Hackers have stolen the personal information and medical records of more than 3.75 million people in a data breach at health data giant CareCloud, the company has confirmed with federal regulators. The disclosure marks the first confirmation of the scale of the data breach, which is now confirmed to be the fifth-largest theft of health data in 2026 so far.

CareCloud detailed the March data breach in a filing with the Department of Health and Human Services (HHS) on Monday. The number of affected victims was reportedly revised up in an update on Tuesday, though it’s unclear if the figure is expected to rise further.

The New Jersey-based tech company provides electronic medical record storage to tens of thousands of healthcare providers around the United States, consequently serving millions of patients. CareCloud handles a large amount of patient data and billing information on behalf of hospitals, doctor’s offices, and other medical practices.

CareCloud has not publicly commented on the cyberattack since it disclosed the breach in March, when it said hackers had accessed patients’ medical data stored in one of its cloud storage environments over six days. The company later said in data breach notifications that the hackers exfiltrated data from the company’s Amazon Web Services account and stole reams of patient data.

The stolen data includes patients’ names, postal addresses, Social Security numbers, and their medical and health information. The hackers also took government-issued identification numbers, such as passports and driver’s licenses, as well as banking and financial information.

CareCloud chief executive Stephen Snyder has not responded to multiple emails to date requesting information about the incident, including whether the company has paid the hackers; who, if anyone, is responsible for cybersecurity at the company; or if Snyder plans to resign following the incident.

The breach at CareCloud follows several sizable healthcare breaches confirmed this year.

Tech giant TriZetto confirmed in March that a 2024 data breach affected 3.4 million people’s data, and an as-yet-unspecified number of people have had their data stolen during a July data breach at healthtech billing software maker Craneware.

According to HHS’ running tally of healthcare data breaches, dental insurance giant DentaQuest has had the largest data breach this year so far, with at least 15 million people’s personal and health information being affected.

A biotech startup called Vivodyne says the AI drug-discovery industry has a data problem, and that it has built a machine to fix it.

HIVE, modular robotic labs built by the company, can grow 20 kinds of human tissue, then autonomously dose and monitor them, generating the kind of causal biological data that today’s AI models are missing — data that today mostly comes from animal testing, or studies of single cells or proteins, not living tissue.

“Absent human testing, what are these [AI] models going to do?” asks Andrei Georgescu, Vivodyne’s CEO and co-founder. “They’re going to cure cancer in mice.”

Even Anthropic CEO Dario Amodei wrote over the weekend that claims that AI will cure cancer have become more cliche than credible — “the thing that will work is actually curing cancer,” as he put it.

To be fair, the idea that AI will cure cancer is something Amodei himself has tossed out in previous essays; Sam Altman has repeatedly cited curing cancer as a justification for OpenAI’s push toward AGI and ever-larger compute buildouts; and Google DeepMind’s Demis Hassabis said last year that AI could potentially cure all disease within a decade.

The actual results remain tepid. A handful of AI-designed drugs have proceeded into human trials — one as far as Phase III, widespread human testing — but the reality is that the roadblocks aren’t necessarily ones that AI can solve today.

Nobel-prize winning Alphafold was a big advance for understanding the building blocks of life, but it has yet to actually produce a new drug. Isomorphic Labs, founded to build on Alphafold, is expecting its first trials, originally planned for 2025, by the end of this year. In February, the company wrote that true drug discovery will require “highly accurate predictive models, across an expansive range of biochemical properties and interactions.”

Georgescu says the space needs “a sanity check”— that existing models don’t have the data to capture the complexity of human biology. It’s a challenge already facing the pharmaceutical industry, where 90% of drugs that are effective in animal testing to enter clinical trials don’t receive regulatory approval for humans.

Vivodyne’s plan is different. Vivodyne was spun out of the University of Pennsylvania in 2021, after Georgescu received a PhD in bioengineering there. The company says its tissues closely match the behavior of real human organs — that its liver cells have 94% predictive accuracy compared to human trials that test for toxicity, its airway tissue matches the behavior of real human tissue 96% of the time, and its bone marrow has achieved 100% concordance in tests of 20 different chemotherapy drugs.

Last week, the company, which has raised just under $80 million across two rounds led by Khosla Ventures, opened what it calls the world’s largest “human data center” just outside of San Francisco, and Georgescu says his team is already achieving twice the throughput of all the animal trials being held in the US.

The idea is to accelerate the path of drug candidates by having a better idea of what will work before going through the expense of a clinical trial, which typically costs tens of millions of dollars. Though it won’t name its partners publicly, Vivodyne says it is working with multiple major pharma companies to solve a problem that Georgescu compares to automotive crash tests: An automaker is typically confident its car will pass NHTSA requirements before testing it, but drugmakers rarely have that same confidence going into a clinical trial, where the vast majority of drugs fail to win FDA approval.

But there is a larger vision: Georgescu sees his autonomous biology labs as key to generating the kind of causal data that can be used to train new models on human biology. He points to studies like this one, published in Nature Methods last month, that find no clear data scaling laws when training generative AI models on existing cellular data.

“All the training is done on static snapshots of these cells, and the models are not conditioned at all by the how a cell got to that state,” Georgescu told TechCrunch. “In other words, the model learns ‘this is cell state A,’ ‘this is cell state B,’ but never ‘cell state B is the effect of inflaming cell state A.’”

Vivodyne’s HIVE machines, however, are tracking hundreds of thousands of ongoing experiments where diseased tissue is exposed to some stimulus, which Georgescu expects to provide the kind of reinforcement learning that will produce AI models that understand human biology enough to make more meaningful progress in healthcare.

Georgescu believes that will be key not just for today’s medicine challenges, but also for a future where complex diseases require drugs that, unlike the majority of those available today, target multiple pathways.

“If we want combination therapies, the space that has to be searched explodes—it can’t be an experimental approach,” he told TechCrunch. “You have to say, ‘I want this effect to happen, so what cause should I invoke?’ Establishing causality in human biology is the basis of all of this.”

Data center developers are expected to spend as much as $4 trillion by the end of the decade — and they’re already heavily constrained by both political and power-grid considerations in where they can build. But while most treat the speed of fiber as a given, one company is betting that faster fiber could change the geographical math behind the data center buildout.

On Tuesday, Relativity Networks announced $22 million in SAFE note funding drawn by Rhapsody Venture Partners, Bell Ventures Inc., and Faster Than Glass LLC, among others. A SAFE note, in which an investment transfers into a specific numbers of shares once the company raises its first priced round, is a standard method used for pre-seed and seed rounds. The company also secured a $40 million follow-on order from a leading hyperscaler that declined to be named for this piece.

Relativity Networks deals in hollow-core fiber, a rarely deployed technology that allows data to be transmitted 30% faster than conventional fiber. Where traditional fiber transmits light through fiber-optic glass, hollow-core fiber transmits the same light through a vacuum chamber in the center of the line, bringing it far closer to the theoretical limit of light speed. 

The difference is a matter of microseconds. CEO Jason Eisenholz estimates that a signal takes roughly five microseconds to travel one kilometer in conventional fiber. By switching to hollow-core, that figure can be reduced to only three and a half microseconds.

When AI compute occurred across a single rack of GPUs, the fiber latency was easy to ignore — but as scale has grown, so has the physical distance between GPUs. Now, it’s common for a data center campus to sprawl across hundreds of acres and dozens of buildings. Eisenholz sees a particular opportunity for multi-campus deployments, in which pre-existing data centers are knit together to operate as a single unit.

“The largest systems are distributing the compute across multiple campuses to reach the power that exists,” he tells TechCrunch. “They’re moving to where the warm shell is, but they still need to operate as one synchronized machine.”

The result is a way to partially alleviate the harsh spatial logic that has restrained many ongoing data center buildouts. In latency terms, reducing time by 30% is giving developers an opportunity to span 30% larger distances before latency becomes a problem. As compute projects scale ever larger, Einholz thinks it could be a major shift for the industry.

“The first era of AI optimized for compute,” he said. “It was GPU, GPU, GPU. The second era optimized the networking inside the data center to take advantage of that compute. The third era that we see coming is optimizing the geography.”