Of all the debates raging about the potential downsides of AI, there is one worry causing the most hand-wringing among AI enthusiasts in Silicon Valley. Their fear is that the giant AI labs that sell proprietary models are somehow acting like Trojan horses.

The concern is that, as startups and enterprises use AI models from labs like OpenAI and Anthropic, the labs gain ever-increasing access to those companies’ most sensitive business information. The model makers can then use that knowledge for themselves, potentially becoming competitors to their own customers. Those issuing such warnings range from VCs like Jason Calacanis to Palantir CEO Alex Karp.

Now, in a surprising blog post published on Monday, Microsoft CEO Satya Nadella has joined this crowd. Nadella warns that AI users (the “buyers” as he calls them) are paying twice. They knowingly spend for AI token usage but they also, obliviously, hand over valuable data in the process.

“You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!” he writes.

Most dangerously, enterprises are literally teaching the models about the nuances of their businesses, he argues.

“Models learn from ‘exhaust,’ the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how,” he writes.

This is “the kind of knowledge a competitor could never buy,” and yet enterprises are handing it over.

Nadella argues that if AI companies get to freely scrape the internet to train their models, it’s only fair that enterprises get to study — or “distill” — those models in return. “Distillation” is the practice of using a model’s own outputs to learn how it works and to train a new, often cheaper, model based on those insights. In February Anthropic accused Chinese open source models of sending millions of prompts to Claude as a way to improve their own models, and urged the U.S. government crack down on export controls.

Nadella’s point is that model makers can’t have it both ways. It’s hypocritical for them to freely train on the world’s data while restricting others from doing the same to their models.

“While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation,” the Microsoft CEO writes.

Nadella is particularly concerned when model makers “reserve the right to learn from customer usage and interaction data.”

Nadella’s solution is the kind of thing the CEO of a giant cloud provider would suggest. He wants companies to “retain ownership” of their data including prompts, feedback, etc. So he’s urging them to build their own “proprietary learning environments” on the cloud (where their data is likely already stored anyway and, conveniently, which could mean Microsoft’s cloud, Azure). He also wants companies to build in what he calls “orchestration layers” — essentially, a way to easily switch between AI models from different providers rather than being locked into one. Tools like AI “gateways” that let companies do exactly this, have become increasingly popular.

While Nadella never uses the words “open-source” as the method for retaining ownership, this is an obvious subtext. Yet, there’s another subtext.

Large companies, many of which still have some of their own data centers in addition to using the cloud, are already moving to open source models installed on their own premises (“on-prem,” in industry jargon). Idit Levine, founder and CEO of Solo.io — which makes networking and security software that helps enterprises manage AI systems — says she’s seeing exactly this shift play out with her own customers. After experimenting with proprietary model makers, they start asking themselves: “Can I take an open-source model and run it on-prem? It will do almost 90% of what the big one’s doing. It will cost way less,” she tells TechCrunch. “They understand that, and they can control it.”

Solo.io’s technology was selected last year as the tech powering the Linux Foundation’s Agentgateway project. Her company counts enterprises like T-Mobile, ADP and SAP as customers. She sees companies increasingly installing on-premise open source models and sees it as the next big wave in enterprise AI use.

She’s not alone. Vercel — best known as a platform for building and hosting websites, which has recently added AI model-switching tools — and OpenRouter, a company that helps developers route requests across different AI models — are both seeing a surge in traffic to open-source models. In fact, open models accounted for 29% of all traffic routed through Vercel’s gateway last month.

With the CEO of Microsoft, a company that has invested in both OpenAI and Anthropic, now openly urging enterprises to be wary of using proprietary models, we’ll bet this trend continues to grow. “In consuming intelligence, you are creating intelligence. And what you create should belong to you,” Nadella writes.

If you grew up in the 1980s or ’90s, you likely remember shaky home video footage, taken with a handheld camcorder, of family gatherings, vacations, and other events.

Camcorders combined a camera with a video recorder. They included a rechargeable battery, a slot for a videotape, and a shoulder strap. Most were outfitted with an optical zoom lens and a small, articulating screen—a display mounted on a hinge that could tilt and rotate. The operator could check the screen to view what was being recorded.

The user’s natural hand and body movements when filming led to jittery footage. The best way to get a steady shot was to place the camcorder on a tripod or a gimbal: a motorized stabilizer.

There were fewer poor-quality recordings after Panasonic introduced its PV-460 VHS camcorder in 1988. It was the first video camera to include an optical image stabilizer, which compensated for movements. Stabilization features are now standard in today’s cameras including ones found in smartphones and drones.

The PV-460 camcorder was honored as an IEEE Milestone on 9 July. The dedication ceremony was held in Kadoma, Japan, at the Panasonic Museum, which displays the company’s past products.

The IEEE Kansai Section in Japan sponsored the Milestone.

“The release of the PV-460 fundamentally transformed personal videography, enriching the way people captured travel, events, and family memories,” section members wrote in support of the Milestone nomination. Their proposal is available here.

“Its image stabilization features democratized video creation by dramatically lowering technical barriers, allowing ordinary people to express themselves with newfound creative freedom,” they wrote. “Beyond the home, image stabilization technology found critical applications in specialized fields, contributing to advancements in areas such as educational media and telemedicine.”

The history of camcorders

Before the camcorder was invented in 1982, people filming events in the 1970s and early 1980s used two pieces of equipment: a video camera and a separate video cassette recorder (VCR), which were connected by a multipin cable. The camera was about the size of a toaster, and the VCR could be as large as a suitcase. To record, the person operated the camera with one hand and carried the VCR in the other or rested it on a shoulder. The cable transmitted the images from the camera to the cassette.

The PV-460 was made possible by several groundbreaking innovations, according to the Milestone proposal, one of which dates back to the 1950s.

In 1956 Italian manufacturer Durst released its Automatica, considered one of the first cameras to use automatic exposure technology. By combining a light meter with the camera’s internal mechanical systems, the technology removed the necessity of calculating exposure settings by hand when the lighting shifted or other conditions changed. The innovation enabled amateur photographers to take decent pictures.

The next breakthrough technology—autofocus—was invented in 1973 by Norman Stauffer, a manager of research for Honeywell in Littleton, Colo. It uses a sensor, a control system, and a motor to focus on a selected area. The invention led to the development of early electronic autofocus cameras, which eliminated the need for photographers to manually adjust the lens. Stauffer received the 1990 IEEE Masaru Ibuka Consumer Technology Award for his invention.

“The release of the PV-460 fundamentally transformed personal videography, enriching the way people captured travel, events, and family memories.” —Milestone sponsors

U.S. inventor Jerome Lemelson is credited with developing technologies that underpinned the camcorder, according to MIT. In the 1950s and ’60s, Lemelson filed several patent applications related to video and audio recording devices. In 1980 he was granted patents related to a portable video camera system. In 1982 JVC and Sony used the technologies to develop what they called the camera/recorder, which became known as a camcorder.

Sony released the first handheld camcorder in 1983: the Betamovie BMC-100P. It used the Betamax videocassette format and could record up to 3.5 hours of footage on 1.27-centimeter cassette tape. The operator rested the 2.5-kilogram camcorder on top of a shoulder to shoot footage. It sold for around US $2,000 at the time (roughly $33,400 today). The machine couldn’t rewind or play back tapes; it could only record.

Other electronics companies including JVC soon introduced their own models using the VCR format, which eventually replaced Betamax.

Over time, camcorders became more compact.

But none of the companies could fix the shaky-footage problem.

Solving a shaky problem

A team at Panasonic led by researcher Mitsuaki Oshima took on the task of image stabilization: detecting and correcting small camera movements, referred to as camera shake, according to the proposal. Oshima, an IEEE life senior member, is now an honorary Fellow at Panasonic.

“The movements that needed to be detected and corrected included horizontal, vertical, and rotational motions—specifically pitch, yaw, and roll,” the Milestone sponsors wrote. “Rotational motion, in particular, becomes the dominant factor affecting image stability during high-magnification shooting. Therefore, the development team focused on detecting rotational motion and began developing an angular velocity sensor.”

An AVS, essentially a gyroscope, detects how quickly an object is changing its orientation in space.

Sensors capable of detecting angular velocity were large and expensive at the time, making them unsuitable for consumer video cameras, the sponsors wrote. What was needed, they said, was a compact and inexpensive version.

Oshima and his team built a high-performance, small, low-cost vibration-type gyroscope. The stabilization mechanism included a miniaturized sensor paired with an optical-axis correction mechanism.

The mechanism adjusts the lens or image sensor to counteract physical shifting and vibrations, ensuring that the light path remains centered on the sensor—which is crucial for maximizing sharpness and quality, the Milestone sponsors wrote.

“The system detects lens displacement caused by camera shake and immediately compensates for it, ensuring stable video footage,” they wrote. “As a result, the effects of camera shake are minimized, allowing users to capture smooth and steady videos with ease.”

Without Oshima’s image stabilization technology, the PV-460 wouldn’t have been developed and released in 1988.

The technology was patented and broadly licensed by other companies. It has become a standard feature in a variety of imaging applications.

Awards and accolades

The PV-460 gained instant popularity when it debuted in June 1988. It received rave reviews at that year’s Consumer Electronics Show.

Panasonic received a 100 Award in 1989 from R&D World magazine for “the development of a VHS camcorder with an antishake mechanism.”

Oshima’s research paper, “VHS Camcorder With Electronic Image Stabilizer,” and others are available in the IEEE Xplore Digital Library.

To learn more about historical figures in engineering, IEEE Milestones, and IEEE History Center programs and events, check out The Institute’s IEEE Tech History collection. IEEE Spectrum also covers aspects of tech history.

Milestone plaque display

The Milestone plaque is to be displayed on the ground floor of the Panasonic Museum, which is open to the public. The museum is located near the now-shuttered Panasonic research lab where the technology was developed. The plaque reads:

“In 1988 the pioneering PV-460 camcorder equipped with image stabilization for enabling smooth and steady video capture was introduced by Panasonic. By pairing a miniaturized vibrating-structure gyroscope sensor with an optical-axis correction mechanism, the PV-460 eliminated the jitter caused by hand motion. Broad international licensing of this patented scheme made it a standard feature in film and digital cameras, smartphones, and related imaging devices.”

Selected by the IEEE History Committee and endorsed by the IEEE Board of Directors, IEEE Milestones recognize outstanding technical developments around the world that are at least 25 years old. The Milestone program is administered by the IEEE history and heritage group.

On Friday, Apple dropped the bombshell news it was suing OpenAI over the alleged theft of trade secrets, claiming that OpenAI stole Apple’s confidential data and engaged in efforts to learn proprietary information while recruiting former Apple employees.

In accusing OpenAI of stealing secrets about Apple’s unreleased products, Apple revealed that a former employee allegedly siphoned reams of sensitive files from the company’s shared network folders, weeks after leaving Apple for a job at OpenAI.

In its complaint, Apple says the former employee, a system electrical engineer named  Chang Liu, allegedly “exploited a rare, previously unknown authentication bug” that allowed access to the company’s network. The bug is classified as a zero-day vulnerability, meaning that Apple had no time to fix it before it was allegedly exploited.

Apple has since fixed the bug and said it terminated the employee’s access once it learned of this “security breach.” In its complaint, Apple said the bug could have allowed a “few other” people to access data on its network, but alleged that only Liu exploited the bug to steal Apple’s confidential information while no longer an employee, citing a check of its server logs. 

The disclosure, while light in detail, highlights the challenges that organizations face with protecting sensitive corporate data after employees no longer work there. Companies often move to immediately cut off departing staff from further access to protect any sensitive information from leaving, including inadvertently. Companies that fail to fully decommission their employees’ accounts can face future security lapses, data breaches, or malicious actions by disgruntled staff.

Apple spokespeople did not respond to an email from TechCrunch with questions about the security vulnerability, how it was exploited, and when the company decommissioned the employee’s credentials.

“LOL… so funny.”

In the complaint, Apple alleged that Liu took “dozens of Apple’s confidential hardware-related files” over the course of several weeks while as a new OpenAI employee. 

Apple said the files contained “detailed information about unreleased products, engineering presentations, technical specifications, and proprietary project data.” 

The company claims Liu failed to return the Apple-issued work laptop he had previously used to access Apple’s network, suggesting it was once able to send and receive files from Apple’s internal systems. The complaint said that Liu allegedly claimed to have “another computer.” While he was at OpenAI, Liu also allegedly misused the access of an acquaintance, Yu-Ting Peng, a then-Apple employee who later went to work for OpenAI. Liu allegedly used Peng’s Apple-issued work laptop “while she was still employed at Apple and he was not.”

Apple said that during February 2026, Liu “tried to access Apple’s network storage — a cloud-based file repository containing Apple’s confidential engineering files, project documentation, and other proprietary information.”

Liu had allegedly discovered that he “still could access Apple’s network repository after leaving Apple, the result of a then-unknown authentication vulnerability.”

Apple did not describe the authentication “bug” that Liu allegedly used to access Apple’s network. However, authentication bugs generally refer to flaws in the login process that allow improper access to systems or data, either because of a weakness in how the login mechanism works or due to a misconfiguration, such as overbroad permissions or not decommissioning the login credentials of a former employee. 

Apple wrote in its complaint that when Liu learned he had unauthorized access to Apple’s systems, he did not report the bug to Apple under his employment agreement obligations, nor did he return his Apple-issued work laptop. 

The complaint added that Liu also failed to “delete the program that allowed the access” to Apple’s network. The company did not say what program or app that Liu allegedly used to access Apple’s systems. It’s not uncommon for employees to have tools, such as a work-approved VPN or remote-viewing app, that allow them to access sensitive data from outside of the company’s offices using their credentials.

Given that Liu was previously granted credentials to Apple’s network as an employee, TechCrunch asked Apple when the company decommissioned Liu’s access, but we did not hear back.

Once Liu allegedly gained access to the network share, he wrote to Peng: “LOL, I found out I can access the [network storage], so funny.”

Apple filed its suit in the U.S. District Court for the Northern District of California in San Jose, and has demanded a jury trial. OpenAI previously said it has “no interest in other companies’ trade secrets.”

The case, if it proceeds, could begin this year.