time,” making it “a particularly compelling use case for artificial intelligence.”
System integrators Accenture and Capgemini will help customers implement and scale Microsoft Discovery deployments, bridging the gap between Microsoft’s technology and industry-specific applications.
Microsoft’s quantum strategy: Why Discovery is only the start of a scientific computing revolution
Microsoft Discovery also represents a stepping stone toward the corporate’s broader quantum computing ambitions. Zander explained that while the platform currently uses conventional high-performance computing, it’s designed with future quantum capabilities in mind.
“Science is a hero scenario for a quantum computer,” Zander said. “If you ask yourself, what can a quantum computer do? It’s extremely good at exploring complicated problem spaces that classic computers just aren’t capable of do.”
Microsoft recently announced advancements in quantum computing with its Majorana one chip, which the corporate claims could potentially fit 1,000,000 qubits “within the palm of your hand” — in comparison with competing approaches that may require “a football field value of apparatus.”
“General generative chemistry — we expect the hero scenario for high-scale quantum computers is definitely chemistry,” Zander explained. “Because what it will probably do is take a small amount of information and explore an area that might take tens of millions of years for a classic, even the biggest supercomputer, to do.”
This connection between today’s AI-driven discovery platform and tomorrow’s quantum computers reveals Microsoft’s long-term strategy: constructing the software infrastructure and user experience today that can eventually harness the revolutionary capabilities of quantum computing when the hardware matures.
Zander envisions a future where quantum computers design their very own successors: “One of the primary things that I would like to do after I get the quantum computer that does that sort of work is I’m going to go give it my material stack for my chip. I’m going to mainly say, ‘Okay, go simulate that sucker. Tell me how I construct a brand new, a greater, new edition of you.’”
Guarding against misuse: The ethical guardrails Microsoft built into its scientific platform
With the powerful capabilities Microsoft Discovery offers, questions on potential misuse naturally arise. Zander emphasized that the platform incorporates Microsoft’s responsible AI framework.
“We have the responsible AI program, and it’s been around, actually I feel we were considered one of the primary firms to really put that sort of framework into place,” Zander said. “Discovery absolutely is following all responsible AI guidelines.”
These safeguards include ethical use guidelines and content moderation just like those implemented in consumer AI systems, but tailored for scientific applications. The company appears to be taking a proactive approach to identifying potential misuse scenarios.
“We already search for particular sorts of algorithms that might be harmful and take a look at and flag those in content moderation style,” Zander explained. “Again, the analogy can be very just like what a consumer sort of bot would do.”
This give attention to responsible innovation reflects the dual-use nature of powerful scientific tools — the identical platform that would speed up lifesaving drug discovery could potentially be misused in other contexts. Microsoft’s approach attempts to balance innovation with appropriate safeguards, though the effectiveness of those measures will only turn out to be clear because the platform is adopted more widely.
The greater picture: How Microsoft’s AI platform could reshape the pace of human innovation
Microsoft’s entry into scientific AI comes at a time when the sector of accelerated discovery is heating up. The ability to compress research timelines could have profound implications for addressing urgent global challenges, from drug discovery to climate change solutions.
What differentiates Microsoft’s approach is its give attention to accessibility for non-computational scientists and its integration with the corporate’s existing cloud infrastructure and future quantum ambitions. By allowing domain experts to directly leverage advanced computing without intermediaries, Microsoft could potentially remove a major bottleneck in scientific progress.
“The big efficiencies are coming from places where, as a substitute of me cramming additional domain knowledge, on this case, a scientist having learned to code, we’re mainly saying, ‘Actually, we’ll let the genetic AI try this, you possibly can do what you do, which is use your PhD and get forward progress,’” Zander explained.
This democratization of advanced computational methods may lead to a fundamental shift in how scientific research is conducted globally. Smaller labs and institutions in regions with less computational infrastructure might suddenly gain access to capabilities previously available only to elite research institutions.
However, the success of Microsoft Discovery will ultimately rely upon how effectively it integrates into complex existing research workflows and whether its AI agents can truly understand the nuances of specialised scientific domains. The scientific community is notoriously rigorous and skeptical of recent methodologies – Microsoft might want to show consistent, reproducible results to realize widespread adoption.
The platform enters private preview today, with pricing details yet to be announced. Microsoft indicates that smaller research labs will give you the chance to access the platform through Azure, with costs structured similarly to other cloud services.
“At the tip of the day, our goal, from a business perspective, is that it’s all about enabling that core platform, versus you having to arise,” Zander said. “It’ll just mainly ride on top of the cloud and make it much easier for people to do.”
Accelerating the longer term: When AI meets scientific method
As Microsoft builds out its ambitious scientific AI platform, it positions itself at a novel juncture within the history of each computing and scientific discovery. The scientific method – a process refined over centuries – is now being augmented by among the most advanced artificial intelligence ever created.
Microsoft Discovery represents a bet that the subsequent era of scientific breakthroughs won’t come from either sensible human minds or powerful AI systems working in isolation, but from their collaboration – where AI handles the computational heavy lifting while human scientists provide the creativity, intuition, and demanding considering that machines still lack.
“If you consider chemistry, materials sciences, materials actually impact about 98% of the world,” Zander noted. “Everything, the desks, the displays we’re using, the clothing that we’re wearing. It’s all materials.”
The implications of accelerating discovery in these domains extend far beyond Microsoft’s business interests and even the tech industry. If successful, platforms like Microsoft Discovery could fundamentally alter the pace at which humanity can innovate in response to existential challenges – from climate change to pandemic prevention.
The query now isn’t whether AI will transform scientific research, but how quickly and the way deeply. As Zander put it: “We need to start out working faster.” In a world facing increasingly complex challenges, Microsoft is betting that the mixture of human scientific expertise and agentic AI could be precisely the acceleration we want.
