A critique published in Nature Wednesday calls the basic technology behind Microsoft’s “breakthrough” quantum computing chip the Majorana 1 into question. Microsoft unveiled the chip in February 2025 and said it featured a brand-new technology known as a topological qubit. Topological qubits, they said, would be the “building blocks” for their future quantum computer. Microsoft announced the next generation chip Majorana 2 at Build earlier this month.
Technology
Scams that aren’t illegal (but should be)
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Every year during National Consumer Protection Week, you hear warnings about phishing emails, fake IRS calls and identity theft. Those threats are real, but there is another risk that gets far less attention, and it is completely legal.
Right now, hundreds of companies collect, package and sell personal information, including your home address, phone number, family members, income estimates and even your daily habits. They are not targeting you because you did anything wrong. Instead, they profit simply because your data is valuable.
Unlike traditional scams, this does not happen in the shadows. It happens out in the open, every single day. As a result, most people only realize it is happening after someone uses their personal information against them.
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Data brokers build detailed profiles using information pulled from public records, apps and online activity. (Kurt “CyberGuy” Knutsson)
Your personal information is a product
Data brokers are companies most people have never heard of, but they know a surprising amount about you. They collect information from public records, online activity, retail purchases, app usage and hundreds of other sources.
Then they build detailed profiles and sell them to advertisers, marketers and anyone else willing to pay. A typical profile may include:
- Full names, ages and phone numbers
- Home addresses
- Names of relatives and household members
- Estimated income, home value and net worth
- Shopping habits and interests
- Political, health and lifestyle indicators
This information often appears on people-search sites, where anyone can look you up in seconds. Scammers use these same databases to find and target victims. But even legitimate companies use them in ways most consumers never knowingly agreed to.
People-search sites expose more data than you realize
Search your own name online, and you may find pages listing your address, relatives’ names and contact details. These sites present themselves as “background check tools” or “public records directories.” But their business model depends on making personal information easy to find.
- That creates real-world risks. Criminals use these sites to:
- Impersonate banks, government agencies, or delivery services
- Convince victims they already “know” them
- Locate elderly or vulnerable individuals
- Target family members using shared address history.
Even strangers can learn where you live, who your relatives are and how to contact you. No hacking required.
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People-search websites make your address, phone number and even family connections easy to find in seconds. (Serene Lee/SOPA Images/LightRocket via Getty Images)
Your browsing history is being tracked and sold
Many websites and apps track what you click, read and buy. Incogni’s research found that popular apps like TikTok, Alibaba, Temu and Shein collect numerous personally identifiable data points and share them with third parties, like advertising networks and data brokers.
Even web extensions track what you do online. Popular Chrome extensions like the AI-powered Grammarly or Quillbotinvade your privacy, require extensive permissions and collect sensitive data.
Over time, this data collection builds a behavioral profile. It can reveal:
- Financial stress or debt concerns
- Health interests or medical conditions
- Major life events like moving, retirement, or the loss of a spouse
- Online purchases and brand preferences.
This is why you may suddenly receive highly specific emails, calls, or ads that feel uncomfortably personal. Someone already knew what to say.
AI is accelerating data collection
AI makes personal data more valuable and easier to collect than ever before. These systems scrape public websites, social media profiles, images and videos to pull identifying details. They also connect scattered pieces of information into a single, detailed identity profile, which can include:
- Photos connected to your name
- Voice recordings from public videos
- Employment history
- Locations you’ve lived at or visited.
Once collected, this information can circulate indefinitely. You can delete a social media post, but copies of that data may already exist elsewhere.
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The more accessible your personal data is, the easier it becomes for scammers to target you with convincing, personalized attacks. (Kurt “CyberGuy” Knutsson)
Most AI companies collect data by default, unless you opt out
Are you using ChatGPT, Gemini, or even LinkedIn? Then your data is automatically collected from your chatbot conversations, posts, and more. They collect user interactions like prompts, voice recordings, uploaded photos and behavioral data to improve the AI system.
In some cases, you have to manually disable this in settings, but it’s buried in countless opt-out guides or obscure labels. For example, to opt out of LinkedIn data collection, you need to:
- Go to Settings and find the Privacy tab.
- Find the toggle named ‘Data for Generative AI Improvement.’
- Review other default data sharing options.
- Disable everything from personal demographic information to social, economic and workplace research.
AI-powered apps and services continuously switch it up and make it harder for you to opt out. Why? Your data is fueling their business model. The more data points they have, the better they can train their AI and the more money they make.
Why this matters for your safety, not just your privacy
Most people think data collection is just about targeted ads. But the same information can be used to make scams far more convincing. Instead of sending generic phishing emails, scammers can reference your real address or recent activities.
For example: “Hi, Mr. Smith, this is your bank. We noticed unusual activity on your bank account, ending in 0123. Please confirm your information.”
Because the details are accurate, the message feels legitimate. This dramatically increases the chances someone will respond. In many cases, the information came from data broker databases that were legally purchased or accessed.
Consumer protection starts with reducing your digital footprint
National Consumer Protection Week is meant to empower people to protect themselves. That protection shouldn’t stop at obvious scams. It should include limiting how easily your personal information can be found in the first place.
A data removal service helps remove your personal data from data brokers and people-search sites that collect and sell it. Instead of submitting dozens or hundreds of manual requests yourself, they automate the process and continue removing your data as it reappears.
Check out my top picks for data removal services and get a free scan to find out if your personal information is already out on the web by visiting Cyberguy.com.
Get a free scan to find out if your personal information is already out on the web: Cyberguy.com.
Kurt’s key takeaways
When most people think about scams, they imagine criminals hiding in the shadows. But some of the biggest threats to your personal information are operating out in the open. Data brokers legally collect and sell detailed profiles about you. People-search sites make your address, phone number and even relatives easy to find in seconds. Your browsing activity is tracked, packaged and monetized. And now AI is speeding up how quickly that information can be gathered, connected and reused. This is not just about annoying ads. The more accessible your personal data is, the easier it becomes for scammers to sound convincing and target you with precision. Real consumer protection is not only about avoiding suspicious links. It is about limiting where your information lives and who can access it. The less strangers know about you, the harder it is to use your own data against you.
Have you ever searched for your name online and been surprised by what you found? Let us know by writing to us at Cyberguy.com.
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Related Article
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Robots have gotten very good at moving fast, repeating steps and doing jobs that would wear you and me out. But ask a robot to pick up something delicate, oddly shaped or slightly different from the last item it handled, and things can get a little complicated quickly.
That is where a new collaboration between ABB Robotics and PSYONIC comes in. ABB Robotics is working with PSYONIC, a California bionics company, to explore whether real-world touch and motion data from human prosthetic use can help train robotic arms.
In other words, the same kind of bionic hand that helps a person grip a tool, pick up a fragile object or adjust pressure in real time could help teach robots how to do those tasks better.
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SOFT ROBOTIC ARMBAND GIVES PROSTHETIC HAND USERS NATURAL CONTROL
The PSYONIC Ability Hand can capture touch, motion and grip-force data from real human prosthetic use. (ABB Robotics)
How a bionic hand could teach a robot
The collaboration centers on PSYONIC’s Ability Hand and ABB’s GoFa cobot. The Ability Hand was originally developed for prosthetic use. It has multi-articulating fingers, pressure sensors, vibration feedback and flexible mechanics that help it conform to irregular objects. That combination is important because human grip isn’tt one fixed action. You hold a coffee cup differently than a screwdriver. You handle an egg differently than a phone. Most of us do that without thinking about it.
For robots, that instinctive adjustment is hard. ABB and PSYONIC want to explore how movement, contact and grip-force data from the Ability Hand can help train robots to handle objects that are fragile, uneven or unpredictable. ABB’s GoFa cobot brings the industrial side of the equation, offering the accuracy and repeatability needed to test those movements in a controlled way. The result could be a robot arm that learns from real human handling data, then applies that information to factory and warehouse tasks.
Why robot grip is such a hard problem
Industrial robots can already lift, move, weld, sort and assemble with impressive speed. However, many still struggle when a task involves subtle touch. Think about a robot picking up a soft package, a medical component or a part that shifts slightly on a conveyor belt. Too much pressure can damage the item. Too little pressure can make the robot drop it. A tiny change in angle can throw off the whole process.
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That is why gripping and dexterity remain major challenges in automation. ABB calls this a key part of Autonomous Versatile Robotics, or AVR, its vision for robots that can sense, reason, move and handle objects with precision in changing environments.
Marc Segura, president of ABB Robotics, put it this way: Human dexterity remains “one of the most difficult things to replicate in industrial-grade robotics.” He said the collaboration with PSYONIC could help “close the long-standing gap” between human and robot dexterity. That gap is where this technology could make a real difference.
What makes the PSYONIC Ability Hand different
The PSYONIC Ability Hand was built to help people. It uses myoelectric control, touch sensing and compliant mechanics in a lightweight design. Its sensors can detect pressure during a grip, while vibration feedback can help communicate touch back to the person using it. That same sensing ability could be valuable for robots.
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PSYONIC says the Ability Hand can capture detailed data about movement, contact and grip force. When that hand is used by people in real-world situations, it can generate a more natural dataset than a lab-only robot demonstration.
ABB’s GoFa cobot is being used to test how bionic hand data could help robots handle delicate and irregular objects. (ABB Robotics)
Dr. Aadeel Akhtar, founder and CEO of PSYONIC, called dexterous manipulation “a data challenge as much as a hardware challenge.” That line really gets to the heart of this. Better robot hands are important. Yet the training data behind those hands may be what decides how useful they become in real workplaces.
Where bionic hand data could show up first
ABB and PSYONIC say this work could apply across automotive, aerospace, packaging, logistics and life sciences. That makes sense. These are industries where robots already play a major role, but where delicate or variable handling can still slow things down. A robot that can better adjust its grip could help with fragile components, oddly shaped products, soft packaging or repetitive tasks that are tough on the body.
HUMANOID ROBOTS HANDLE QUALITY CHECKS AND ASSEMBLY AT AUTO PLANT
The International Federation of Robotics has also pointed to advanced gripping and digital integration as a way to reduce engineering time by up to 30%. That’s important for companies because automation often gets delayed by setup, tuning and custom engineering. If touch-enabled robotic hands can reduce some of that work, companies could deploy robots faster and use them in more flexible ways.
How touch-trained robots could change factory work
There is a hopeful side to this. Robots that handle repetitive or ergonomically challenging work could reduce strain on people. That could mean fewer workers stuck doing the same painful motion all day. However, there is also a bigger labor question here. More capable robots could take on tasks that once seemed too variable to automate. That may affect how companies hire, train and assign work in the future.
The most useful version of this technology would support people instead of simply replacing them. For example, robots could handle the repetitive gripping while workers focus on oversight, quality checks, machine setup and higher-skill work.
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Kurt’s key takeaways
ABB Robotics and PSYONIC are taking a different approach to one of robotics’ hardest problems: touch. Instead of training robots only in a lab, they want to use real movement and grip data from a bionic hand that people already use. That could help robots become better at delicate, variable tasks that have traditionally been hard to automate. It could also push industrial robots closer to working safely and effectively around humans in more settings. But the human side should not get lost in the excitement. If robots are going to learn from human touch, companies need to be clear about data use, workplace impact and safety testing.
The collaboration could help robots become more useful in factories, warehouses and other workplaces where precise grip matters. (ABB Robotics)
Would you feel comfortable knowing a robot at work was trained using real human touch data? Let us know by writing to us at CyberGuy.com.
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Copyright 2026 CyberGuy.com. All rights reserved.
Amazon’s Prime Day is now in its second day, and whether you’re looking for a new pair of wireless earbuds or a smartwatch, there’s a good chance you’ll find a discount. The Apple Watch Series 11 has already dropped to a new low price, while the AirPods Pro 3 are discounted to $179. With Tim Cook warning that price hikes are coming, now may be the moment if you’ve been eyeing one of the company’s devices.
Below are the best Apple deals currently available. Some are exclusive to Prime Day, while others are simply great discounts we think are worth highlighting. We’ll continue updating this guide throughout Prime Day, highlighting more deals as they become available.
Earbud and headphone deals
Update, June 24th: Adjusted prices and availability, and added deals for Apple’s MagSafe Charger as well as the Apple Magic Keyboard.
But in a peer-reviewed article, Henry Legg, a physicist at the University of St Andrews, reanalyzed Microsoft’s data on their device and argued that the company’s researchers did not conclusively demonstrate a working topological qubit in the first place.
Theory predicts that the electrons in this wire behave in a collective pattern known as a Majorana particle, for which the chip is named.
Proponents of quantum computing predict that the technology’s computational abilities will advance new medicine discovery, encryption, and machine learning. Companies like Google and IBM have already demonstrated more advanced machines than Majorana 1 or 2, although presently, no one has conclusively gotten any quantum computer to perform anything useful. But Microsoft claimed that Majorana 1, and subsequently Majorana 2, paved their path toward a practical quantum computer.
Microsoft’s design, unique among quantum computing companies, involves a tiny wire, thinner than a human hair, made of the semiconductor indium arsenide stuck to a superconductor. Theory predicts that the electrons in this wire behave in a collective pattern known as a Majorana particle, for which the chip is named. Microsoft wants to encode information in the properties of the Majorana particle. (A topological qubit is to a Majorana particle as a transistor is to silicon.)
Proponents of the Majorana particle think it is promising qubit material because theory predicts that when formed into topological qubits, the Majorana should compute with fewer errors than competing materials, such as superconducting circuits pursued by IBM. This suggests that ultimately, fewer topological qubits are needed to scale up to a useful quantum computer.
That is, if Microsoft has actually made a Majorana particle. “They haven’t convincingly shown that they have Majoranas,” Legg told The Verge. “You can’t make a qubit if you don’t have the Majoranas.”
In Legg’s critique, he writes that what Microsoft claims as a signature of the Majorana particle could actually be from the formation of quantum dots, which are electron-containing structures, in the device. Quantum dots would not be useful for building the quantum computer. He also writes that Microsoft cherry-picked their data.
“You can’t make a qubit if you don’t have the Majoranas.”
Microsoft’s team published a rebuttal in Nature disputing Legg’s interpretation of their data. Legg’s critique “does not constitute a substantial scientific challenge to our findings,” the Microsoft team wrote. Legg has not “proposed an alternative model that fits all of our data,” Chetan Nayak, a physicist leading Microsoft’s quantum team, told The Verge.
Legg first posted his critique on the online physics repository arXiv on February 26, 2025, within a week of Microsoft’s Majorana 1 announcement. It took a year for Nature to conduct a peer review and publish his article.
Meanwhile, on June 2, Microsoft announced a new chip, the Majorana 2, featuring what they claimed was the next generation of their topological qubits. The company says they can build a “scalable quantum computer” by 2029. “We 100% stand behind our results,” Nayak told The Verge. “We stand by our roadmap. We stand behind our long-standing commitment to scientific rigor and dialogue.”
Legg says the company’s characterization of Majorana 2, which Microsoft wrote in a non-peer reviewed manuscript, suffers from similar problems he pointed out a year ago. “Nothing in this [manuscript] resolves the fundamental issues that so many scientists have with this company’s previous claims,” Legg told The Verge.
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