Category: AI

A nine-person federal jury in Oakland, California, unanimously rejected Elon Musk's lawsuit against OpenAI, Sam Altman, Greg Brockman, the OpenAI Foundation, and Microsoft on Monday, ending a three-week trial with a verdict that took jurors less than two hours to reach. The outcome turned not on whether OpenAI had betrayed its founding mission, but on something far more procedural: Musk waited too long to complain about it.

U.S. District Judge Yvonne Gonzalez Rogers adopted the advisory verdict and dismissed all of Musk's claims on statute-of-limitations grounds. The applicable deadlines were three years for breach of charitable trust claims and two years for unjust enrichment claims. By the time Musk filed suit, the clock had already run out.

The core of Musk's case was that Sam Altman and Greg Brockman had steered OpenAI away from its original nonprofit mission without his knowledge, enriching themselves and their investors in the process. Musk helped found the company in 2015 and contributed roughly $38 million in its early years, and he argued that those contributions came with an implicit understanding that OpenAI would remain a public-interest organization rather than a for-profit enterprise.

OpenAI pushed back hard on that framing. The company argued that there was never any promise to remain a nonprofit in perpetuity, and that Musk had been aware of discussions about restructuring dating back to 2017. A for-profit subsidiary was created in 2019. OpenAI's lawyers also argued that Musk filed the lawsuit not out of principled concern for the company's mission but because he failed to gain unilateral control of one of the most valuable AI organizations in the world. That argument landed well enough that it apparently influenced the broader narrative of the trial, even if the jury's final decision rested on timing alone.

Microsoft, which has made substantial investments in OpenAI, was also named in the suit. Jurors rejected the claim that Microsoft aided and abetted a breach of charitable trust, again on statute-of-limitations grounds. Microsoft said after the verdict that the facts and timeline had "long been clear" and that it remained committed to its work with OpenAI.

The trial drew testimony from some of Silicon Valley's most prominent figures and unfolded over three weeks in Oakland. By the standards of tech industry litigation, which tends to be expensive, slow, and often anticlimactic, this one delivered a fairly decisive conclusion. The jury's sub-two-hour deliberation suggests the limitations question was not a close call.

Musk posted on X, the platform he owns, that he would appeal. Whether that appeal has a realistic path forward is a separate question, but it is consistent with how he has approached this dispute since filing the original complaint. The case has always been as much about public positioning as legal remedy, and an appeal keeps the narrative alive even after the verdict.

For OpenAI, the ruling clears one significant obstacle as the company pursues a conversion to a for-profit structure that has generated scrutiny from regulators and competitors alike. The Straits Times noted that the verdict removes a legal cloud that had been hanging over any potential IPO plans. None of that is settled by Monday's jury decision, but losing a $150 billion lawsuit, as the New York Times characterized the stakes, would have been a more complicated starting point for whatever comes next.

The verdict does not resolve the deeper tensions behind the case. Questions about how AI companies govern themselves, what obligations early donors and co-founders retain when an organization pivots toward commercial priorities, and whether nonprofit origins can constrain a company indefinitely are genuinely unresolved in American law. Those questions just will not be answered in this particular courtroom.

What the verdict does establish is that if you believe you have been wronged, the law expects you to say so within a reasonable window of time. OpenAI's transformation from nonprofit research lab to one of the most commercially valuable AI companies in the world happened gradually and, according to the jury, with sufficient visibility that Musk should have acted sooner. The case he eventually brought arrived after the statute of limitations had expired, and that was enough to end it.

A nine-person federal jury in Oakland, California, took less than two hours on Monday to reject every claim Elon Musk brought against OpenAI, Sam Altman, Greg Brockman, and Microsoft. The verdict was unanimous and swift: Musk waited too long to file his 2024 lawsuit, and the applicable statutes of limitations had expired. U.S. District Judge Yvonne Gonzalez Rogers adopted the advisory verdict as the court's decision and dismissed all claims on the spot.

The suit had grown from a genuine dispute over what OpenAI was supposed to be. Musk co-founded the company alongside Altman and Brockman and contributed roughly $38 million in its early years, when OpenAI operated as a nonprofit with a stated mission to develop artificial intelligence for the broad benefit of humanity. His core allegation was that Altman and Brockman betrayed that founding mission by attaching a for-profit corporate structure to the organization and accepting large investments from Microsoft, among others. Musk's legal theories included breach of charitable trust and unjust enrichment against the OpenAI executives, plus an aiding-and-abetting claim against Microsoft.

The central dispute at trial was not really whether any of that happened. It was about when Musk found out. His legal team argued that Microsoft's $10 billion investment in 2023 was the moment he realized OpenAI had strayed from its founding terms, making that the clock's start date. OpenAI countered with earlier evidence of restructuring that, in its telling, Musk had known about for years before finally deciding to sue.

The jury sided with OpenAI's timeline. Judge Gonzalez Rogers, who presided over three weeks of testimony from Musk, Altman, and Microsoft CEO Satya Nadella, said afterward that there was "a substantial amount of evidence to support the jury's finding," adding that she had been prepared to dismiss the case on the spot once deliberations concluded. That is not a judge softening a loss for the plaintiff.

Musk took to X to offer his own framing. The judge and jury, he wrote, "never actually ruled on the merits of the case, just on a calendar technicality," and he announced plans to appeal. Calling a statute of limitations a technicality is a bit like calling the deadline for a tax return a technicality. The law treats timeliness as substantive, not administrative, and courts have long held that plaintiffs who sleep on their rights do so at their own peril. Whether an appeals court sees enough reason to revisit the question remains to be seen.

The practical stakes beyond the courtroom are significant. OpenAI has been working toward a transition to a fully for-profit structure, and a live lawsuit from one of its co-founders challenging the legitimacy of that evolution was the kind of legal cloud that complicates those conversations considerably. As Fast Company noted, the verdict clears a meaningful obstacle on the path toward a potential IPO. Investors and partners interested in OpenAI's long-term trajectory will notice that the company's founding narrative is no longer subject to active litigation.

What the trial did not settle is the underlying philosophical argument Musk has been making in public for years. OpenAI began as a nonprofit precisely because its founders, Musk among them, believed the technology was too consequential to be steered purely by profit motives. The shift toward a commercial model and deep partnership with Microsoft is exactly what Musk claimed he feared. That argument has merit as a policy debate. It just wasn't, in the end, a viable legal claim given when he chose to make it.

Three weeks of courtroom proceedings did surface some texture about how OpenAI operates and how its relationship with Microsoft evolved, though any revelations were largely absorbed into the verdict's procedural conclusion rather than producing a definitive public accounting. Altman and Nadella testified about governance and investment structure; Musk testified about his own understanding of the organization's direction. None of it overcame the timeline problem.

For now, the score is OpenAI 1, Musk 0, with an appeal pending and the broader rivalry between the two men's AI ventures very much ongoing. Musk's xAI has been building out its Grok models and competing directly for enterprise customers and talent. The legal route having closed, at least for the moment, the competition shifts back to the product and market fronts where it was always going to be decided anyway.

Linus Torvalds announced Linux 7.1-rc4 last week with the usual assortment of hardware quirks and stability fixes, but buried in the release notes was a complaint that carries real weight for anyone who touches open-source security work: the Linux kernel security mailing list has become, in his words, "almost entirely unmanageable" because AI-assisted bug hunters are flooding it with duplicate reports generated by running the same tools over the same code.

The culprit, as Torvalds described it, is a combination of low friction and redundancy. Researchers using similar AI-powered scanning tools are independently finding the same issues and independently firing them off to the security list, apparently without checking whether anyone else has already done so. The result is a pile of near-identical reports that maintainers have to wade through before they can determine whether any of them describe something genuinely new and dangerous. Torvalds was pointed about where the problem lies: "AI tools are great, but only if they actually help, rather than cause unnecessary pain and pointless make-believe work."

This is not the first time he has flagged the pattern. XDA Developers noted that during the Linux 7.0 release-candidate cycle, Torvalds observed an unusual spike in relatively minor bug reports and suspected automated tooling was behind it. The 7.1-rc4 announcement suggests the problem has not resolved itself naturally.

The release candidate addresses the issue with something more formal than a complaint: new documentation, linked to patches from Willy Tarreau, that spells out what actually constitutes a Linux kernel security bug and offers guidance on responsible use of AI in bug discovery. The existence of that documentation is telling. When a project the size of the Linux kernel needs to publish a style guide for AI-assisted bug reporting, the volume has clearly crossed from nuisance into structural problem. The guidance is presumably aimed at the good-faith researchers who are generating noise without meaning to, not at bad actors, and framing it as documentation rather than a scolding is a reasonable way to handle a problem that is more about coordination failure than malice.

That framing also reflects how Torvalds tends to think about AI as a category. In remarks recapped by IT Home, he compared AI tools to compilers: useful instruments that free developers from lower-level grunt work without making developers themselves obsolete. The same logic applies here. A tool that surfaces a real bug is valuable. A tool that surfaces a real bug that thirty other researchers have already found and reported is producing noise, and noise has a cost that gets paid by the humans reading the list.

The rest of the 7.1-rc4 release is more routine. Phoronix described the week as busy on fixes, with Torvalds himself calling it larger than he would have liked. The build includes additional quirk entries for Intel and AMD laptops, a microphone fix for the Framework Laptop 13 Pro ahead of its release, and a round of kernel security patches that had already shipped in stable versions, including work connected to the ssh-keysign-pwn vulnerability and Dirty Frag.

On the AI-assisted development side of the ledger, Greg Kroah-Hartman continues to merge kernel fixes with help from his AI tooling, tracked in driver-core.git. That work represents the constructive end of AI involvement in kernel development: patches that go through the normal review process, land in the tree, and fix actual problems. The contrast with the security list situation is instructive. AI helping a trusted maintainer produce reviewed, mergeable patches is a workflow that fits the existing infrastructure. AI helping a large number of independent researchers generate uncoordinated reports aimed at a single inbox is a workflow that breaks it.

The practical implication for anyone doing AI-assisted security research on the kernel is fairly direct: check the existing reports before filing, and consider whether what the tool found is genuinely novel or a duplicate of something already in flight. The new documentation from Tarreau should give clearer criteria for making that call. The broader implication is that open-source projects operating at Linux's scale are going to need explicit policies around AI-generated contributions, not because AI assistance is inherently problematic, but because the tooling has lowered the cost of generating reports faster than norms around coordination have kept up.

Torvalds has spent decades managing the gap between what contributors can technically submit and what maintainers can realistically process. The AI-report deluge is a new version of a familiar tension, and the kernel project appears to be handling it the way it handles most things: by writing it down, adding it to the documentation, and moving on.

Google's new Gemini Intelligence feature for Android promises to pull together data from across a user's Google account, handle multi-step tasks on your behalf, and generally act as a proactive assistant rather than a reactive one. The catch: a lot of phones won't be getting it.

Announced at Google's Android Show on May 12, ahead of the Google I/O developer conference scheduled for May 19-20 in Mountain View, Gemini Intelligence is being positioned as something meaningfully different from the Gemini assistant already on Android devices. The distinction, in broad strokes, is that it doesn't just respond to questions but takes action across apps and surfaces without being asked. Think sending texts, managing calendar items, surfacing information before you go looking for it.

Hardware requirements are where the feature runs into friction. Business Standard and SamMobile both reported that strict compatibility thresholds could exclude a significant number of existing Android phones, including older premium models. Paying top dollar for a flagship last year is apparently no guarantee. For Samsung users specifically, SamMobile flagged that support for current Galaxy devices remains an open question.

Frandroid went further, suggesting that even some future phones might not qualify, depending on how those requirements shake out.

There is also a reasonable skepticism forming around what, precisely, is new here. A Reddit user in the Pixel community questioned the hype, pointing out that existing Gemini already appeared capable of app-based actions and proactive prompts. It's a fair question, and one Google will likely need to answer clearly at I/O.

Android Authority framed the feature in more existential terms, describing it as a peek at a future where the assistant handles so many tasks that users end up reviewing and approving AI actions rather than performing them directly. Whether that sounds like liberation or a slow handover of the wheel probably depends on the day.

HeraldCorp reported that Google's broader I/O message is expected to be less about any single product and more about building out a Gemini-centered ecosystem spanning phones, wearables, and vehicles. So Gemini Intelligence may be less a standalone announcement than the opening act for something larger.

Original source: https://kite.kagi.com/5b73205d-fb42-4ee3-87ba-854f2545e029/tech/4

Three of the most closely watched private companies in American tech are preparing to go public, and the numbers involved are almost cartoonishly large. SpaceX, OpenAI, and Anthropic are each targeting valuations near $1 trillion, with combined fundraising ambitions approaching $200 billion, according to reports from Tech Xplore, Malay Mail, and other outlets.

SpaceX is expected to move first, eyeing a June IPO and seeking to raise up to $80 billion on its own. To put that in perspective: all U.S. IPOs combined raised $70 billion in 2025. OpenAI and Anthropic are each pursuing roughly $60 billion in their own planned listings, with timelines pointing to late 2026 or 2027.

The three offerings together could nearly match the $240 billion raised by U.S. IPOs over the past four years. That is either a sign of extraordinary momentum or the kind of concentration that makes risk managers reach for antacids.

Emily Zheng of PitchBook flagged exactly that concern, noting that the value here is stacked in three mega-offerings rather than distributed across a healthier spread of companies coming to market. Wall Street is reportedly eager anyway, with investor appetite described as strong despite the risks.

The timing adds some wrinkles. Ongoing conflict in the Middle East is contributing to inflationary pressure and geopolitical uncertainty, according to the reports, which is not the backdrop any IPO roadshow team would choose. Markets absorb big offerings more comfortably when the macro picture is calm, and calm is not the current prevailing mood.

For the AI industry specifically, the OpenAI and Anthropic listings carry weight beyond the dollar figures. The News Lens described the two planned offerings as a key indicator for how public markets will value AI companies broadly, and for where the overall market is heading. If investors pile in at trillion-dollar valuations, it sets a reference point for every AI startup with public ambitions. If the offerings stumble, the ripple effects run in the other direction.

SpaceX, for its part, has the advantage of a more tangible business: rockets, satellites, and government contracts. The AI companies are asking investors to price in a future that is still being written, which is a harder sell in a jittery market, and a potentially very lucrative one if the thesis holds.

Original source: https://kite.kagi.com/5b73205d-fb42-4ee3-87ba-854f2545e029/tech/11