Working in isolation, especially for leaders, is rapidly becoming an outmoded idea. The modern era is defined by rapid technological advancements and increasingly complex, collaborative global challenges. In this environment, leadership can no longer be approached as an individual pursuit.

Instead, leadership must be a collaborative effort in which knowledge, responsibility, and innovation are continuously exchanged across teams, roles, and areas of expertise. Success depends on the ability to foster connection, leverage diverse perspectives, and work collectively toward shared outcomes.

The shift is especially important in science, technology, engineering, and mathematics fields.

IEEE is bringing together emerging professionals and established experts and leaders at the inaugural IEEE International Leadership Conference to address the need for cross-generational knowledge-sharing and to equip professionals with tools for collaborative leadership. Honoring Expertise, Accelerating Potential is the theme of the ILC, scheduled for 3 and 4 October in Budapest.

The conference is expected to focus on how leaders can share information across roles, adapt to rapid technological advancements, and build stronger, more connected professional communities. Through discussions, panels, and interactive sessions, attendees can examine how collaboration across experience levels and disciplines can strengthen decision-making and foment innovation.

“There are several factors driving this shift [in leadership], including accelerating technological development cycles, the need to build public trust, and the large percentage of the STEM workforce approaching retirement,” says Vickie Ozburn, conference cochair. “Progress in STEM now depends less on individual brilliance and more on the ability to transfer knowledge, adapt, and make decisions that integrate technical expertise with ethical and social considerations.”

From hierarchies to shared leadership

Instead of traditional corporate models rooted in hierarchy and individual advancement, a more dynamic framework is taking shape, one that views leadership as a shared ecosystem built on mentorship, continuous learning, and intentional knowledge transfer.

It means recognizing that professional development is no longer a one-directional flow of experience from senior professionals to newcomers. Instead, it thrives as a multidirectional exchange. When emerging professionals, mid-career managers, and seasoned experts including retirees are brought together, the result is not only richer dialogue but also more resilient and well-informed decision-making. A cross-generational dialogue enables organizations to honor what has worked, critically assess what has failed, and thoughtfully shape what needs to evolve.

Bridging experience to drive future leadership

Howard Wolfman, cochair of the IEEE ILC, underscores the importance of historical perspective in leadership development, invoking George Santayana’s enduring insight: “Those who cannot remember the past are condemned to repeat it.”

“In STEM especially, this principle carries significant weight,” says Wolfman, an IEEE life senior member and the founder and principal of Lumispec Consulting, in Northbrook, Ill. “Technological innovation doesn’t happen all of a sudden; it builds on decades of research, lessons learned, and accumulated knowledge. When leaders actively connect insights from across experience levels, they gain a more complete understanding of both opportunity and risk.”

That perspective reinforces the need for greater collaboration across roles and experience levels, ensuring that knowledge is not lost and is continuously built upon and applied in new ways. In this way, leadership development becomes a continuous, interconnected process rather than a series of isolated stages.

STEM careers are no longer defined by linear progression but by evolving contributions, in which each phase adds value to the field’s broader advancement.

What the changes mean for leaders today

Adopting a new leadership paradigm requires a shift in mindset across all levels. For senior leaders, success is defined not only by what they have built but also by the people they mentor and the knowledge they pass forward. Their legacy lies in enabling future leaders to succeed.

For emerging young professionals, innovation becomes more informed and impactful when it is grounded in historical context and informed by those who have already navigated similar challenges.

“Technological innovation doesn’t happen all of a sudden; it builds on decades of research, lessons learned, and accumulated knowledge. When leaders actively connect insights from across experience levels, they gain a more complete understanding of both opportunity and risk.”—Howard Wolfman, cochair of the IEEE International Leadership Conference

For organizations, cross-generational collaboration should be recognized as a strategic advantage, not merely an aspiration. Creating environments where knowledge flows freely and diverse perspectives are actively integrated is essential for long-term success.

The evolution reframes the distinction between management and leadership.

“A leader does the right thing, and a manager does things right,” Wolfman says. As the environment continues to shift, doing the right thing increasingly depends on drawing insights from across generations and experiences.

Building future-ready leadership pipelines

To build leadership pipelines capable of sustaining innovation and trust, organizations must begin asking more intentional questions:

• How do we create systems where knowledge sharing is continuous rather than episodic?
• How do we elevate emerging voices earlier in their careers?
• How do we ensure that experienced professionals remain engaged and valued contributors?
• How do we design leadership development as a collaborative, inclusive process rather than a competitive one?

Ultimately, leadership cannot be tied solely to titles or tenure. It is about contributing to a continuum in which each generation strengthens the next.

The IEEE ILC attendees are likely to leave the event with new insights and with a transformed perspective: Leadership is not about waiting for advancement or recognition; it is about engaging in an exchange of knowledge, responsibility, and vision, where the strength of the whole depends on the contributions of every generation.

Registration for the conference opens soon.

Some material also goes to an unnamed US technology and industrial company, under a deal penned in the first quarter of 2026.

In the same quarter a year ago, the largest portion of MP’s sales by revenue—mined material, not NdPr—went to China’s Shenghe Resources. But MP has stopped selling to Shenghe as part of its deal with the US government.

MP ultimately plans to produce its own magnets at scale, which would require it to consume much of what it produces. Mined rare earths are turned into oxides, which are used to make metals and alloys that go into magnets.

The company has penned agreements with General Motors and Apple to supply them with its magnets. It said in May that it expected to begin shipping finished magnets to GM this year.

Meanwhile, Energy Fuels—which won $725 million in conditional government funding in June—plans to scale its production of rare earths and also has eyes on Asia.

“We will be sending oxides in the near-term to Korea,” said chief executive Ross Bhappu. Last year, a major South Korean manufacturer made a small amount of Energy Fuels’ NdPr into magnets.

Energy Fuels is in the process of acquiring Australian Strategic Materials, which owns a rare earths metal-making plant in South Korea. It also announced a $1.9 billion deal to buy German magnet maker Vacuumschmelze (VAC) in June, which Bhappu said would result in more of Energy Fuels’ products going to VAC’s US operations.

China is the largest global producer of the widely used neodymium iron boron magnets. Outside China, Japan produces 10,000-15,000 tonnes per year, while South Korea produces 2,000-3,000 tonnes annually, and the US produces 1,000 tonnes or less, according to John Ormerod, a rare earths consultant at JOC LLC. There is also some production in Europe.

Phoenix, which secured a conditional $500 million from Washington in June, said government funding would help it scale up metal and oxide production, which would “expand the pie for everyone.”

MP’s recent earnings have been boosted by the money it receives under its US government deal—which guarantees a minimum sale price for some products and tops up any shortfall from the price paid by third parties.

© 2025 The Financial Times Ltd. All rights reserved. Not to be redistributed, copied, or modified in any way.

OpenAI on Wednesday launched GPT-Live, a pair of new voice models that fundamentally redesign how people talk to ChatGPT — replacing the company’s existing Advanced Voice Mode with an architecture that can listen and speak simultaneously, much like an actual human conversation.

The two models, GPT-Live-1 and GPT-Live-1 mini, are rolling out globally starting today across iOS, Android, and ChatGPT.com. GPT-Live-1 becomes the default voice model for paid ChatGPT users on the Go, Plus, and Pro tiers, while GPT-Live-1 mini serves free-tier users. OpenAI also plans to bring the models to the API, and developers can sign up to be notified.

The release marks the third generation of ChatGPT’s voice technology in roughly two years — and OpenAI’s clearest bid yet to turn its chatbot into something that feels less like querying a search engine and more like talking to a colleague.

Why full-duplex voice changes everything about talking to AI

The defining technical advance in GPT-Live is what OpenAI calls a “full-duplex architecture.” In telecommunications, full-duplex means both parties on a phone call can talk and listen at the same time. Applied to AI, it means the model continuously processes your incoming audio even while it generates its own spoken response — no more waiting for a clean silence gap to figure out when you’ve finished a thought.

“Instead of processing a sequence of separate messages, GPT-Live continuously processes input while generating output,” OpenAI wrote in its research blog. “The model can therefore make interaction decisions many times per second: whether to speak, continue listening, pause, interrupt, or invoke a tool.”

In practice, that translates to a voice assistant that can insert conversational acknowledgments — “mhmm,” “yeah,” “got it” — while you’re still talking, pick up on a natural pause without jumping in prematurely, and handle rapid interruptions without derailing the entire exchange. 

OpenAI’s previous Advanced Voice Mode, launched to paid users in September 2024, processed and generated audio within a single model but still operated on rigid turn-by-turn exchanges. As OpenAI acknowledged in the announcement, “because turn detection is based on silence, even a brief pause or background noise could be mistaken for the end of turn — causing the model to interrupt at unnatural times.”

That brittleness created a product that, while impressive in demos, could be deeply frustrating in extended real-world use. Background chatter in a coffee shop could trigger a response. A thinking pause might get swallowed. The experience felt, as one researcher put it on X shortly after the announcement, like “walkie-talkie turn taking.” GPT-Live is designed to end that era.

How OpenAI split voice and intelligence into two separate layers

GPT-Live introduces a second structural change that may prove just as consequential for enterprise adoption: it decouples the voice interaction layer from the reasoning layer.

When a user asks a straightforward question, GPT-Live handles it directly. But when the query demands web search, deeper reasoning, or more complex agentic work, GPT-Live delegates the task to a frontier model running in the background — at launch, GPT-5.5, the large language model OpenAI released in April — and continues talking with the user while the computation happens asynchronously.

“While it works, GPT-Live can keep talking with you and maintain the flow of conversation,” OpenAI explains. “As we release new frontier models, we’ll continuously update the model used by GPT-Live.”

This delegation model is a meaningful architectural bet. Rather than building a single monolithic voice model that tries to be both conversationally fluid and deeply intelligent, OpenAI has split the problem in two: a voice-native model optimized for real-time interaction, and a separate reasoning engine that can be swapped out as the state of the art improves. 

It is, in effect, a modular design — one that allows OpenAI to upgrade the intelligence of its voice assistant without retraining the voice model itself. The implications for enterprise and developer workflows are significant. A voice agent built on this architecture could maintain a natural conversation with a customer while simultaneously querying databases, searching the web, or performing multi-step reasoning — tasks that would have introduced several seconds of dead air under the old pipeline.

The three generations of ChatGPT voice, from clunky pipeline to continuous stream

To understand how far voice AI has come, it helps to trace the three generations that led to GPT-Live.

The original ChatGPT Voice, launched in 2023, used a cascaded pipeline — a speech-to-text model (Whisper) transcribed what you said, a large language model (GPT-4) generated a text response, and a text-to-speech model converted that response back into audio. Each handoff introduced latency and lost information. 

As OpenAI noted, “the complexity came at a cost: information could be lost across models, and responses were slow and stilted.” That cascaded approach was the industry standard, and its limitations were well-documented. As the blog OpenHelm noted in an October 2024 analysis of OpenAI’s Realtime API, the old pipeline stacked up to roughly 1,700 milliseconds of latency — nearly two full seconds of dead air before the first word of a response. Managing the state between the three separate APIs consumed an enormous amount of engineering effort.

OpenAI’s Advanced Voice Mode, which began its limited rollout to paid ChatGPT Plus users in July 2024 before expanding more broadly in September 2024, collapsed that three-model pipeline into a single model that processed audio natively. As TechCrunch reported at the time, the rollout came with five new voices — Arbor, Maple, Sol, Spruce, and Vale — alongside improved accent handling and smoother conversations. 

The feature also launched on the web in November 2024, extending it beyond mobile. But Advanced Voice Mode still operated through discrete, alternating turns — and it launched into the shadow of a PR debacle that OpenAI is still working to leave behind.

The Scarlett Johansson controversy still shadows OpenAI’s voice ambitions

Advanced Voice Mode arrived in the wake of one of OpenAI’s most damaging self-inflicted crises. During the GPT-4o launch in May 2024, the company showcased a voice called “Sky” that many listeners immediately noted sounded strikingly similar to Scarlett Johansson, who famously voiced an AI companion in the 2013 film Her.

Johansson said she had declined OpenAI CEO Sam Altman’s offer to voice the system, then was “shocked, angered and in disbelief” when the product launched with a voice her own friends couldn’t distinguish from hers, as NBC News reported. Altman had tweeted just the word “her” the day the product launched.

OpenAI pulled the voice and apologized, but the incident drew public scrutiny from SAG-AFTRA and members of Congress, and crystallized broader concerns about AI companies moving fast with creative IP.

The Hollywood labor union said the issue underscored “why we’re strongly championing federal legislation that would protect their voices and likenesses … from unauthorized digital replication,” as NBC News reported. Forbes contributor Paul Tassi wrote at the time that Altman, “by holding up Her on a pedestal of something to strive for, has missed the point of that film” — in which the protagonist’s relationship with his AI companion ultimately does him more harm than good.

GPT-Live appears designed, in part, to move past those controversies. OpenAI says it has “remastered the nine distinct voices in ChatGPT for GPT-Live” and notes the system “is designed for conversation, not voice impersonation,” with “safeguards to prevent it from imitating a real person’s voice.”

What 150 million weekly voice users will actually notice today

OpenAI disclosed that more than 150 million people talk to ChatGPT using voice and dictation features each week — a notable slice of the platform’s 900 million total weekly active users. The voice experience has grown into a substantial product in its own right, used for language practice, bedtime stories, commute-time chat, and hands-free everyday help.

The new product features reflect that usage. GPT-Live introduces rich visual cards that surface during voice conversations — weather forecasts, stock data, sports scores, and maps — giving users something to glance at without breaking the flow of speech.

Users can now choose between three reasoning levels for answers: Instant for quick responses, Medium for moderate thinking, and High for more complex work. And if you take a moment to think, “ChatGPT Voice now waits instead of jumping in and interrupting,” OpenAI wrote. “If you ask it to stay quiet and listen, it will. And when there’s background noise, like passing traffic or nearby conversations, ChatGPT is better at focusing on your voice instead of getting distracted.”

Early reactions from users with preview access were cautiously positive. “I had early access to sol. it is a phenomenal model,” wrote one user on X, adding it is “much better at frontend, long context knowledge work, and its vibes are much better.” Another observer cut to the heart of the matter: “The smarts are not new here, GPT-Live hands hard questions to GPT-5.5. What is new is the feel: full-duplex voice that listens while it talks.”

New voice-specific safety tests reveal where the risks still live

The GPT-Live system card, published alongside the announcement, reveals a safety strategy built around the particular risks of real-time voice interaction — a domain where the speed and intimacy of conversation create hazards that text-based chat does not.

OpenAI expanded its safety evaluations to include audio-native tests, using both real user voice samples (from those who opted in) and synthetically generated prompts targeting edge cases across categories like self-harm, sexual content, illicit behavior, emotional reliance, mental health, and hate speech.

On the synthetic evaluations — which OpenAI described as deliberately adversarial — GPT-Live-1 showed substantial improvements over Advanced Voice Mode. In illicit behavior, for instance, the safety score rose from 0.63 to 0.97. On self-harm, it climbed from 0.72 to 0.98. Hate speech achieved a perfect 1.00, up from 0.87.

On the production-prompt evaluations — which used real user audio and reflected more ambiguous, borderline scenarios — the picture was more mixed. GPT-Live-1 matched or improved on Advanced Voice Mode in most categories but showed a slight regression on emotional reliance (from 0.88 to 0.82), though OpenAI noted the change was not statistically significant.

The company built real-time safeguards that can intervene while the model is speaking — steering toward safer responses, surfacing crisis resources, or ending the voice conversation entirely in higher-risk situations. It also designed additional protections for teen users and adapted self-harm support flows for voice, including crisis helpline integration.

Perhaps most notably, OpenAI said it is “rolling out longer-term measurement and post-launch monitoring focused on emotional reliance” — an acknowledgment that the very naturalness GPT-Live strives for creates its own category of risk.

Google, ByteDance, and Nvidia are already in the full-duplex race

While OpenAI was refining its safety guardrails, its rivals were shipping full-duplex systems of their own. Google’s Gemini Live, which supports full-duplex conversation alongside camera and screen sharing — capabilities GPT-Live notably lacks at launch — is already available in the Gemini app. Google released Gemini 3.1 Flash Live in March as its highest-quality real-time audio model, targeting low-latency voice interactions for developers.

ByteDance launched Seeduplex in April, claiming to be the first production-scale full-duplex speech AI deployed at scale, inside its Doubao app. Seeduplex reported roughly a 50 percent reduction in false-response and false-interruption rates compared to ByteDance’s previous half-duplex system. And Nvidia’s PersonaPlex, released in January, brought customizable voice and role control to full-duplex models, breaking what had been a constraint where natural-sounding models were locked into a single fixed voice.

The competitive picture is clear: full-duplex voice interaction is quickly becoming table stakes for consumer AI products, not a differentiator. OpenAI’s advantage lies in the scale of its existing user base, its integration with GPT-5.5’s reasoning capabilities, and the breadth of the ChatGPT ecosystem.

But the window in which any one company has a monopoly on natural-sounding voice AI has already closed. OpenAI also acknowledged several gaps. GPT-Live does not support voice with video or screen sharing at launch. Language support is limited, with the company noting that “for certain languages, the model may have a non-native accent or gaps in fluency.” And API access is not available on day one, meaning enterprise developers cannot yet build on GPT-Live directly — a constraint that will slow the model’s penetration into commercial voice-agent workflows where competitors like Google, ElevenLabs, and Deepgram already have developer-facing products.

The end of the chat box may be closer than anyone expected

GPT-Live is essentially OpenAI’s most significant bet yet on voice as the primary interface for AI — not just a convenience feature bolted onto a text chatbot, but a purpose-built interaction layer that sits between the user and the company’s most powerful models.

“Over time, we believe this research will also unlock the ability to use voice for increasingly complex, longer-running, and more agentic work,” OpenAI wrote. That ambition — using natural voice as the front end for autonomous AI agents that can perform multi-step tasks — is the logical endpoint of the full-duplex plus delegation architecture.

Imagine telling your phone to book a flight, negotiate with your insurance company, or debug a production server, all through a conversation that feels as natural as talking to an assistant who also happens to have the intelligence of a frontier AI model.

Two years ago, talking to ChatGPT meant dictating into a microphone and waiting nearly two seconds for a stilted reply. One year ago, it meant a smoother exchange that still felt like a polite, slightly awkward phone call with someone who insisted on waiting for you to finish every sentence. Today, it means something closer to a real conversation — imperfect, still constrained in some languages and missing video, but unmistakably closer. OpenAI once got into trouble for wanting to recreate the movie Her. With GPT-Live, the company may finally be reckoning with the harder question the film actually posed: not whether AI can sound human enough to talk to, but what happens to us when it does.

SpaceXAI has released its latest model, Grok 4.5 — the first since the company went public several weeks ago.

In a blog post published Wednesday, SpaceXAI characterized its new release as a workhorse that can tackle all of the typical tasks that the AI industry has sought to automate: coding and app-building, office and clerical work, research, writing, and other forms of routine knowledge work.

Grok can supposedly do all this for less spend, too, as SpaceXAI says that its model has “twice greater token efficiency” than other leading models. If it carries through to real-world use cases, that efficiency would be a big advantage for SpaceXAI, since the cost of tokens has been a growing concern for AI consumers.

The company released benchmark metrics Wednesday that appeared to show Grok’s competitiveness with other top models from SpaceXAI competitors, although just short of best-in-class:

In a post on his social media platform X (which is a subsidiary of SpaceXAI), founder Elon Musk compared the model to Opus, Anthropic LLM designed for intensive and complex tasks.

“Based on strong positive feedback from customers in our beta test program, @SpaceXAI will make Grok 4.5 available to the public tomorrow. It is an Opus-class model, but faster, more token-efficient and lower cost,” wrote Musk in a post on X.

Musk later added: “Our internal assessment is that Grok 4.5 is roughly comparable to Opus 4.7, but much faster. The combination of capability, faster speed and lower cost is what makes it competitive.”

SpaceXAI says that its new model costs $2 per million input tokens and $6 per million output tokens. That’s quite competitive, if Grok’s capabilities match SpaceXAI’s rhetoric.

Opus 4.7, by comparison, costs $5 per million input tokens, and $25 per million output tokens. OpenAI has tiered costs for different model versions: Sol, its most expensive, costs $5 for input tokens and $30 for output, while its least expensive, Luna, costs $1 for input and $6 for output.

It’s a big week for AI model releases. OpenAI is planning to release GPT 5.6, its latest, most powerful model, on Thursday. The release of that model had previously been limited by the Trump administration, due to concerns about its security implications. OpenAI has called it its “strongest model yet.”

A China-linked threat cluster has been exploiting vulnerable Roundcube servers at U.S. and Canadian universities to steal credentials and deploy backdoor malware.

The campaign has been observed since May and focuses on physics and engineering departments, administrators and professors, as well as organizations involved in astrophysics, particle physics, or national security-related research.

Researchers at cybersecurity company Proofpoint are tracking the activity under the name ‘UNK_MassTraction’ and believe to be associated with a new threat cluster.

The attack begins with a malicious email sent from compromised accounts or spoofed domains, using a generic lure.

Opening the email in a vulnerable Roundcube webmail client triggers exploitation of a cross-site scripting flaw tracked as CVE-2024-42009, which executes JavaScript code inside the victim’s browser, loading a payload called IceCube.

According to the researchers, IceCube “is a fully-featured Roundcube stealer” that can harvest usernames, passwords, cookies, two-factor authentication (2FA) data, and browser information.

Proofpoint says that the malware uses “helpers” to exploit a Roundcube deserialization flaw tracked as CVE-2025-49113 and attempts to install SquareShell, a PHP webshell that includes remote code execution capabilities.

If successful, the attacker gains remote code execution on the mail server; otherwise, the malware downloads a shell script that loads another payload, VShell, directly in memory.

VShell is a commodity Go-based backdoor that supports interactive shell access and port forwarding, which is commonly used by Chinese threat actors.

Based on several observations, Proofpoint assesses that UNK_MassTraction is likely a China-aligned espionage actor.

First, the infrastructure used in the attacks overlaps with a covert VPS network previously associated with multiple China-linked actors. Another clue is the presence of Chinese-language artifacts in earlier phishing emails.

Finally, the tactic of targeting internet-facing mail servers as a foothold for accessing internal networks is a hallmark of Chinese attacks.

Taking everything into account, Proofpoint emphasizes that attribution in this case is just an assessment and definitely not a high-confidence one.

An interesting finding regarding the specific targeting of this campaign is that UNK_MassTraction appears to have selected servers previously deemed vulnerable to CVE-2024-42009 and CVE-2025-49113, so some reconnaissance was performed prior to the attacks.

Administrators of Roundcube systems are advised to apply the latest security updates that address the two flaws and treat mail servers with the same diligence they show for VPNs and other remote access nodes.

Security teams log 54% of successful attacks and alert on just 14%. The rest move through your environment unseen.

The Picus whitepaper shows how breach and attack simulation tests your SIEM and EDR rules so threats stop slipping by detection.

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X’s crowdsourced fact-checking system, Community Notes, will be updated to send users direct messages alerting them whenever a post they have interacted with has received a correction. The change, which is not yet live, was announced by X owner Elon Musk. He did not share a time frame for its launch.

The update attempts to address one of the bigger criticisms about Community Notes — that corrections arrive too late to matter. A misleading post can accumulate views and reposts while its accuracy is disputed, and by the time it’s corrected, the damage has been done. By proactively notifying users when a post receives a correction, X is trying to extend the reach of the note beyond the original post. This could also allow users who spread false information to issue their own mea culpa, if they had been duped.

X’s Community Notes system was first established when the company was still known as Twitter, before Musk’s acquisition.

The idea was to introduce a different way to address misinformation on the platform, rather than require Twitter (now X) to be the centralized authority for moderation decisions. Instead, Community Notes contributors could suggest corrections and add critical details or missing information to posts. Consensus is achieved when people who rate the note as helpful are those who typically have different perspectives, and the note goes live.

A similar system has since been adopted by Meta as part of its broader moderation overhaul last year, which saw the company eliminate its partnerships with fact-checkers.

Though Community Notes makes sense for a company that wants to distance itself from the business of fact-checking, it’s also proven difficult to scale. A 2025 study of the feature by Spanish fact-checking site Maldita found that 85% of the proposed notes on X remain invisible to users, and only 8.3% get published and become visible. A separate study conducted by the Digital Democracy Institute of the Americas (DDIA), which encompassed 1.76 million notes published on X between January 2021 and March 2025, put the figure for unpublished notes even higher at 90%.

This weakens Community Notes as a system that surfaces information when it’s most needed, critics have pointed out. Plus, they’ve argued, people aren’t aware when a post they saw or boosted receives a correction later on, as there’s been no way to bring that information to their attention.

Musk’s proposal to send users alerts via X Chat (DMs) would address the latter issue, at least, assuming it goes live. X was asked for comment, but a response was not immediately available.

A Paris startup wants to loosen Nvidia’s grip on AI, not with a new chip, but with software. ZML has released a free tool that runs open-source models fast across Nvidia, AMD, Google, Apple and Intel silicon alike.

Nvidia still rules AI hardware, but its walls keep thinning. ZML, a Paris startup backed by AI pioneer Yann LeCun, has released free software that runs open-source language models across a mix of chips, TechCrunch reports. The list spans five targets: Nvidia, AMD, Google’s TPUs, Intel and Apple.

The tool, ZML/LLMD, is an inference server. Inference means running a trained model to answer prompts, the part of AI that now eats most of the compute. Founder Steeve Morin says the goal is to break the silos that lock users to one vendor, and to squeeze each chip to its top speed.

Why a mix of chips matters

Cost is the driver. As AI bills climb, enterprises and clouds want the freedom to pick cheaper or less power-hungry silicon for a given job. “The idea is to give people back the power to create their own system,” Morin said. Do that well, and it reads less like a feature and more like a wedge under Nvidia’s moat.

It could also lift a wave of novel chipmakers, many of them European. Morin name-checked Axelera, Fractile, Kalray, SiPearl, VSORA and others. Software that treats their chips as first-class, not second-best, gives buyers a real reason to try them.

A crowded, costly race

Morin does not write off Nvidia, and says ZML has a good relationship with the chip giant. But the field is crowded. The “inference gold rush” has minted rivals like Baseten, recently valued at $13bn, plus the teams behind the open-source projects vLLM and SGLang. All chase the same prize: making AI cheaper to run.

Morin thinks ZML reaches further. “We have reached the point where we are co-designing silicon,” he said. His lean team of 20 has shipped fast, with more releases to come.

Why it matters

LLMD ships free for now to gather usage, not yet a paid product. Its unusual root is the bigger signal. A tool built to loosen Nvidia’s grip and to back Europe’s own AI stack landed from Paris, not Silicon Valley. Morin, who raised $20m from investors including Xavier Niel’s Kima Ventures, put it plainly. “I couldn’t do ZML anywhere but in Paris,” he said.

Virtualization

CA and VMware both suing insurance giant

Broadcom has accused Allstate Insurance of dodging a software license audit that the insurer claims only happened after it decided to stop using VMware and CA software.

Those two Broadcom business units – CA and VMware – have brought copyright infringement lawsuits against Allstate.

The CA suit, filed in May 2025, alleges that the insurer breached contracts after the sale of its Employer Voluntary Benefits business to an outfit called StanCorp. The VMware suit, filed in December 2025, alleges that Allstate didn’t comply with contract terms that required it to participate in license audits.

Software license audits are not unusual. Vendors routinely include the right to conduct audits in their contracts, and those rights can extend beyond the term of a license so that software companies can be paid for all use of code under a time-limited contract. Some vendors, however, are known to audit more often and more vigorously than others, or to use audits to gain leverage during license renewal negotiations.

Allstate claims Broadcom’s decision to audit it was not entirely reasonable.

“This case is about VMware’s decision to initiate a haphazard ‘audit’ of Allstate, once it was aware that Allstate did not intend to renew its contracts with VMware or its sister company, CA,” the company stated in a June 12 filing.

That accusation came after months of conflict.

An Allstate filing in the CA matter claims that Broadcom launched four audits, covering “Tanzu,” “VMWare,” “Agile Operations” and “Mainframe.” Broadcom advised of its intent to audit around April 2025.

Broadcom alleges Allstate didn’t co-operate with the audits. “Throughout August and September 2025, VMware sent weekly follow‑ups. Allstate continued to stonewall and withheld the requested materials,” according to VMware’s claim.

Allstate says it simply didn’t have the resources to respond to four simultaneous probes.

One of the tools Broadcom uses during software audits is a set of scripts that detect software installations. Allstate acknowledges it received the scripts and other audit material.

Then on September 12, Broadcom alleges, Allstate dropped a bombshell: It had “removed VMware from all devices.” On October 1, the insurance giant apparently told the virtualization pioneer “all VMWare instances have been terminated and removed” – at least from an environment governed by an enterprise license agreement.

After terminating its VMware estate, Allstate said Broadcom’s audit scripts wouldn’t work. The insurer nevertheless completed an audit questionnaire, but Broadcom said the info in that document was “woefully incomplete.”

Both cases continue and, on June 12, Allstate filed a document that offered its view of the matter – and includes the allegation that Broadcom only ordered its audits once it realized Allstate was binning VMware and CA software.

Allstate also accuses Broadcom of making “vague, competing, and contradictory demands of Allstate, often in direct violation of its contractual agreements.”

Broadcom and Allstate tried alternative dispute resolution in both matters but have not found common ground. Courts have proposed the two matters adopt the same timeline, which will see Dispositive Motions – an attempt to resolve a case before a full trial – take place no later than May 17, 2027.

The Register has asked Allstate why it decided to stop using Broadcom software and if it has replaced it. We’ve not heard back at the time of writing.

However we understand that the relationship between Allstate and Broadcom has not been good for quite some time, and that the insurer decided to move away from both VMware and CA at around the time Broadcom’s acquisition of VMware closed.

VMware points to major clients such as the London Stock Exchange and Nationwide Bank as evidence big corporate entities trust it with their private clouds, and therefore the heart of the IT estate that powers their business and enables innovation. And this week, AWS also showed confidence in VMware by adding support for version 9.x of its Cloud Foundation suite.

However, The Reg has also learned of several big users quitting VMware – including T-Mobile, Tesco, and Western Union – sometimes under acrimonious circumstances. ®

Google Photos is getting a new “Video Remix” feature that can edit and transform videos in seconds, Google announced on Wednesday. The feature is powered by Gemini Omni, Google’s recently released model that promises to “create anything from any input.”

The launch is Google’s latest push to bring more generative AI tools into its consumer apps as it continues to compete with companies like Apple, OpenAI, and Adobe. By baking AI-powered video editing into Google Photos, the tech giant is making it easier for users to edit clips with a few taps instead of relying on dedicated software, giving users another reason to stay within Google’s ecosystem.

The Video Remix tool can be accessed in the “Create” tab in Google Photos, allowing you to do things like apply cinematic relighting to brighten up a dark clip, swap out a plain background for something else, or add artistic styles to videos, such as watercolor, raw sketchbook, and oil painting effects.

For example, you could edit a video to make it appear that you shot it in a greenhouse, relight a video with a morning glow, or paint a video in a watercolor effect.

“Creating beautiful video clips shouldn’t require professional skills or hours of editing,” Google wrote in a blog post. “Now, with Video Remix in Google Photos, you can transform ordinary videos into share-worthy moments in just a few taps.”

Video Remix starts rolling out today to eligible Google AI Plus, Pro, and Ultra subscribers in the U.S., Argentina, Bangladesh, Brazil, Colombia, Egypt, India, Indonesia, Japan, Mexico, Pakistan, Philippines, South Korea, and Turkey.

The feature is the latest in a series of AI-powered updates introduced to Google Photos. The app recently launched new touch-up tools to allow users to apply subtle edits and fixes, such as removing blemishes, refining skin texture, brightening eyes, and whitening teeth. Google also announced an AI-powered feature that turns photos of your clothes into a digital closet where you can create new outfit ideas and virtually try on outfits.