The tech trial of the year, Musk v. Altman, was ultimately a fight for control. Elon Musk argued that Sam Altman, with whom he helped found the now-massive company OpenAI, shouldn’t direct the future of AI. Altman’s lawyers, in turn, poked at Musk’s own credibility. A jury came to a verdict on Monday after just two hours of deliberation, dismissing Musk’s claims due to the statute of limitations.
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Musk v. Altman proved that AI is led by the wrong people
In a strictly legal sense, three weeks of testimony added up to nothing. But the trial offered a more damning broader takeaway: Almost nobody in this saga seems worth trusting. Some of the most powerful people in tech seem temperamentally incapable of dealing with each other honestly. And if that’s true, it raises a bigger question: Why are they in control of a trillion-dollar industry that’s set to upend people’s lives?
OpenAI was, in the testimony of both Musk and Altman, founded to stop powerful AI from being owned and advanced by the wrong people. Testimony and evidence showed its founding team fretting about who would control artificial general intelligence (AGI), a buzzword for AI that broadly equals or surpasses human knowledge and ability. They deeply feared Google DeepMind and its leader, Demis Hassabis. In 2015, Altman said he’d been mulling over whether anything could “stop humanity from developing AI” — and after concluding it was impossible, that he wanted “someone other than google to do it first.”
Fellow cofounders Greg Brockman and Ilya Sutskever so strongly opposed one-person control that they seemed willing to torpedo a lucrative deal that could — in their words — give Musk an “AI dictatorship.” In a part of the same email addressed to Altman, Brockman and Sutskever questioned his motivations, writing, “We haven’t been able to fully trust your judgements throughout this process … Is AGI truly your primary motivation? How does it connect to your political goals?”
These concerns would be quickly borne out. A central focus of Musk v. Altman was “the blip,” a five-day period in November 2023 when OpenAI’s board removed Altman as CEO. Sutskever had spent more than a year architecting his ouster, assembling a 52-page memo alleging “a consistent pattern of lying, undermining his execs, and pitting his execs against one another.” The implications were broader than executive infighting, potentially impacting the public rollout of AI systems. Then-CTO Mira Murati, for instance, testified in court that Altman told her OpenAI’s legal team had okayed skipping a safety review for one of its models — a statement, she said, that turned out to be false.
In closing arguments, Musk attorney Steven Molo hammered home the long list of people who had testified under oath that Altman was, in one way or another, a liar — all of whom Altman had worked with for years. “The defendants absolutely need you to believe Sam Altman,” Molo told the jury. “If you cannot trust him, if you don’t believe him, they cannot win. It’s that simple.”
But during court proceedings, Musk — who now leads competing lab xAI, under his space company SpaceX — didn’t come off any better. Joshua Achiam, now OpenAI’s chief futurist, testified that Musk’s race against Google led him to take an “obviously unsafe and reckless” approach to achieving AGI. When he and others raised concerns, he says, Musk argued that OpenAI’s for-profit makeover created incentives to disregard safety, but his own xAI is for-profit and has, at best, a haphazard approach to safety. And in the name of making sure OpenAI remained open, Musk was obsessive in his need for control over it. In closing arguments, Sarah Eddy, one of OpenAI’s attorneys, told the jury that Musk “wanted dominion over AGI.”
As one X user put it, “if untrustworthyness had mass, putting Musk and Altman too close to one another would collapse the courtroom and all of earth into a black hole.”
OpenAI and Musk did not immediately respond to requests for comment.
It’s not just Musk and Altman, either. Trial evidence suggested Murati helped get Altman removed, then switched sides to support his reinstatement while appearing “totally uninterested” in disclosing the role she’d played. Shivon Zilis, a close Musk associate who served on OpenAI’s board, asked Musk if he’d “prefer I stay close and friendly to OpenAI to keep info flowing” during his departure — avoiding revealing that she had two children with him at the time. Brockman’s diary entries played a key role in Musk’s case; at one point, he admitted Musk could “correctly” claim “we weren’t honest with him” if OpenAI made a for-profit shift without his involvement.
Musk v. Altman gave each man an opportunity to sling dirt at the other and, in theory, establish himself as the more scrupulous guardian of AI. But a more obvious takeaway is that several of the AI industry’s household names are at best naive — and, at worst, hypocrites with little regard for the consequences of their actions.
Public sentiment about AI is at an all-time low. In a Pew Research survey from last summer, half of US adults said the “increased use of AI in daily life makes them feel more concerned than excited” — and only 10 percent said they felt more excited than concerned. Many of these concerns are related to job loss, but protests are also surging against mass data center construction across the country. Some resistance has turned potentially violent, with individuals allegedly attempting to attack Altman’s home on two occasions. And many tech CEOs themselves maintain that they have bunkers or other doomsday-prepping plans for if things go horribly wrong.
These companies push public messaging that AI empowers its users. But a 2025 Pew Research study found that nearly 60 percent of US adults feel they have little to no control over how AI is used in their lives. In the US, the prospect of meaningful government regulation — which could at least offer some level of external oversight — remains shaky. And now, it’s clearer than ever how far the AI world’s biggest players will go to maintain control.
Amid the trial’s reams of evidence, one document offers a rare example of Altman and Musk offering to cede some power. In March 2015, Altman emailed Microsoft CEO Satya Nadella with a simple request: Sign a letter that he and Musk were drafting, asking the US government to establish “a new regulatory agency for AI safety” and address “the biggest risk to the continued existence of humanity that most people are ignoring.” Weeks later, Nadella responded to shut down the idea. The “issue of human safety and the control problem will become real issues,” he said. But executives, he insisted, should be calling for “federal funding and encouragement of research,” not oversight. Altman promptly agreed. The letter, he promised, would be changed — leaving the option of regulating the AI industry “if and when.”
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First announced over a year ago in April 2025, the Busy Bar will be available for purchase starting on July 14th when the device also starts shipping. Created by the same team behind the Flipper Zero wireless multitool, the Busy Bar is instead described as a “productivity multitool” that relies on a pixelated LED display to help reduce distractions and improve focus. The first 3,000 units purchased on July 14th will be discounted to $199, but the Busy Bar will normally retail for $249. Those who previously joined the Busy Bar waitlist will still be able to purchase one next month for $179.
The Busy Bar looks a lot like an alarm clock, but it’s designed to be used on a desk, perched atop a monitor or cubicle wall, or mounted to a wall or door. When installed in a place where it’s visible to coworkers, family, or roommates, the Busy Bar serves as a status display letting others know when you’re focusing on a task and shouldn’t be distracted.
At the push of a button, the Busy Bar will display a highly visible status message on its 72 x 16 LED pixelated screen that can include a countdown timer so potential distractors know when you’ll be available again. Alongside the status display, the Busy Bar can start a Pomodoro timer and mute notifications on other devices. The Busy Mode can be set to automatically activate through custom triggers, including when you join a phone call, start streaming, begin recording audio, or just open a specific app. It’s also Matter-compatible, allowing it to trigger smart home automations when you need to focus, such as dimming lights or playing music on a speaker.
Flipper Devices has created an open API for the Busy Bar so developers can create their own third-party apps to expand its usefulness and capabilities. You can potentially tie it into an office’s scheduling system to indicate when meeting rooms are booked or available, for example. There will also be accompanying apps available for the device on iOS, Android, macOS, and watchOS, with a native Windows app planned for later this year.
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A routine heart test may be hiding a warning sign that doctors have missed for years. That is the big takeaway from new UC Berkeley research published in Nature. Researchers trained an artificial intelligence model to study ECGs, also called EKGs, and look for patterns tied to sudden cardiac death.
This is the scary part. Sudden cardiac arrest can strike people with known heart problems. However, it can also hit younger athletes and people who never knew they were at risk.
Each year, hundreds of thousands of Americans die after cardiac arrest. Once it happens outside a hospital, survival can drop fast. CPR and a defibrillator can save lives, but timing is everything.
Now, AI may help doctors spot some patients earlier, while their hearts still look normal by today’s common tests.
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DIABETES DRUG COULD SLASH RISK OF FATAL HEART CONDITION IN ONE GROUP, SCIENTISTS REVEAL
UC Berkeley researchers say artificial intelligence can detect hidden ECG patterns linked to sudden cardiac death that routine heart screenings may miss. (Photo by Quentin Top / Hans Lucas / AFP via Getty Images)
How AI found a hidden heart risk
An ECG records the electrical activity of your heart. It creates the familiar spikes and waves doctors review to check rhythm and other heart clues.
For this study, researchers used more than 440,000 ECGs from Sweden. They paired those scans with death certificates and health records. Then they trained the AI model to look for waveform patterns linked to sudden cardiac death.
After that, they tested the model on separate patient data from the U.S. and Taiwan. That step is important because medical AI often looks good in one dataset, then fails in the real world. Here, the model held up across very different health systems.
Why today’s heart screening can miss people
Doctors often use a measurement called left ventricular ejection fraction, or LVEF, to judge risk. In plain terms, it shows how much blood the heart pushes out with each beat.
If that number falls below a certain threshold, a patient may qualify for an implantable defibrillator. That device can shock the heart back into rhythm during a dangerous event.
However, this method leaves big gaps. Many people who die suddenly never had that deeper heart evaluation. Others may have a heart that pumps normally but still be at risk for a dangerous rhythm problem.
The UC Berkeley model found a high-risk group with a 7% annual rate of sudden cardiac death. The standard reduced LVEF group had a 4.6% annual rate.
Even more striking, most patients flagged by the AI were missed by the LVEF method. In other words, a routine ECG may hold warning signs that current screening overlooks.
AI found a hidden ECG warning sign
The researchers did more than ask AI for a risk score. They also tried to understand what the model saw. That is important because medical AI can become a black box if doctors get an answer with no clear reason behind it.
To dig deeper, the team used another AI system to compare low-risk and high-risk ECG patterns. Think of it as a way to see how a normal-looking heartbeat pattern could shift into a higher-risk one.
That comparison pointed to a visible feature in one part of the ECG called aVL. This is one of the standard views doctors use to read the heart’s electrical activity. The feature showed up in the QRS complex, the part of the ECG that reflects the heart’s main electrical signal during each beat.
Researchers say this signal strongly predicted sudden cardiac death. They also say it had not been previously described in medical literature. That raises a fascinating possibility. AI may help doctors make better predictions and spot warning signs humans have missed.
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A new AI model analyzed hundreds of thousands of ECGs and identified patients at higher risk of sudden cardiac death, even when standard heart tests appeared normal. (Photo by Andreas SOLARO / AFP via Getty Images)
Why this could change defibrillator decisions
An implantable defibrillator can save a life. Still, putting one in the wrong patient has risks. The procedure can be invasive and costly. Also, many devices placed under current rules never need to fire.
So doctors face a brutal challenge. Miss the patient who needs the device and the result can be deadly. Implant too many and patients face procedures they may never need.
This new AI tool could help narrow that gap. It may flag patients who need closer monitoring before doctors consider bigger steps.
The next phase is already underway. Researchers are working with health systems in Sweden, Taiwan and the U.S. to test the algorithm on hospital ECG databases.
If the tool flags a scan as high risk, doctors could contact the patient. The patient may then wear a heart-monitoring patch. That could reveal more about the dangerous rhythm before it turns fatal.
The privacy question no one should ignore
There is another side to this story. Medical AI needs huge datasets to work well. Researchers said it took about a decade to compile the data used in this study. That tells you how hard serious clinical AI can be.
But it also raises a fair question. Who controls the data when your scan helps train a medical model? Hospitals, researchers and AI companies need clear guardrails. Patients should know how their health records get protected, shared and used.
Before sharing more health data, review health app permissions, logins and privacy settings. Health apps can hold sensitive information, so small privacy choices can have big consequences. Better prediction can save lives. However, trust will decide how quickly people accept these tools.
What this means to you
This AI tool is promising, but you cannot use it at home today. You cannot upload an ECG and get a personal risk score. Doctors are still testing it before it becomes part of routine care. Still, the idea is powerful. A routine heart test you may have already had could one day reveal a hidden risk that today’s screening might miss.
For now, do not ignore warning signs. Fainting, unexplained dizziness, a racing heartbeat or a family history of sudden cardiac death should be discussed with a doctor. A normal checkup does not always mean every heart risk has been ruled out. If your doctor wants you to track blood pressure, compatible cuffs can sync readings with Apple Health. Wearables can also flag some heart-health clues, including possible hypertension alerts, but they do not replace a doctor.
Also, know what to do in an emergency. Learn CPR if you can. Look for AEDs at work, school, gyms and public places. When cardiac arrest happens, fast action can help save a life.
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8 COMMON FOOD PRESERVATIVES LINKED TO HIGHER RISK OF HIGH BLOOD PRESSURE AND HEART DISEASE
Researchers say AI uncovered a previously unknown ECG warning sign that could help doctors identify dangerous heart rhythm disorders earlier. (Photo: Arne Dedert/dpa (Photo by Arne Dedert/picture alliance via Getty Images)
Kurt’s key takeaways
This is the kind of AI breakthrough that grabs me because it starts with something so ordinary: a routine ECG. Many of us have had one. You lie back, a few stickers go on your chest and a machine prints out a wave pattern most people never think about again. Now, researchers say AI may be able to find a hidden warning sign in that pattern. That is powerful because sudden cardiac death often gives families no time to prepare and doctors no second chance. However, this tool still needs more testing before it becomes part of everyday care. Doctors need to know it works across more patients. Hospitals need a plan for what happens after an AI alert. Patients also deserve clear privacy protections when their medical scans help train these systems. Still, the idea is hard to ignore. A common heart test could someday help spot danger before a person collapses. That to me is hopeful, unsettling and exactly why this kind of medical AI deserves very close attention.
Would you want an AI system scanning your old medical tests for hidden health risks? Let us know by writing to us at CyberGuy.com
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China’s Zhipu AI (Z.ai) released its open-weight GLM-5.2, and some researchers have claimed that it matches Mythos in certain bug-finding and cybersecurity scenarios. While GLM lags behind models from Anthropic and OpenAI in other, more general tasks, it seems that China has dramatically reduced the gap in the capabilities between its models and those of the US.
This level of advancement is particularly concerning to the US government, which has worked to restrict China’s access to powerful models like Anthropic’s Mythos and Fable, as well as the hardware necessary to train and run them. The Trump administration views Mythos and other advanced AI models capable of identifying vulnerabilities as serious national security threats. Recently, OpenAI unveiled GPT-5.6, which has also raised concerns about its potential for misuse and has limited access to it.
Because GLM is an open-weight model, it can be downloaded and run by anyone on readily available hardware. That gives it great flexibility and allows power users deep access, but it also makes it ripe for abuse by bad actors who can run it with little oversight.
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