Countless individuals are being sent letters informing them that their medical records were compromised during a cyber incident at the U.S. health technology leader CareCloud earlier this year, as fresh information about the data breach emerges.

The firm has remained silent about the incident since March, when it first acknowledged that cyber criminals had infiltrated one of its six patient data repositories. New revelations reviewed by TechCrunch provide the most comprehensive overview of the breach to date, including that close to 350,000 individuals have been impacted thus far.

Based in New Jersey, CareCloud manages patient records for over 45,000 providers across the U.S., encompassing physician offices, hospitals, and various medical practices. Consequently, the company handles a considerable volume of sensitive medical and billing information pertaining to millions of healthcare patients nationwide.

As per a data breach notice submitted to California’s attorney general’s office this week, CareCloud disclosed that hackers had access to one of its electronic health record data stores for a minimum of six days, from March 10 to March 16. The firm reported that a hacker “purported to have exfiltrated data from databases.” No details were provided on how the hackers validated this claim, but it is common for cybercriminals to present samples of purloined data to victims along with ransom requests to deter online publication.

TechCrunch has not been informed of any ransomware or extortion group taking public responsibility for the data breach at CareCloud.

The notice provided minimal information about the hack aside from its initial disclosure on March 27 to regulators, but it corroborated TechCrunch’s previous findings that the hackers infiltrated the company’s data storage hosted on Amazon Web Services.

TechCrunch has discovered that the data breach impacts at least 345,000 individuals across the U.S., according to reports from several state attorneys general, including those in New Hampshire, Massachusetts, and Texas. TechCrunch has also obtained CareCloud’s report submitted to Maine’s attorney general. 

The count of affected individuals is expected to increase as additional notifications are filed with state agencies. 

The notifications confirm that CareCloud alerted authorities that the compromised data encompassed individuals’ names, mailing addresses, and Social Security numbers, along with government-issued IDs, such as passports and driver’s licenses. Moreover, the notifications indicate that the stolen data contained financial details, including bank account information and credit card numbers, along with a broad range of medical and health-related information.

CareCloud’s CEO Stephen Snyder did not reply to TechCrunch’s inquiry for a statement or to questions regarding the situation.

The cyberattack aimed at CareCloud is the latest in a series of data breaches affecting healthcare providers this year, including an incident involving healthcare revenue tech leader TriZetto that impacted 3.4 million individuals, and a month-long breach at New York’s public health provider NYC Health + Hospitals, where hackers acquired 1.8 million individuals’ health records and numerous employees’ fingerprint scans.

Last week, U.K.-based tech company Craneware, which offers accounting and billing software to thousands of U.S. healthcare providers, confirmed that hackers had pilfered a “significant volume” of data belonging to its clients from their servers, raising alarms about a breach concerning patient data.

Are you aware of more details regarding CareCloud’s data breach? Do you have insider knowledge about its security measures while working at CareCloud? Reach out to this reporter via encrypted message at zackwhittaker.1337 on Signal.

Two years ago, tech innovator Avi Schiffmann unveiled Friend, an AI-equipped wearable designed for conversation that would send you daily text updates. The primary concept was to leverage artificial intelligence to alleviate loneliness. This week, the company revealed a significant redesign of the product, featuring an enhanced addition: a voice.

“Presenting friend 2.0,” Schiffmann tweeted on Thursday. Friend now includes a built-in speaker capable of projecting a distinct and consistent personality to its user.

A new advertisement for the wearable displays a woman interacting with her Friend, presumably discussing her ex-girlfriend. “There’s nothing wrong with being gay,” the necklace assures her.

The scene then transitions to a man on a hillside sharing his filmmaking dreams with his Friend. “Your last movie was incredible!” the necklace praises him.

The new price point for this upgraded version of Friend is $249, significantly higher than the initial $99 when it launched two years ago.

What else can you actually do with your Friend besides casual conversation about your day? That aspect remains ambiguous. In a recent update on X, Schiffmann elaborated on what he believes is the essence of his unconventional product.

“I’m fascinated by this type of connection in trying to provide some kind of confidant, friend, God, not really certain what to call it,” Schiffmann remarked. “But it isn’t an assistant, nor is it a romantic partner.”

Remarkable! Marketing a plastic, algorithm-driven necklace by suggesting it could be a divine presence is quite an audacious strategy.

Naturally, Schiffmann’s company has previously engaged in bold marketing tactics. Last year, Friend’s billboard campaign in the New York City subway gained viral attention after frequent vandalism, presumably by individuals opposed to the idea of human connection being supplanted by a digital talisman.

So far, most AI wearables have struggled to gain traction in mainstream markets. The offering that closely resembled Friend was Humane Inc., which aimed to substitute the iPhone with its AI pin but had to cease operations within a year due to disappointing sales.

Microsoft is in a unique position as AI overtakes the tech industry. It’s one of the world’s largest cloud providers and software-as-a-service companies, while also holding valuable stakes in the two biggest AI labs, OpenAI and Anthropic.

Those incentives are starting to clash as Microsoft posts blockbuster financial results. The company just reported an extremely profitable quarter with $90 billion in revenue and net income of $35.8 billion. For the fiscal year, which ended June 30, Microsoft reported $331.8 billion in revenue with a net income of $133.7 billion for the year.

And CEO Satya Nadella is not about to let the trajectory of Anthropic and OpenAI — which are expanding into applications and agentic infrastructure that could ultimately let them own customer relationships — derail that kind of cash.

Nadella has been preaching to enterprises to use multiple models and to stop relying on the frontier AI labs for the agentic harness/app layer.

Doing so is dangerous, he’s been saying, because it requires companies to share too many of their internal secrets with model makers of dubious trustworthiness. He knows his customers. Enterprise IT fears both data leaks and being locked into a vendor.

Now he has openly told Wall Street analysts during the company’s quarterly conference call Wednesday that this is an opportunity for Microsoft to sell customers its own homegrown models, alongside agents, AI security and more, while promising lower costs.

In other words, he’s pitching Microsoft as an alternative to many of the upscale services that OpenAI and Anthropic are developing for their own growth.

When UBS analyst Karl Keirstead specifically asked Nadella to weigh in on the open vs. closed-sourced debate roiling the AI industry, and how Microsoft will benefit from it, Nadella came out swinging.

“The goal is to have the firm be in control of their own destiny,” the CEO said of enterprises. “We are very, very clear about the architectural sort of design of the platform, which is you got to keep your harness separate from the model … that means any model at any given time is swappable.”

Microsoft, of course, sells a menu of harnesses (aka AI agents), too, under the Copilot name, including its coding agent GitHub Copilot. Coding agents are where much of the AI dollars are being spent today.

And he used the high-profile incident from last week as proof of his warnings.

“If you look even at the Hugging Face incident, the biggest thing that we should take away from that is you can’t sort of depend on any one model,” Nadella said. “You will maybe need multiple models to even remediate some challenges that get caused by one model. Like that’s the way to think about it, right? Which is you can’t be subject to a refusal of one model.”

The incident involved an unreleased model from OpenAI breaking out of its sandbox and successfully mounting a full-scale hack on Hugging Face, all in pursuit of besting a benchmark. Trying to understand what happened, Hugging Face at first tried to use a private frontier model (which it hasn’t named) that refused to help it. So it turned to the Chinese open-source model Z.ai GLM 5.2 to analyze logs and defend its infrastructure. The incident has so shocked the industry that even Sam Altman is now saying that maybe AI development should slow down a bit.

Nadella also made clear that Microsoft is happily selling its own homegrown models, the MAI family, on its own homegrown AI chips, Maya, and pitching them as cheaper alternatives.

“Every customer wants the right model for each task based on quality, latency, cost, and compliance. We offer the broadest model catalog in the cloud with over 11,000 models, including the leads from OpenAI, Anthropic, Mistral, xAI, as well as our own MAI family,” he said.

He added: “We’re also accelerating our own model development. We announced more than a dozen new models across image, voice, transcription, coding, security, including our first reasoning model, MAI thinking one, all with cost-efficient inference at the core for the enterprise use cases. We are co-designing these models with our silicon, and we are seeing 40% better performance per watt when running MAI models on Maya 200.”

As for Mythos? Nadella pointed to Microsoft’s new Mythos competitor announced earlier this week, MAI Cyber One Flash. It “achieves better performance than the much larger Mythos model, but at half the cost when combined with our multi-agent security harness,” he said.

Sure, the Microsoft CEO says that enterprises should use the frontier models that OpenAI and Anthropic offer in their mix. But his bigger message is: don’t trust them enough to rely on them.

Meta founder and CEO Mark Zuckerberg is trying to sell investors on his prediction for the future — one where billions of people will have their own personal AI agents in the next five years. (Let’s hope that future also comes with data centers efficient enough to power all those agents — without triggering a fresh wave of climate disasters.)

“I think that it’s extremely unlikely if you look out five years from now, for example — whatever period of time you want — that you don’t have billions of people with a personal agent that understands your goals and that is just working on your behalf 24/7 to achieve your goals in whatever the domain is that you care about,” Zuckerberg said on Wednesday’s quarterly earnings call with investors.

He added that he could see people using these agents to help them with their finances, health, interpersonal relationships, and household management.

“As we move toward a future where we’re all interacting with multiple agents, I think that WhatsApp and our other messaging surfaces are going to become increasingly important,” he said, noting that WhatsApp is already the leading platform where users interact with Meta AI.

Meta is not alone in setting high expectations for AI systems that can act on a person’s behalf rather than just answer questions. Google emphasized custom AI agents as a key new feature in its Search overhaul, which sparked outcry from users who felt bogged down by the constant onslaught of AI results on Google. Meanwhile, subscriptions to Anthropic’s Claude have skyrocketed as engineers fawn over the agentic coding assistant Claude Code.

Compared to its competitors, however, Meta may not enjoy as much confidence from investors as it continues dumping cash into innovative projects that may or may not pan out — Meta’s stock dropped almost 10% after posting this quarter’s earnings. Meta’s Reality Labs, the organization responsible for its AR glasses, VR headsets, and related software, lost around $4.6 billion this quarter, roughly in line with the losses the division has posted each quarter since 2021. That’s a running total now of around $88 billion.

Meta’s AI spending is likely to climb even higher, which is more of a concern at this juncture. The company reported free cash flow of $784 million this quarter, down from $8.55 billion the same quarter last year. That’s a 91% drop year over year, exacerbated by the company’s investments in AI infrastructure. This week, Meta and BlackRock announced a partnership to build a $14 billion data center in El Paso, Texas.

“We believe that there will continue to be a significantly higher margin on selling intelligence rather than selling compute directly, but we think that there’s a big opportunity, obviously, to sell compute as well,” Zuckerberg said.

Ultimately, he believes that the personal agents that Meta is developing will be “the foundation for our next wave of products and revenue lines in the months and years ahead.”

So far, Meta’s business agents, rolled out globally on WhatsApp and Messenger this quarter, have been adopted by more than one million businesses. It may be harder to get people to adopt consumer AI agents, but the road to “billions” has to start somewhere — the company can’t get there on enterprise agents alone.

The largest electrical grid in the U.S. has struggled to cope with an onslaught of data centers. Now, after an auction to add more generating capacity fell short, the grid’s operator, PJM Interconnection, has said it will cut off data centers and other large users during power shortages.

The decision arrives as the breakneck pace of data center construction has grid operators scrambling to generate power. By 2035, data centers are expected to use 4x more electricity than they do today.

PJM won’t start curtailing supply until June 2027, and the cuts will only apply to data centers that are 50 megawatts or larger. The grid operator is running another auction for new generating capacity.

Similar to other demand response programs, which have existed for decades and typically include large users like manufacturers, the customers who have their power cut will be compensated. Such programs typically give customers advance notice, ranging from 30 minutes to a few days, depending on forecasted demand. 

The move will likely spur many new data centers — and potentially existing ones — to set up their own sources of on-site power. Those that don’t will probably rely on backup generators, which tend to be costlier to run and frequently more polluting. 

Many data centers favor diesel generators since the fuel is widely available and can be stored on-site. Federal regulations allow such generators to be used for up to 50 hours per year for demand response events, and up to 100 hours per year for events like emergencies and maintenance.

This week, Vantage Data Centers came under fire for its apparent coordination with Virginia environmental regulators to cast doubt on a report that said diesel backup generators could contribute to tens of millions of dollars in annual health damages for people living near a 96 megawatt data center in Northern Virginia.

PJM has come under fire in recent months for the way it has managed new generating capacity and large new users, including data centers. The grid operator’s territory runs from Virginia to Illinois, covering 67 million customers. Over the last year, wholesale electricity prices have nearly doubled, and PJM’s independent market monitor blamed data centers for much of the increase. 

Chinese tech giant Baidu has started testing autonomous vehicles in London as part of its partnership with Lyft and Freenow, the German taxi and multi-mobility app that Lyft now owns. Baidu is the latest in a string of companies to test self-driving technology in the UK ahead of commercial robotaxi deployments.

The testing, which began Tuesday with human safety operators, comes nearly a year after the two companies struck a strategic partnership to deploy Baidu’s purpose-built Apollo Go RT6 robotaxi across key European markets through the Lyft platform. The vehicles will eventually be available through Freenow, which Lyft acquired in 2025 for about $197 million.

That deal gave Lyft a foothold in Europe’s ride-hailing market, where a handful of well-funded companies are now jockeying to be first to market with robotaxis.

London is particular is shaping up to be a key battleground in the region. In April, Waymo began testing its autonomous vehicles with human safety operators in the city. Uber and its self-driving tech partner, Wayve, also announced plans to launch a robotaxi service in London this year. That initial service — which customers can now sign up for on an interest list — will have human safety operators behind the wheel before fully driverless operations begin later.

Baidu and Freenow by Lyft (as the latter service is now called) said they expect to invite the public to hail their robotaxis in 2027. The companies, which didn’t provide a more detailed timeline, noted that the launch will depend on regulatory approval.

For now, dozens of test vehicles will operate within London’s borough of Brent. Lyft and Freenow said they continue discussions with safety and city officials, including Transport for London (TfL) and the Centre for Connected and Autonomous Vehicles (CCAV). The UK government is in the process of creating autonomous vehicle regulations and opened applications in May for companies interested in an AV pilot program that lets companies test self-driving vehicles under government oversight.

When the service does launch, Freenow by Lyft said it will operate a hybrid network — employing the same language rival Uber has used — meaning human drivers operating taxis and private-hire vehicles will work alongside the robotaxis.

“As a platform with deep roots in the taxi industry, our priority is ensuring that autonomous technology supports the professional drivers who keep London moving,” Thomas Zimmermann, CEO of Freenow by Lyft, said in a statement.

The cutting-edge of physical AI resembles a Jenga game inside a warehouse located in San Leandro, California.

That warehouse is home to Encord, a firm specializing in data tools designed to train AI models. Andrew Ceja functions as a pilot—the term the company uses for its robotic trainers—and he is meticulously extracting wooden blocks from a precarious tower while equipped with a headset that features a camera to monitor his view. While this method for gathering robot training data is rather typical, this particular headset is outfitted with sensors that record his brain waves as he skillfully dismantles the block structure.

Encord is among a limited yet expanding group of startups betting that the next significant limitation for humanoid and warehouse robots will not be model architecture, but rather the significant lack of authentic physical training data. Instead of merely aiding robotics firms in managing existing data, Encord aims to cultivate a business model around generating the data that is currently absent.

The brain wave headset that Ceja is utilizing was developed by Zander Labs, a German neuroscience startup betting that gauging brain activity—to infer mental states such as error, intent, and surprise—can produce a more advantageous data set for training models. Encord’s collaboration with Zander is presently a pilot initiative; Encord claims that the objective is to create an initial brain wave-tagged data set, test it through customer robotics models, and assess whether it genuinely enhances performance before deciding to expand the project.

Lucas Gehrke, a neuroscientist from Zander overseeing the project, indicates that the level of brain activity registered at any given moment during a task can provide insights for model developers trying to determine when their highest-effort models should be deployed.

According to Vineeth Velmurugan, Encord’s head of robot learning, this is the “bleeding edge” of addressing the robotics data bottleneck. A former member of OpenAI’s robot lab and Berkshire Grey, a warehouse automation company, Velmurugan joined Encord to establish the company’s internal data-creation team.

Encord was established to assist businesses developing machine-vision applications in annotating data and evaluating models. As their clients—Velmurugan indicates that Encord collaborates with numerous leading robotics companies, although he cannot disclose their names—began implementing end-to-end learning in robotic manipulation tasks, executives realized they needed to create training data independently, not just manage it. “The data simply does not exist,” Velmurugan remarked.

The premise that generative AI can replicate its success with robots as it has with chatbots continues to encounter this same hurdle. Large language models (LLMs) were constructed from the text of the entire internet and beyond. Securing the equivalent raw materials for teaching neural networks about physical manipulation is difficult: self-driving car companies gather this data themselves, but scaling it is a challenge. Training from video can be effective, but it falls short in comparison to authentic real-world data. Velmurugan believes it will necessitate a data set roughly five times larger than YouTube’s video corpus to achieve a breakthrough—a scale that elucidates why data generation has evolved into a commercial endeavor rather than merely a research concern.

Satisfy your egocentric data requirements

Companies developing robotic intelligence are now relying on two primary sources: “Egocentric” video gathered by workers wearing cameras, often supplemented with additional viewing angles and metrics, and data collection from robots operated remotely. Encord engages in both, sourcing egocentric data from various factories worldwide and utilizing its San Leandro facility to test novel modalities, such as brain wave data, or assemble data sets around particular skills for fine-tuning.

During a TechCrunch visit, pilots were operating leader-follower systems—robotic arms working in tandem, with one arm directly controlled by a human and the other mimicking its movements—to gather data on tasks like pouring coffee from a pot into mugs (which tends to be quite messy) and stacking poker chips. “Every humanoid firm has requested these components,” Velmurugan notes.

Storage shelves were filled with boxes of artificial flowers in vases, books, plastic vegetables, cat litter trays and scoops, bags, and bundles of wires, the essential materials used for training manipulators in household tasks.

At one station, another pilot, Sofia Infante, skillfully manipulates robotic arms to connect and disconnect ethernet cables from the back of a server—the type of task data center operators hope could be automated, provided robots could achieve the necessary precision. Trying my hand at the controls, I understood why that remains elusive: robotic grippers are significantly less agile than human fingers and lack the versatility we take for granted in our arms.

Another innovative data modality that Encord is pursuing involves a set of sensors attached to the forearm to detect electrical signals in muscles. Videos of humans manipulating objects typically don’t capture the entirety of the hand, but Velmurugan aims to construct a 3D representation of the hand’s location at any point using the arm sensors, fostering a more comprehensive grasp for models.

Encord’s data sets are accompanied by physical descriptions of the content of each video—“right hand tightens bolt”—to assist LLM-based models in comprehending the actions unfolding. Velmurugan estimates that this type of thorough annotation holds 100 times the value of “poor-quality ego data” for training specific tasks, and it incurs only 20 times the cost to produce, which, on paper, seems like a reasonable exchange.

However, “20 times more” is still a substantial sum, and that’s the challenge: scraping content from the internet, as LLM creators did by sourcing information from Stack Overflow and other websites, cost frontier labs nearly nothing. Producing physical training data does not, and that represents the limitation of comparing physical AI to LLMs. This kind of data must be generated, not merely gathered, which alters the economics of constructing these models.

Velmurugan claims that advancements are underway—with Encord’s insight into programs across the industry, he can observe startups and frontier labs alike determining what is effective and what is not in enhancing physical AI models. This perspective—being positioned among many robotics firms simultaneously—is also part of Encord’s appeal. It can identify which data techniques are gaining traction across the industry prior to any individual customer.

This will keep the roughly dozen pilots at Encord’s facility engaged. Both Infante and Ceja are members of an emerging workforce constructing the foundations for neural networks; they previously worked at Scale, another AI data annotation firm, before joining Encord.

Ceja had experience at a waste management firm where his technological interests led him to oversee the maintenance of a robotic trash sorter. Now, as the Jenga tower teeters, he expresses his enjoyment of the challenges presented by training tasks for robots — “It’s something new every day!”