Microsoft announced Thursday it is teaming up with digital pathology provider Paige to build the world’s largest image-based artificial intelligence model for identifying cancer.
The AI model is training on an unprecedented amount of data that includes billions of images, according to a release. It can identify both common cancers and rare cancers that are notoriously difficult to diagnose, and researchers hope it will eventually help doctors who are struggling to contend with staffing shortages and growing caseloads.
Paige develops digital and AI-powered solutions for pathologists, which are doctors who carry out lab tests on bodily fluids and tissues to make a diagnosis. It’s a specialty that often operates behind the scenes, and it’s crucial for determining a patient’s path forward.
“You don’t have cancer until the pathologist says so. That’s the critical step in the whole medical edifice,” Thomas Fuchs, co-founder and chief scientist at Paige, told CNBC in an interview.
But despite pathologists’ essential role in medicine, Fuchs said their workflow has not changed much in the last 150 years. To diagnose cancer, for instance, pathologists usually examine a piece of tissue on a glass slide under a microscope. The method is tried and true, but if pathologists miss something, it can have dire consequences for patients.
As a result, Paige has been working to digitize the pathologists’ workflow to improve accuracy and efficiency within the specialty.
Doctors working with Paige technology
Source: Paige
The company has received approval from the Food and Drug Administration for its viewing tool FullFocus, which allows pathologists to examine scanned digital slides on a screen instead of relying on a microscope. Paige also built an AI model that can help pathologists identify breast cancer, colon cancer and prostate cancer when it appears on the screen.
Digital pathology is costly
Paige is the only company that has received FDA approval for pathologists to use its AI as a secondary tool for identifying prostate cancer, and CEO Andy Moye said this is likely in part because of barriers related to storage costs and data collection.
Digitizing a single slide can require over a gigabyte of storage, so the infrastructure and costs associated with large-scale data collection balloon quickly. Fuchs said the storage costs can be inhibiting for smaller health systems, which is why wealthy academic centers have historically been the only organizations that can afford to invest in digital pathology.
Paige spun out of the Memorial Sloan Kettering Cancer Center in New York in 2017 and has a “fantastic wealth of data,” according to Moye, which is why the company was able to build its own AI-powered solutions in the first place. To put the scale in perspective, Paige has 10 times more data than Netflix, including all the shows and movies that exist on the platform.
But in order to expand its operations and build an AI tool that can identify more cancer types, Paige turned to Microsoft for help. Over the past year and a half, Paige has been using Microsoft’s cloud storage and supercomputing infrastructure to build an advanced new AI model.
Paige’s original AI model used more than 1 billion images from 500,000 pathology slides, but Fuchs said the model the company has built with Microsoft is “orders of magnitude larger than anything out there.” The model is training on 4 million slides to identify both common and rare cancers, which can be difficult to diagnose. Paige said it is the largest computer vision model that has ever been announced publicly.
“Until ChatGPT got released, no one really understood how this is going to impact their lives. I would argue this is very similar for cancer patients going forward,” Moye said. “This is sort of a groundbreaking, land-on-the-moon kind of moment for cancer care.”
Moye added that the company is thinking of ways to incorporate predictive modeling to give pathologists and patients easy access to information about their biomarkers and genomic mutations down the line.
Desney Tan, vice president and managing director of Microsoft Health Futures, said Microsoft’s infrastructure is a key component of the partnership, but that the company is also working to develop the new algorithms, detection and diagnostics that Paige is hoping to deliver in the next couple of years.
He added that though the technology is powerful, it’s meant to enrich pathologists, not replace them.
“We think of these AI implements, these technologies, as tools, really just as the stethoscope is a tool, just as the X-ray machine is a tool,” Tan told CNBC in an interview. “AI is a tool that is to be wielded by a human.”
On Thursday, Paige and Microsoft will publish a paper on the model through Cornell University’s preprint server arXiv. The paper quantifies the impact of the new model compared with existing models, and Fuchs said it outperforms anything that has been built in academia up to this point.
But the preprint is just the first step of a much longer journey. Paige wanted to make the research available to the broader community while it is under peer review, and the company intends to submit to the scientific journal Nature. The process can take months, if not longer. Paige also has years of work ahead before it will be able to roll the model out as a product — including thorough testing and collaboration with regulators to ensure it is safe and accurate.
Ultimately, Fuchs said the AI model will solve the storage problem for health systems, while also helping pathologists work through cases and arrive at a diagnosis more quickly. For some patients, it could mean the difference between waiting two days and two weeks to find out what’s wrong.
“The more you go away from academic medical centers, especially in community clinics where pathologists are completely overwhelmed across all cancer types with so many cases, there, the impact is quite drastic,” Fuchs said. “That really helps to democratize access to health care in these places.”
Neptune and OpenAI have collaborated on a metrics dashboard to help teams that are building foundation models. The companies will work “even more closely together” because of the acquisition, Neptune CEO Piotr Niedźwiedź said in a blog.
The startup will wind down its external services in the coming months, Niedźwiedź said. The terms of the acquisition were not disclosed.
“Neptune has built a fast, precise system that allows researchers to analyze complex training workflows,” OpenAI’s Chief Scientist Jakub Pachocki said in a statement. “We plan to iterate with them to integrate their tools deep into our training stack to expand our visibility into how models learn.”
OpenAI has acquired several companies this year.
It purchased a small interface startup called Software Applications Incorporated for an undisclosed sum in October, product development startup Statsig for $1.1 billion in September and Jony Ive’s AI devices startup io for more than $6 billion in May.
Neptune had raised more than $18 million in funding from investors including Almaz Capital and TDJ Pitango Ventures, according to its website. Neptune’s deal with OpenAI is still subject to customary closing conditions.
“I am truly grateful to our customers, investors, co-founders, and colleagues who have made this journey possible,” Niedźwiedź said. “It was the ride of a lifetime already, yet still I believe this is only the beginning.”
A person walks by a sign for Micron Technology headquarters in San Jose, California, on June 25, 2025.
Justin Sullivan | Getty Images
Micron said on Wednesday that it plans to stop selling memory to consumers to focus on meeting demand for high-powered artificial intelligence chips.
“The AI-driven growth in the data center has led to a surge in demand for memory and storage,” Sumit Sadana, Micron business chief, said in a statement. “Micron has made the difficult decision to exit the Crucial consumer business in order to improve supply and support for our larger, strategic customers in faster-growing segments.”
Micron’s announcement is the latest sign that the AI infrastructure boom is creating shortages for inputs like memory as a handful of companies commit to spend hundreds of billions in the next few years to build massive data centers. Memory, which is used by computers to store data for short periods of time, is facing a global shortage.
Micron shares are up about 175% this year, though they slipped 3% on Wednesday to $232.25.
AI chips, like the GPUs made by Nvidia and AdvancedMicro Devices, use large amounts of the most advanced memory. For example, the current-generation Nvidia GB200 chip has 192GB of memory per graphics processor. Google’s latest AI chip, the Ironwood TPU, needs 192GB of high-bandwidth memory.
Memory is also used in phones and computers, but with lower specs, and much lower quantities — many laptops only come with 16GB of memory. Micron’s Crucial brand sold memory on sticks that tinkerers could use to build their own PCs or upgrade their laptops. Crucial also sold solid-state hard drives.
Micron competes against SK Hynix and Samsung in the market for high-bandwidth memory, but it’s the only U.S.-based memory supplier. Analysts have said that SK Hynix is Nvidia’s primary memory supplier.
Micron supplies AMD, which says its AI chips use more memory than others, providing them a performance advantage for running AI. AMD’s current AI chip, the MI350, comes with 288GB of high-bandwidth memory.
Micron’s Crucial business was not broken out in company earnings. However, its cloud memory business unit showed 213% year-over-year growth in the most recent quarter.
Analysts at Goldman on Tuesday raised their price target on Micron’s stock to $205 from $180, though they maintained their hold recommendation. The analysts wrote in a note to clients that due to “continued pricing momentum” in memory, they “expect healthy upside to Street estimates” when Micron reports quarterly results in two weeks.
A Micron spokesperson declined to comment on whether the move would result in layoffs.
“Micron intends to reduce impact on team members due to this business decision through redeployment opportunities into existing open positions within the company,” the company said in its release.
Microsoft pushed back on a report Wednesday that the company lowered growth targets for artificial intelligence software sales after many of its salespeople missed those goals in the last fiscal year.
The company’s stock sank more than 2% on The Information report.
A Microsoft spokesperson said the company has not lowered sales quotas or targets for its salespeople.
The sales lag occurred for Microsoft’s Foundry product, an Azure enterprise platform where companies can build and manage AI agents, according to The Information, which cited two salespeople in Azure’s cloud unit.
AI agents can carry out a series of actions for a user or organization autonomously.
Less than a fifth of salespeople in one U.S. Azure unit met the Foundry sales growth target of 50%, according to The Information.
In another unit, the quota was set to double Foundry sales, The Information reported. The quota was dropped to 50% after most salespeople didn’t meet it.
In a statement, the company said the news outlet inaccurately combined the concepts of growth and quotas.
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“Aggregate sales quotas for AI products have not been lowered, as we informed them prior to publication,” a Microsoft Spokesperson said.
The AI boom has presented opportunities for businesses to add efficiencies and streamline tasks, with the companies that build these agents touting the power of the tools to take on work and allow workers to do more.
OpenAI, Google, Anthropic, Salesforce, Amazon and others all have their own tools to create and manage these AI assistants.
But the adoption of these tools by traditional businesses hasn’t seen the same surge as other parts of the AI ecosystem.
The Information noted AI adoption struggles at private equity firm Carlyle last year, in which the tools wouldn’t reliably connect data from other places. The company later reduced how much it spent on the tools.