The capabilities of the Kimi K3, developed by China's Moonshot lab and recognized as the largest open-weight large language model (LLM), have sparked a contentious discussion that intertwines the potential economic impacts of leading American AI firms with the future trajectory of LLM technology.

Dean W. Ball, OpenAI's head of strategic futures, controversially suggested that the U.S. government should find a way to instill regulatory fear and skepticism towards these new models, arguing that open-weight versions could deter investments in cutting-edge laboratories. This claim prompted backlash from industry figures such as Yann LeCun and Martin Casado, who contended that open-source software can foster innovation while coexisting with proprietary technologies. Shortly after, Ball backpedaled on his stance that a regulatory offensive should be the White House's "best strategy" and acknowledged that open-weight models do not inherently impede technological advancement.

According to Axios, the Trump administration is contemplating a ban on K3 and similar advanced Chinese models, reportedly at the urging of U.S. tech companies. However, a Politico report indicated that the Department of Commerce does not plan to act on such a ban in the immediate future.

For major AI corporations, the advantages of open-weight models are evident. These models, which can operate on independent infrastructures or within large organizations, provide a less expensive alternative compared to leading models from companies like Anthropic and OpenAI. If users increasingly choose to operate outside of proprietary systems, these organizations could see diminished returns on their substantial investments in model training.

Braden Hancock, co-founder of Snorkel AI and a research partner at the Laude Institute, articulated this concern, emphasizing that robust open-source models could tighten profit margins and reduce the pricing power of major companies. Yet, he noted that this shift would likely lead to increased AI usage rather than decreased.

This growing trend does not present a challenge for stakeholders lacking investments in companies like Anthropic and OpenAI; AI development is poised to continue expanding. This raises the question of why the government might hinder consumer access to these technologies in a market characterized by its supposed freedom.

Concerns regarding Chinese AI models vary. One worry focuses on the potential for U.S. data to be accessed by the Chinese government; similar fears led to a ban on the importation of modern Chinese electric vehicles due to data privacy issues. However, experts generally believe that open-weight models hosted on U.S. servers are unlikely to result in data leaks back to China, despite some risks remaining.

Another worry is that these models may carry biases favoring the People's Republic of China, yet how this would manifest in tasks such as coding remains uncertain.

A third concern posits that Chinese models might lack the safety measures mandated by U.S. regulations, which seek to prevent abuses of AI, such as exploiting closed computer systems or assisting in weapon development. Interestingly, these regulatory constraints may leave U.S. firms more vulnerable; David Sacks, a venture capitalist and advisor to Trump, has noted instances where American companies turn to Chinese LLMs to address security challenges that U.S. models cannot meet.

Underlying these concerns is the fear that the U.S. could fall behind China in AI development if domestic research slows. Sam Bresnick, a research fellow at Georgetown’s Center for Security and Emerging Technology, argues that the increasing significance of AI in military operations justifies ongoing investment in frontier labs. He questions why U.S. government resources should be directed to shielding American firms from competition that is excluded from the U.S. market based on national origin.

Proponents of open AI criticize what they perceive as a false dichotomy crafted by leading companies, pitting innovation against proprietary systems. Hancock remarked that the main impact of open-source models from China isn't necessarily about backdoor threats, but rather about the potential for Chinese innovation to dominate the landscape. He pointed out the success of PyTorch as a case study; it became the industry standard because of its open-source nature, allowing community contributions to drive its growth over competing libraries.

Hancock and others express concerns that Chinese LLMs could become the focal point of global research efforts. Currently, many U.S. academic programs rely heavily on Chinese open-weight models, with Hancock noting that a substantial portion of the academic literature students engage with originates from Chinese institutions, while American frontier labs become increasingly secretive.

Clem Delangue, CEO of Hugging Face, a platform for open AI collaboration, argued that restricting access to open models would not enhance safety but would merely conceal risks, centralize power among a few entities, and hinder broader participation in creating safer and more beneficial AI technologies.

According to Bresnick, a more effective method for maintaining U.S. competitiveness would involve restricting chip exports, particularly Nvidia's H200 processors to China, potentially obviating the contentious debate surrounding bans on open-source technologies that many American companies wish to adopt.

The complexities of AI economics contribute to the ongoing dilemma. Both open-source and proprietary business models are still being refined as companies grapple with profitability, especially with rising training costs.

These economic challenges are mirrored in China, where AI enterprises are also striving to secure revenue and computational resources while the government encourages the release of open models for policy reasons, notwithstanding the difficulties in monetizing them.

Some U.S. businesses, like Thinking Machines Lab and Nvidia, aim to leverage open model releases as a business strategy. Hancock highlighted that Nvidia benefits from fostering a multitude of AI companies rather than being reliant on a handful financially robust firms capable of manufacturing their own chips, which is part of the rationale behind its investment in Nemotron, an array of open models.

Bresnick concluded that the U.S. would greatly benefit from developing its own competitively priced, highly capable open models. However, this goal often conflicts with the current strategies employed by frontier labs.

Should the US be concerned about OpenAI's fear of open-weight models?

A dark, shadowy underground lab corridor dominated by a heavy vault door marked in plain “OpenAI” text, its seams cracked open. From within, tendrils of glowing red code labeled “Open-Weight Models” spill out like smoke, slithering across the floor. In the foreground, a solitary U.S. flag on a metal pole stands rigid in the gloom, its stripes flickering in the low, warm spotlight. Deep shadows and muted highlights create a tense, ominous atmosphere—no artificial blue tones.

OpenAI is scared of open-weight models. Should the US be? - TechCrunch

- The launch of China's Kimi K3 model raises concerns about US competitiveness and regulation.  
- OpenAI's Dean W. Ball suggests creating regulatory fear around open-weight AI models in the US.  
- Advocates argue open-source AI models could enhance innovation and counterbalance reliance on proprietary systems.  

The impressive capabilities of Chinese lab Moonshot’s Kimi K3, the biggest open-weight large language model, has kicked off a debate that conflates two things: the economic possibilities of American AI giants and the future of LLMs as a technology.

OpenAI’s head of strategic futures, Dean W. Ball, went so far as to argue that the US government should find a pretext to create regulatory fear, uncertainty, and distrust around the new models, since open-weight models must necessarily deter capital spending by the frontier labs.

People freaked out, with tech luminaries like Yann LeCun and Martin Casado arguing that open software can accelerate innovation and coexist with proprietary projects. Ball soon retracted his claims that a regulatory crackdown was the White House’s “best strategy” and that open-weight models necessarily slow down advances in the technology.

However, Axios reports that the Trump administration is considering banning K3 and other advanced Chinese models at the behest of American frontier labs. Another report from Politico said that the Department of Commerce would not take that step anytime soon.

The benefit for major AI companies is clear: Open-weight models, running on independent infrastructure or inside major enterprises, offers cheaper intelligence than Anthropic or OpenAI’s class-leading models. If users increasingly spend more outside the closed labs, that means smaller return on their massive investments in model training.

That view extends far beyond OpenAI. “Strong, frontier-caliber open source models will place a squeeze on the margins and will bring down the prices of the frontier companies,” Braden Hancock, the co-founder of Snorkel AI and a research partner at the Laude Institute, told TechCrunch. “It will not necessarily mean that the amount of AI usage goes down a little bit. You know, obviously, quite the opposite.”

That’s not a problem for people without shares in Anthropic and OpenAI. AI will still proliferate. So what’s the justification for the government to block Americans from purchasing something in our ostensibly free markets?

Concerns over Chinese models come in several flavors. One is protecting US data from the Chinese government; the US banned the import of modern Chinese EVs over concerns about their data gathering. But experts tend to think that open-weight models run on US servers are unlikely to leak data back to China, although it’s not impossible that such a thing could be done.

Another is that the models may have implicit bias toward the PRC — but it’s not clear what that might mean for, say, coding tasks.

A third common worry is that Chinese models lack the guardrails that the US government has mandated (through an opaque process), which aim to prevent leading US LLMs from being used to exploit closed computer systems or create weapons. However, those same guardrails may make US companies more vulnerable: David Sacks, the venture capitalist and Trump adviser, has been sharing cases of US companies turning to Chinese LLMs to close security gaps when US frontier models refuse to do the tasks.

But the most significant motivation for restricting the models is that fear that China will be able to outpace the US if the frontier labs slow down.

Sam Bresnick, a China-focused research fellow at Georgetown’s Center for Security and Emerging Technology, says the growing importance of AI to the US military operations gives the US a reason to support continued investment in AI at the frontier labs. But the whole question, he says, is fraught.

“Why should the weight of the U.S. government be aimed at protecting these these companies from competitors that are being locked out from the U.S. market based on their origins?” Bresnick asks.

Advocates for open AI say that the frontier companies are creating a false binary between innovation and closed models.

“The bigger impact of having these open source models come from China is less that they’re sneaking in back doors, and more that they are owning the innovation,” Hancock told TechCrunch. “You end up with, effectively, an expanded workforce on your model. PyTorch became the industry standard because it was open source, and so the whole community could contribute to it rather than just one company, and it grew and grew, and all the rest of the deep learning libraries kind of died in comparison.”

Hancock and other advocates fear that Chinese LLMs will become the locus of international research. Already, US graduate programs mainly build on open-weight Chinese models, and Hancock says that half of the papers students study are coming from Chinese institutions, with American frontier labs increasingly reticent about sharing their work widely.

“Restricting open models wouldn’t make AI safer,” said Clem Delangue, the CEO of Hugging Face, a platform for open AI collaboration. “It would simply hide the risks, concentrate power in the hands of a few and make it harder for the next generation of builders, researchers, academia, non-profits, governments to participate in making AI safer and more beneficial for all.”

Bresnick says that the real way to slow China would be to focus more on chip export controls. A better way to preserve US AI leadership would be to stop selling Nvidia H200 processors to China. “That,” he says, “could potentially keep us out of this thorny debate about banning open source technologies that huge numbers of US companies want to use.”

Part of the problem is that uncertainty around AI economics. “The open business model, the proprietary business model — neither one is figured out. AI companies are struggling to figure out how to make money on their tools, especially as training costs need to go up and up,” Bresnick points out.

The same challenges that play out in the US are also playing out in China, where AI companies are also struggling to generate revenue and access compute power, and the government is seen as encouraging open releases for policy reasons despite the challenge in capitalizing on them.

Some US companies, including Thinking Machines Lab and Nvidia, are trying to make a business around releasing open models. Hancock points out that Nvidia would do better “if there are dozens or hundreds of companies building AI than rather than two or three that are well capitalized enough to make their own chips,” which is one reason behind its investment in Nemotron, a collection of open models.

“The main point is the U.S. would be very well served to have its own very capable, much less expensive open models,” Bresnick said. “It just clashes with the approach the frontier labs have taken.”

With additional reporting from Rebecca Bellan.

OpenAI is scared of open-weight models. Should the US be?

As discussions around artificial intelligence (AI) gain traction on Wall Street, technology firms are still promoting the idea that AI will soon transform into an extraordinary revenue generator. A prime example is OpenAI, which is projecting an astonishing $100 billion in annual ad revenue by 2030, despite currently grappling with the challenge of even reaching $1 billion.

This substantial disconnect was highlighted in a recent report by the marketing analytics agency Emarketer, which was first brought to attention by AdWeek. The analysis indicates that OpenAI is on track to fall short of its five-year ad revenue estimates by an astonishing 90 percent. Moreover, Emarketer has made a sobering assessment, calculating that the overall potential market for chatbot advertising is capped at only $5.4 billion.

This revelation poses problems not just for OpenAI but for major tech competitors that are also eager to capture a slice of the AI advertising landscape. According to Emarketer, by 2026, the combined advertising revenue from OpenAI, Microsoft, Google, and Amazon is projected to remain below $1 billion. In contrast, OpenAI had anticipated that its own ad revenue would reach $2.5 billion by the end of this year.

For investors, these findings raise critical questions. If leading AI enterprises, having invested over $1.6 trillion into developing AI technology, are unlikely to meet even OpenAI's modest 2026 revenue expectation, what are the chances they can achieve their ambitious projections in the upcoming years?

To reach its projected figures, AdWeek notes that OpenAI would need three significant developments to occur simultaneously. First, advertisers would have to entirely rethink their established strategies that rely on search engines and social media, reallocating their ad budgets towards chatbots instead. Next, OpenAI would need to gain an upper hand over established ad-selling titans like Google and Meta, all while witnessing a dramatic increase in the AI advertising market from a modest six-figure income in 2026 to a booming 12-figure revenue stream by 2030.

In essence, OpenAI needs to create a remarkable narrative to substantiate these forecasts. This is critical, considering the company itself forecasts that advertising will account for 36 percent of its total revenue by 2030. Should OpenAI fail to realize these projections, it could unravel its five-year financial vision, challenging the very foundations of one of the largest financial bubbles seen in recent history.

OpenAI Seems to Fall Well Short of Its Sales Targets

A moody, dimly lit corporate war room at night: an executive in a sharp suit seen from behind, shoulders tense as he stares at a giant wall-sized digital chart plunging far below projected sales targets. Scattered across a long table are crumpled spreadsheets stamped “Q3 Goals,” a half-empty coffee mug, and a tablet glowing with red downward arrows. The word “OpenAI” is etched in soft white light on a brushed-metal panel behind him. Shadows loom in the corners, emphasizing the weight of missed targets.

OpenAI Appears to Be Missing Its Sales Goals by a Vast Margin - Futurism

- OpenAI aims to earn $100 billion annually from ads by 2030, struggling currently to reach $1 billion.  
- Emarketer estimates the total chatbot advertising market at only $5.4 billion, hindering OpenAI's projections.  
- OpenAI's advertising revenue needs three miracles to meet projections, challenging its future profitability.  

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Even as the AI bubble becomes a mainstream talking point on Wall Street, tech companies continue to peddle the fantasy that AI is poised to become an almost magical money-maker. Case in point, OpenAI wants you to believe that by 2030, it’ll be raking in $100 billion a year just from ads alone — even though it’s currently struggling to reach just $1 billion.

That massive gulf was observed in a new analysis from marketing consulting firm Emarketer, first flagged by AdWeek, which found OpenAI is on pace to undershoot its own five-year ad revenue projections by a whopping 90 percent. In fact, Emarketer’s take is even more devastating than that: it estimates that the entire addressable market for chatbot advertising — the maximum amount of money up for grabs overall — at $5.4 billion.

That figure isn’t just bad news for OpenAI, but for every giant tech company, all of which are jockeying for a piece of the AI ad pie. In 2026, Emarketer projects that the combination of OpenAI, Microsoft, Google, and Amazon will bring in under $1 billion in ad revenue. For context, OpenAI had projected that its AI ad revenue alone would hit $2.5 billion by the end of this year.

It all adds up to a difficult question for investors. If the top AI companies — which have burned over $1.6 trillion building AI so far — can’t even hit OpenAI’s meager projection for 2026, what hope do they have of hitting their projections four years from now?

To hit that number, AdWeek observes, OpenAI needs three miracles to happen all at once. First, advertisers have to abandon decades worth of infrastructure built around search engines and social media and put all their advertising budgets into chatbots. Once that happens, OpenAI has to out-muscle previous ad-sales giants like Google and Meta, while the entire AI-ad market balloons from just a six-figure stream in 2026 to a raging, 12-figure river by 2030.

Basically, OpenAI will have to make a lot of history to justify these numbers. And whether it can do so is no small matter: according to OpenAI’s own forecasts, advertising is supposed to make up 36 percent of the company’s total revenue by 2030. If the AI lab can’t make the math work, the company’s five-year financial story falls apart, and with it, a major bullet point justifying one of the largest financial bubbles the world has ever seen.

More on OpenAI: Lawsuit Alleges That ChatGPT Encouraged Suicide of Woman Who Walked Into Traffic

OpenAI Appears to Be Missing Its Sales Goals by a Vast Margin

Last week, the tech world was taken by surprise when a new large language model from China, named Kimi K3 and created by Beijing's Moonshot AI, made its debut. This open-weight model showcased impressive benchmark results, rivaling some of the leading closed-weight models from established companies like OpenAI and Anthropic—at a significantly lower cost.

Similar to how DeepSeek disrupted Silicon Valley in early 2025 with its AI offerings, Kimi K3 caused notable ripples in the market. The tech-heavy Nasdaq experienced a major sell-off, and the S&P 500 also faced declines following the model's announcement.

For prominent closed-weight AI organizations, this situation presents a considerable challenge. Executives are increasingly concerned about rising expenses and are actively searching for more affordable options. Kimi K3 could potentially attract their interest, complicating the efforts of companies such as OpenAI and Anthropic to draw in new clients, particularly at a time when they need to offset their high operational costs.

Dean Ball, OpenAI’s head of strategic futures who recently joined the company, expressed his concerns in a lengthy and contentious tweet. He accused the Chinese government of irresponsibility for permitting such a powerful model as Kimi K3 to be open sourced, citing the inherent risks involved.

Ball also contended that “open-weight models are inherently decelerationist,” a claim that ignited a fierce discussion in response. Within the context of AI, accelerationism pushes for rapid technological progress to create a new societal structure, while decelerationism advocates a more cautious approach due to potential dangers.

According to Ball, open-weight models may hinder advancements in AI by discouraging funding for further development. He suggested that the Trump administration would likely implement strict regulations regarding the use of open-weight models from China, which could induce enough anxiety—often referred to as “FUD”—to dissuade major players from integrating Chinese AI solutions.

Despite his obvious position of interest as an OpenAI executive, observers on social media reacted skeptically to Ball's assertions. One user remarked, “The head of strategic futures at OpenAI seems a little rattled.” Alexander Green, founder of AI firm Littlebird, vehemently disagreed with Ball's perspective, labeling his views as irrational.

Ball's defensive reaction is understandable, given OpenAI's struggles to keep pace with its rivals, as many AI startups require significant funding to manage their soaring expenses. The emergence of competitive models like Kimi K3 poses a substantial threat without regulatory measures.

An AI agency, Lumien, critiqued Ball’s initial post, suggesting it resembled more a geopolitical commentary suitable for a think tank blog rather than a well-considered opinion from someone just starting at a company that stands to gain the most from regulatory support.

Trump's AI advisor, David Sacks, dismissed Ball's ideas about potential government intervention as either a sign of a regulatory capture strategy or an ill-founded prediction. He criticized the usage of regulatory uncertainty as a means of competition, stating, “The leading closed labs, already dominating AI model revenue, seek to eliminate their open-source rivals.”

Sacks further emphasized that the Silicon Valley ecosystem—which predominantly champions open competition—must respond to the maneuverings of these top labs.

Other officials were less tactful in their criticism of Ball, with US defense undersecretary Emil Michael labeling him the “supreme village idiot” of the AI industry—indicative of the heated discussions surrounding this issue.

OpenAI Executive Expresses Concern That China's Advanced Models Will Undermine Competition for For-Profit Firms.

A dimly lit high-tech boardroom in warm, shadowy tones. A lone business executive’s silhouette sits at a long table under a single overhead spotlight, head in hands. Across the room, a traditional red pavilion draped with a large Chinese flag glows ominously as workers hand out glowing AI module crates marked “FREE.” Behind the executive, a wall-sized screen displays the text “OpenAI” in stark white. The scene is tense and foreboding, no blue AI lighting.

OpenAI Exec Laments That China Is Giving Away Models So Good That For-Profit Companies Won't Be Able to Compete - Futurism

- The Chinese model Kimi K3 challenges top AI firms like OpenAI at lower costs.  
- OpenAI’s Dean Ball expresses concerns over the risks of open-weight models in AI.  
- Ball's controversial comments spark debate about regulatory strategies in the competitive AI landscape.  

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Last week, a Chinese-made large language model called Kimi K3, developed by Beijing-based firm Moonshot AI, burst onto the scene.

The open-weight model impressed with early benchmark results, trading blows with some of the most advanced closed-weight ones being developed by the likes of OpenAI and Anthropic — and at a fraction of the cost.

Much like Chinese competitor DeepSeek’s AI model upending Silicon Valley in early 2025, Kimi-K3 sent shockwaves across the industry. A major sell-off roiled the tech-heavy Nasdaq, with the S&P 500 slipping after the unveiling.

For top closed-weight AI labs, it was the perfect storm. Executives are already balking at rapidly rising costs and desperately looking for cheaper alternatives. Kimi K3 may end up luring them in, making it even harder for the likes of OpenAI and Anthropic to attract new customers — right as they need to cover at least some of their exorbitant costs.

OpenAI’s head of strategic futures, Dean Ball, who joined the Sam Altman-led company two weeks ago after helping shape AI policy for the Trump administration, was shaken by what he saw. In a lengthy and controversial tweet on Friday, Ball accused the Chinese state of acting recklessly by allowing models as powerful as Kimi K3 to be open sourced “given potential risks.”

Ball also argued that “open-weight models are inherently decelerationist,” a claim that sparked a raging debate in the comments. In the context of AI, accelerationism advocates for faster AI progress to establish a new world order. Decelerationism, by contrast, argues that the risks outweigh the benefits, calling for a far more careful approach.

To Ball, open-weight models hinder the progress of AI by deterring labs from investing more capital in development. As a result, he predicted that the Trump administration would “create large amounts of regulatory risk around the use of open-weight Chinese models,” which would in turn generate enough fear, uncertainty, and doubt — or “FUD,” in the lingo — that would have hyperscalers back off from using Chinese AI.

Leaving aside his glaring conflict of interest as an executive at OpenAI, netizens were baffled by his line of argumentation.

“The head of strategic futures OpenAI seems a little rattled,” one user wrote.

“This is really crazy stuff to believe,” wrote Alexander Green, founder of AI company Littlebird, who effectively disagreed with Ball on his entire line of thinking.

It’s not hard to see the motivation behind Ball’s defensive stance. OpenAI continues to struggle behind its competitors as AI companies seek massive investments to cover their skyrocketing spending. Powerful AI models like Kimi K3 that subvert the status quo could prove an existential threat without regulatory intervention.

“Ball’s original post reads less like advocacy and more like the kind of unsolicited geopolitical candor that plays fine on a think-tank blog but lands badly when you are two weeks into a job at the company most likely to benefit,” AI agency Lumien wrote in a new blog post.

The OpenAI exec’s views on how the Trump administration would most likely intervene also didn’t convince Trump’s AI czar David Sacks.

“I’m not sure whether Dean Ball is confessing to a regulatory capture strategy or simply predicting this will happen,” he tweeted in response. “Either way, the weaponization of regulatory uncertainty as a competitive tool should be completely unacceptable.”

“We are at a critical inflection point in AI policy,” he added. “The leading closed labs, already a duopoly in terms of AI model revenue, want the government to eliminate their open source competition.”

“They have laid their cards on the table,” Sacks wrote. “It is time for the rest of Silicon Valley — the vast majority that still values open competition — to do the same.”

Other government officials were far less kind in their assessment of Ball’s comments.

In one particularly scathing response, US defense undersecretary Emil Michael called Ball the AI industry’s “supreme village idiot” — a shocking, yet somehow unsurprising, tone for a top Trump official.

More on Chinese AI: A Chinese AI Model Just Shot to Number One on the Charts, Sending Shockwaves Through the American Tech Industry

OpenAI Exec Laments That China Is Giving Away Models So Good That For-Profit Companies Won’t Be Able to Compete

A reporter based in London at The Verge, specializing in artificial intelligence coverage and serving as a Senior Tarbell Fellow, provides insights into the intensifying competition between Chinese and American tech firms in the AI landscape. Recently, Chinese companies Moonshot and Alibaba introduced advanced AI models that they assert can rival offerings from OpenAI and Anthropic at significantly lower prices. This rapid advancement signals a tightening of the competitive landscape, particularly as AI technology becomes increasingly pivotal in areas such as national security and economic influence.

Moonshot AI, a prominent AI developer in Beijing, kicked off this wave of announcements by launching its model, Kimi K3, last Friday. The company claims that its internal evaluations consistently position Kimi K3 above most US systems, only trailing behind OpenAI’s GPT-5.6 Sol and Anthropic’s Claude Fable 5, with Kimi K3 performing better on certain benchmarks.

Shortly after, Alibaba previewed its own model, Qwen3.8, which it describes as "one of the most powerful model[s] available today" and claims it comes in just behind Fable 5, Anthropic’s leading model.

A significant distinguishing factor for both Chinese firms is their commitment to public accessibility. Unlike some US companies, including Meta, which have begun releasing models for developers to download and modify, Moonshot and Alibaba are offering their advanced models openly, enhancing their competitive stance within the AI sector.

Moonshot refers to Kimi K3 as the largest open-source AI system globally, boasting an impressive 2.8 trillion parameters. This metric serves as a rough gauge of a model's complexity and performance potential, although a higher parameter count does not always equate to superior capabilities. Meanwhile, Alibaba’s Qwen3.8 features a model size of approximately 2.4 trillion parameters and is described as "continuously evolving." In contrast, OpenAI and Anthropic do not publish specific parameter counts for their top models.

The true performance of these Chinese models remains uncertain until they are fully launched and assessed independently. Moonshot has announced plans to release the complete model weights on July 27, while Alibaba indicates that Qwen3.8 will also be offered with open weights shortly.

The introduction of these models heightens the competitive dynamic between the United States and China in what many consider one of the most significant technological contests of the era. The impact of these developments is reminiscent of last year’s launch of a cost-effective model by DeepSeek that posed a serious challenge to US systems. Moreover, the emergence of these models raises critical questions about whether the substantial investments made by American firms in infrastructure and model development can guarantee a lasting edge, especially when Chinese competitors may achieve similar or greater capabilities with fewer resources.

The release of these advanced Chinese models, which can be freely downloaded and customized, starkly contrasts with the more cautious approach of American labs that maintain proprietary systems. This trend of openness persists despite the U.S. government’s rapid moves to limit global access to top-tier technology, such as implementing export controls to block China's access to leading chips and compelling Anthropic to withdraw its advanced systems from international markets due to concerns regarding competition.

While the true efficacy of these new Chinese models is yet to be verified, their emergence is poised to escalate the technological rivalry between the United States and China, influencing economic strategies and security policies, and revealing that America’s lead in AI may not be as substantial as previously thought.

China strikes a double blow to the U.S. AI supremacy.

A shadowy boxing ring under dim spotlights: a giant metallic red glove emblazoned with China’s flag delivers a fierce one-two punch to a shattered high-tech brain painted with the Stars and Stripes, sparks flying and metal fragments scattering to capture China’s blow to America’s AI dominance.

China delivers a one-two punch to America’s AI dominance - The Verge

- China's AI firms Moonshot and Alibaba claim their models rival OpenAI and Anthropic's best.  
- Both companies emphasize public availability of models, contrasting with US labs' proprietary systems.  
- New models intensify the US-China technological rivalry, impacting economic and national security policies.  

is a London-based reporter at The Verge covering all things AI and a Senior Tarbell Fellow. Previously, he wrote about health, science and tech for Forbes.

China’s leading AI companies are ramping up the pressure on Silicon Valley, as Moonshot and Alibaba unveiled models they claim can go toe-to-toe with the best from OpenAI and Anthropic at a fraction of the cost. The rapid-fire releases suggest America’s lead at the AI frontier is increasingly tight, just as the technology is becoming central to national security, economic power, and geopolitical influence.

The opening salvo came from Beijing-based Moonshot AI, one of China’s leading AI model developers, which unveiled Kimi K3 on Friday. Moonshot claims its own testing ranks it consistently above nearly every US system, trailing only OpenAI’s GPT-5.6 Sol and Anthropic’s Claude Fable 5, though it came out ahead on certain benchmarks.

Over the weekend, Chinese tech behemoth Alibaba followed with a preview of Qwen3.8, a new model it says is “one of the most powerful model[s] available today” and “second only to Fable 5,” Anthropic’s flagship model.

Both companies are emphasizing a key difference from the leading US labs: Rather than locking their most advanced models behind closed doors, they are making them publicly available. While some US companies, most notably Meta, have taken a similar approach, releasing models that developers can freely download, modify, and build upon has become a growing point of differentiation for China’s AI industry.

Moonshot describes Kimi K3 as the world’s largest open-source AI system, with 2.8 trillion parameters. Parameter counts are measures of a model’s complexity during training and offer a rough indication of its scale and performance, though bigger does not always mean better. Alibaba says Qwen3.8 is a 2.4 trillion parameter model and “continuously evolving.” Neither OpenAI nor Anthropic disclose exact parameter counts for their leading systems.

It remains difficult to assess how capable either Chinese model is until they are fully released and independently tested. Moonshot says it will release full model weights — the internal numerical learned during an AI model’s training period — a week from now on July 27th. Alibaba says Qwen3.8 is “going open-weight soon.”

Even so, the releases have already sharpened competition between the US and China in what has been repeatedly characterized as the defining technological race of our time. They have shaken up the industry in a way not seen since DeepSeek unveiled a low-cost model last year that rivaled leading US systems. The models also raise questions about whether the vast sums of money US companies are pouring into chips, data centers, and model training can secure a durable advantage, particularly if Chinese rivals can approach — or surpass — that frontier with fewer resources.

The prospect of two highly capable Chinese models being released for others to download and adapt also contrasts starkly with the more guarded approach of US labs, whose most advanced systems remain proprietary. That openness emerges even as Washington moves rapidly to restrict global access to the underlying technology. The government has used export controls to restrict China’s access to the most advanced chips, as well as to force Anthropic to pull its most capable system from the market over concerns it could help foreign competitors catch up.

Whether the new Chinese models live up to their creator’s claims remains to be seen. But, like DeepSeek before them, they are likely to sharpen the technological rivalry between the US and China, influence economic and national security policy, and show that America’s lead is far narrower than it once appeared.

China delivers a one-two punch to America’s AI dominance

Throughout much of this year, the spotlight was firmly on Anthropic, with investors showing little interest in OpenAI. However, recent developments indicate a shift in preference: Anthropic remains a top choice, yet OpenAI is witnessing a notable increase in demand following the launch of its highly anticipated models and the success of its AI coding tool, Codex.

Dave McClure, the founder and managing partner at Practical Venture Capital, which specializes in acquiring secondary shares of pre-IPO firms, noted, "In the last month, there's been a resurgence of interest."

Both Anthropic and OpenAI are currently private companies, prompting most investors to acquire shares through secondary markets, where stock is provided by employees or early backers. This market lacks liquidity and is fragmented, rendering listed valuations approximate rather than reflective of ongoing public-market transactions.

McClure explained that earlier this year, OpenAI dealt with a slew of negative press regarding issues like sluggish growth, executive turnover, and competition from Anthropic, alongside a lawsuit involving Elon Musk. This resulted in a significant drop in demand for OpenAI shares during the early stages of the second quarter.

Though Anthropic still leads the market by attracting five interested buyers for every two looking for OpenAI shares, Glen Anderson, CEO of Rainmaker Securities, observed, "Just a month back, the demand for OpenAI was quite limited, but we're now seeing an uptick in interest."

OpenAI has yet to respond to requests for comments on the matter.

Growing Optimism Around New Releases

Secondary market traders consulted by Business Insider highlighted that OpenAI's recent model launches and the performance of Codex have rekindled interest among investors after a period dominated by Anthropic's momentum.

In June, OpenAI presented its flagship GPT-5.6 Sol model, which was more widely released this month alongside GPT-5.6 Terra and the budget-friendly GPT-5.6 Luna. Independent evaluations have positioned GPT-5.6 Sol among the leading models, though it still trails behind Claude's latest offerings, Mythos and Fable.

Adam Crawley, cofounder of Augment—an exchange for pre-IPO tech firms—reported, "Investors are returning to OpenAI."

Mark Klein, CEO of Neostellar, which provides access to late-stage startup investments, expressed optimism following an announcement from an OpenAI executive that the company has reached 9 million active users across two of its flagship platforms: Codex, designed for complex software coding tasks, and ChatGPT Work, a newly introduced cloud-based AI assistant that manages multi-step professional workflows.

Klein remarked, "The speed of this growth is significant, indicating that agentic AI is moving beyond its initial developer-focused audience into a wider array of professional applications. More generally, OpenAI seems to be dedicated to leveraging ongoing model enhancements to drive sustained usage in coding, research, and knowledge work."

OpenAI Offers Perceived Value

Previously, Anthropic had been recognized as the more affordable option, consistently trading at a lower valuation than OpenAI. But with the current market dynamics shifting, some investors now find it logical to hedge their Anthropic investments by incorporating the relatively less expensive OpenAI shares.

As reported recently by Business Insider, Anthropic's valuation ballooned to $1.2 trillion in the secondary market. Meanwhile, OpenAI's valuation has risen to $933 billion, reflecting a 20% increase over the past three months, according to Caplight, a secondary trading platform.

OpenAI's CEO, Sam Altman, acknowledged the company's challenges in recent times, attributing much of the responsibility to himself while indicating that a turnaround is on the horizon.

“We did not have our best last 12 months ever, and that’s mostly my fault, but we are on the brink of having our best 12 months to date," he shared on X.

Coinciding with Altman's comments, the Chinese AI startup Moonshot AI unveiled Kimi K3, claiming it to be the largest open-weight AI system globally.

McClure warned, “There continues to be pressure from other Chinese and open-source competitors, which may impact OpenAI's growth and profit margins, presenting significant challenges ahead.”

OpenAI experiences a 'revival' of interest in secondary markets.

A bustling outdoor tech bazaar bathed in warm morning sunlight, rows of wooden stalls draped with “OpenAI” text (no logo) banners displaying second-hand AI modules and used GPUs on price-tagged shelves. Adult buyers in casual attire excitedly examine hardware, handshake deals taking place over crates of digital tokens softly glowing above the tables—capturing the resurgence of interest in secondary markets.

OpenAI Has Seen a 'Resurgence' of Interest in Secondary Markets. - Business Insider

- OpenAI is experiencing renewed investor interest following the release of new models and Codex.  
- Anthropic still dominates investor demand, with five buyers for every two seeking OpenAI shares.  
- OpenAI's valuation has increased to $933 billion, now trading cheaper than Anthropic's $1.2 trillion.  

For most of this year, investors couldn't get enough of Anthropic while largely ignoring OpenAI. Now the tide is turning: although Anthropic remains the clear favorite, OpenAI is experiencing a significant surge in demand after it released highly touted new models and saw success with its AI coding agent, Codex.

"In the last month, there has been a resurgence in interest," said Dave McClure, founder and managing partner at Practical Venture Capital, which buys secondary shares in pre-IPO companies.

Since Anthropic and OpenAI have yet to go public, the vast majority of investors buy via secondary markets, where existing stock is sold by employees or early investors. The market is fragmented and illiquid, so quoted valuations are estimates rather than continuous public-market prices.

"OpenAI was facing a lot of negative stories in the first quarter related to things like slower growth, executives sick or leaving, Anthropic racing past them, and the Elon Musk lawsuit," McClure said. "This resulted in dramatically less demand in the early second quarter for OpenAI secondary."

Anthropic still dominates, drawing five buyers for every two seeking OpenAI shares, according to Glen Anderson, CEO of Rainmaker Securities, a merchant bank focused on private stock transactions.

"A month ago, we were seeing limited demand for OpenAI," he said. "We're still seeing breakneck demand for Anthropic, but now you're seeing OpenAI get bid on a lot more often."

OpenAI did not respond to a request for comment.

Optimism about new models

Secondary traders Business Insider spoke with said that OpenAI's newest models and coding agent, Codex, have given buyers a reason to take another look after months in which Anthropic commanded most of the momentum.

OpenAI previewed its flagship GPT-5.6 Sol model in June and released it more broadly this month alongside GPT-5.6 Terra and the lower-cost GPT-5.6 Luna. Independent benchmarks have placed GPT-5.6 Sol near the top of the field, though still below Claude's latest Mythos and Fable models.

"Investors are coming back to OpenAI," said Adam Crawley, cofounder of Augment, an exchange for pre-IPO tech companies.

Mark Klein, president and CEO of Neostellar, which provides access to late-stage startups, said he has been encouraged by an OpenAI executive disclosing last week that the company has reached 9 million active users across two of its flagship enterprise platforms: Codex, an autonomous AI agent designed for complex software engineering and coding tasks, and ChatGPT Work, a newly launched cloud-based AI assistant that executes multi-step professional workflows.

"The pace of that growth is notable because it suggests agentic AI is expanding beyond its initial developer audience and into a much broader range of professional workflows," Klein said. "More broadly, OpenAI appears focused on translating continued model improvements into sustained usage across coding, research, and knowledge work."

OpenAI is the cheaper stock

Until this year, Anthropic was the clear bargain, consistently trading at a significant discount to OpenAI. Now that the dynamic has flipped, some traders say it makes sense to hedge their Anthropic positions by purchasing the relatively cheaper OpenAI stock.

Anthropic soared to a $1.2 trillion valuation on secondary markets, Business Insider reported last week. OpenAI is now trading at a $933 billion valuation, up 20% over the past three months, according to data from Caplight, a secondary trading platform.

Last week, OpenAI CEO Sam Altman took personal responsibility for the company's recent struggles while signaling a major comeback.

"We did not have our best last 12 months ever, which is mostly my fault, but we are about to have our best 12 months to date," he posted on X.

On the same day as Altman's admission, Chinese AI startup Moonshot AI released Kimi K3 , a powerful new model that the startup says is the largest open-weight AI system in the world.

"There continues to be pressure from other Chinese and Open-source competitors that will likely put pressure on OpenAI growth and margins, so they still have a tough hill to climb," said McClure.

OpenAI has seen a 'resurgence' of interest in secondary markets.

Apple has initiated legal action against OpenAI, alleging that the artificial intelligence firm has engaged in misconduct to persuade current and former Apple employees to divulge confidential information. OpenAI responded, stating it is “not aware of any evidence that this complaint has merit.”

Recently on the TechCrunch’s Equity podcast, hosts Kirsten Korosec, Sean O’Kane, and I discussed the potential ramifications of this lawsuit on OpenAI's ambitions to enter the hardware sector with plans for a mobile smart speaker, as well as its intention to go public.

Sean pointed out that even if the court declines to issue any form of injunction, the lawsuit could still lead to delays in OpenAI's projects. “This situation naturally has the potential to slow down what OpenAI is working on, and I believe that was part of Apple's strategy. They don’t take these steps lightly,” he remarked.

With significant stakes at play, there's speculation on whether OpenAI will aim for a quick settlement or if it believes it can withstand another trial, having recently triumphed in court against Elon Musk. Kirsten suggests the latter is likely.

In our conversation, which has been condensed for clarity, Kirsten posed a question about the implications of a listening device potentially in everyone’s pocket, pointing to concerns over privacy. Sean expressed hesitance at the idea, noting that such devices wouldn't just pick up conversations of the user but those of people nearby. He highlighted the need for a reevaluation of social norms surrounding consent and privacy in public spaces.

Kirsten reminded us of the speculated device, which has been mentioned for some time, and its connection to the recent lawsuit Apple filed. Notably, this lawsuit outlines serious allegations against OpenAI, claiming misconduct aimed at OpenAI employees formerly associated with Apple, including the specific mention of Tang Tan, the chief hardware officer.

The essence of Apple's accusations revolves around the alleged theft of trade secrets, which could impact the development of competing hardware products. While the merits of the lawsuit remain to be explored, the discussion around it certainly promises intrigue.

Sean highlighted two points regarding implications for OpenAI. One major concern is the potential risk to whatever projects the company has ongoing, particularly in light of the impending IPO. OpenAI has confidentially filed for an IPO, with prospects of it launching soon. Sean emphasized that if a substantial portion of their potential market includes hardware, this lawsuit could complicate matters and influence the IPO valuation.

Anthony noted that Apple claims over 400 of its former employees are now part of OpenAI. Although both firms are large, this number indicates notable talent migration from Apple to OpenAI, suggesting a serious shift in intellectual resources. Additionally, he raised questions about the potential damage OpenAI could incur from public perception and branding, considering the prior trial brought forth some uncomfortable revelations, despite their legal victory.

Kirsten concurred with Anthony’s observations, predicting that OpenAI is likely prepared to face the challenges ahead, armed with insights from their earlier experiences in court.

Could an Apple lawsuit hinder OpenAI's hardware ambitions?

A dark, moody scene in a shadowy tech courtroom: on a worn wooden table lies a torn blueprint labeled “OpenAI” with intricate AI hardware circuits drawn across it. A massive gavel bearing a glowing Apple logo slams down onto the plans. Warm overhead spotlight cuts through the gloom, casting dramatic shadows and tension across the scene.

Can an Apple lawsuit derail OpenAI’s hardware plans? - TechCrunch

- Apple filed a lawsuit against OpenAI for allegedly stealing trade secrets from its employees.  
- OpenAI claims there is no evidence supporting Apple's allegations and their lawsuit lacks merit.  
- The lawsuit may delay OpenAI's hardware plans and affect their upcoming IPO preparations.

Apple recently filed a trade secrets lawsuit against OpenAI, accusing the AI company of a pattern of misconduct aimed at getting current and former Apple employees to share confidential information. (In response, OpenAI said it is “not aware of any evidence that this complaint has merit.”)

On the latest episode of TechCrunch’s Equity podcast, Kirsten Korosec, Sean O’Kane, and I debated whether this lawsuit will cast a shadow over OpenAI’s much-discussed plans to get into the hardware business (starting with a mobile smart speaker) and go public.

“Even setting aside whether or not the court grants any kind of injunctive relief or any kind of restraining order over what OpenAI is doing, it just naturally can lead to that sort of situation where it’s going to cause some delays in what OpenAI is working on,” Sean suggested. “Which I’m sure was probably part of the reasoning behind Apple doing this. They don’t do this stuff willy nilly.”

With all those plans on the line, will OpenAI try to settle this as quickly as possible, or did it learn from its recent courtroom victory against Elon Musk that it can endure the cost and embarrassment of a trial? Kirsten, at least, predicts the latter.

Keep reading for a preview of our conversation, edited for length and clarity.

Kirsten Korosec: Sean, how do you feel about Sam Altman listening to you with a little device maybe in your pocket?

Sean O’Kane: I’m good. Maybe that’s predictable, but I’m good. No thanks.

We’ll get into it, I’m sure, but this is allegedly the first product that OpenAI has been working on in its hardware division with Jony Ive and company. They’ve been really coy ever since that weird video they put out last year of them sitting at that coffee shop or bar in San Francisco and sort of talking very vaguely about hardware and legacy devices, meaning laptops and phones. And so if this is the direction they’re headed in, all power to people who want to have somebody like that always listening to them. This is not going to be for me.

Anthony Ha: Part of what we have to remember about those kinds of devices is also that, depending on how mobile it is, it’s not just listening to you, it’s listening to the people around you. I might be fine with it — I’m not fine with it, but let’s say I was — but then if we met up in-person at Disrupt, then suddenly it might be listening to all of us.

There’s all kinds of social norms that are going to have to be renegotiated if these things become widespread. I think we should make fun of and criticize people who record other people without consent.

Kirsten: Well, I bring up the device that has been speculated about for a really long time, and we’ll see what it really ends up being once it’s officially introduced, but it’s important in the context of this lawsuit that Apple filed last Friday.

It was the biggest news of the week, certainly, and this is a trade secret lawsuit. It has some pretty wild allegations and we should very much emphasize these are allegations that have been filed in a complaint by Apple. But what it is accusing OpenAI of is a pattern of misconduct at the highest levels, specifically directed towards OpenAI employees who used to work at Apple. And in fact they’ve named the chief hardware officer Tang Tan in this lawsuit.

This is all important because Apple is accusing OpenAI of essentially stealing their trade secrets, but in the context of that, this could be then used for a competing hardware product. I’m wondering if maybe we don’t get into whether this lawsuit has merits, because we haven’t gone through full discovery, but what are your initial impressions of the lawsuit aside from the fact that wow, this is going to be entertaining?

Sean: Two things. One, this is a pretty big risk potentially to whatever it is OpenAI is working on. Even setting aside whether or not the court grants any kind of injunctive relief or any kind of restraining order over what OpenAI is doing, it just naturally can lead to that sort of situation where it’s going to cause some delays in what OpenAI is working on, which I’m sure was probably part of the reasoning behind Apple doing this. They don’t do this stuff willy nilly.

The other is that we think that OpenAI is — we know that they’ve filed confidentially for an IPO. We think it might happen as early as the end of this year, or early next year, if you believe Sam Altman’s cautious language around the IPO. And this just raises a whole bunch of questions around that because, on the one hand, we think their business right now is probably overwhelmingly the software; they’re not really factoring in any hardware business into that picture at the moment.

They’re about to go to the markets and they’re going to be pitching bankers and investors on where they think their addressable market should be, and if they have a big amount of that pegged to a potential hardware division and hardware products, this could be a huge risk to that and changes a lot of the calculus of sort of how the IPO gets priced. So that’s where my head’s at.

Anthony: One [allegation] that I assume that Apple must have pretty solid like numbers on is, they said more than 400 Apple employees now work at OpenAI. Granted, both of them are very large companies with many thousands or tens of thousands of employees. So as a percentage, it’s not necessarily huge. But that seems like a lot of people and a pretty serious talent drain.

And the other thing I’m wondering is related to Sean’s point. With the context of the potential IPO, how much damage did OpenAI ultimately take from a marketing and brand perspective from the trial it already went through? That it seemed to basically win, but there was a lot of not-terrible-but-kind-of-embarrassing dirty laundry that came out in the testimony. To what extent are they just like, “We do not want to go through that again”? Or did they take the lesson of, “Hey, we went through it and we survived and we’ll be okay if we have to do another trial with Apple”?

Kirsten: I fully predict the latter, by the way.

Can an Apple lawsuit derail OpenAI’s hardware plans?

The introduction of ChatGPT has brought significant challenges to the world of education. Its impact on teachers is profound, making their roles considerably more complicated than they were just a couple of years ago. It's important to reflect on this transformation. When students turn to AI for writing assistance, it poses a serious concern; they may miss out on essential writing skills. This reliance can strip away their unique voices, depriving them of the opportunity to express their truths and share their personal narratives. As a result, we risk diminishing the creative expression of potentially multiple generations.

Dave Eggers informed OpenAI employees that ChatGPT was 'muting a whole generation.'

A moody, low-light OpenAI boardroom with “OpenAI” in plain text stenciled on the back wall. At a simple podium, bestselling author Dave Eggers faces the room, his figure lit by a single overhead lamp. Around a long table, adult staff silhouettes slump in shadow. Above them hovers an oversized ChatGPT chat bubble with a zipper across its “mouth,” casting a long dark silhouette over empty chairs—symbolizing the silenced generation.

Dave Eggers told OpenAI staff that ChatGPT was ‘silencing an entire generation’ - The Verge

- ChatGPT's presence has made teaching significantly more challenging for educators.  
- Students using AI to write may lose essential writing skills and personal voice.  
- The reliance on technology could silence future generations' ability to express themselves.  

“The effect of ChatGPT on educators’ lives is catastrophic. Whether you intended to do it or not, you’ve made every teacher’s life infinitely more difficult than it was two years ago. So, just let that settle in… If students are using it to compose, which is the biggest tragedy of all, they’ll never learn to write. And their voice is stolen from them. They’ll never have the ability to say their truth and tell their own story. And that’s silencing an entire generation or two.”

Dave Eggers told OpenAI staff that ChatGPT was ‘silencing an entire generation’

An open letter regarding OpenAI's covert device

The creator of ChatGPT is developing a mysterious physical product, sparking both intrigue and debate within the tech community.

A public letter addressed to OpenAI’s hidden device

A sunlit minimalist desk by a tall window, a crisp open letter on parchment addressed to “OpenAI” in bold black type, beside a sleek, secretive gadget under soft natural light—its smooth metal shell subtly engraved with “OpenAI” text, gentle shadows hinting at hidden circuitry and a sense of quiet intrigue.

Joanna Stern: An open letter to OpenAI’s secret gadget - NBC News

- OpenAI is developing a secret physical device, details of which are unclear.  
- The project has sparked controversy and speculation within the tech community.  
- Features and functionality of the gadget remain undisclosed, leaving many questions unanswered.  

An open letter to OpenAI’s secret gadget

The ChatGPT maker is working on its own physical device, which is shrouded in mystery — and controversy.

An open letter to OpenAI’s secret gadget

In the ongoing legal confrontation between Apple and OpenAI, new developments have emerged. Apple suspects that additional former employees may have improperly shared sensitive company information with OpenAI, escalating the matter.

The Financial Times reports that Apple has issued legal preservation letters to about 40 ex-employees who are now working at OpenAI. These letters instruct the recipients to retain any documents or communications that could pertain to their previous roles at Apple.

Last week, Apple initiated a lawsuit against OpenAI, claiming that two of its former employees, now at OpenAI, disclosed proprietary trade secrets. Apple contends that this confidential information has been utilized by OpenAI in the development of its consumer hardware offerings.

OpenAI has categorically denied these allegations. Nevertheless, Apple’s lawsuit suggests that this situation could be just "the tip of the iceberg," hinting at broader concerns.

The issuance of these legal letters signals that Apple suspects there may be more individuals who misappropriated confidential information, apart from the two employees currently named in the lawsuit: Tang Yew Tan, a former Vice President of Product Design now serving as OpenAI's Chief Hardware Officer, and Chang Liu, who transitioned from being an iPhone engineer at Apple to OpenAI at the start of the year.

Apple has stated that over 400 of its former employees now work at OpenAI, raising questions about why these specific individuals are being targeted. Notably, they represent roughly 10% of Apple’s workforce that has shifted to the AI firm, making it crucial to watch how this situation develops as the legal proceedings progress.

Apple reportedly sends legal letters to numerous former OpenAI employees.

A dim, moody executive boardroom lit by a single overhead lamp. Centered on a polished mahogany table is a stack of crisp legal envelopes each stamped with a bold Apple logo. On the far wall, the word “OpenAI” appears in stark white text. Silhouetted figures at the table’s edge lean forward warily, shadows stretching behind them. Dark theme, dramatic lighting.

Apple sends legal letters to dozens of OpenAI defectors, report says - Mashable

- Apple has sent legal preservation letters to about 40 former employees at OpenAI.  
- The letters aim to safeguard documents related to potential information sharing with OpenAI.  
- Apple suspects more employees may have shared trade secrets beyond those currently sued.  

It's the latest salvo in the ongoing legal battle between Apple and OpenAI.

Apple appears to believe that additional former employees have shared company information with OpenAI.

Apple appears to believe that additional former employees have shared company information with OpenAI. Credit: Nikolas Kokovlis/NurPhoto via Getty Images

Former Apple employees who now work at OpenAI have been put on notice.

According to a new report from Financial Times, Apple has sent legal preservation letters to roughly 40 former employees, which request that they save any documents or communication that could be relevant to their prior employer.

Last week, Apple filed a lawsuit against OpenAI alleging that two former employees who went on to work for the AI giant shared Apple's trade secrets. Apple believes that this confidential information was used by OpenAI to develop the consumer hardware products that the AI company is currently working on.

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OpenAI has denied the allegations, of course. However, in its lawsuit, Apple said that it believes this is "just the tip of the iceberg."

So, what do these new legal letters mean? It appears that Apple believes that there may be more former employees who misused confidential company information besides Tang Yew Tan and Chang Liu, the two employees whom Apple is currently suing. The legal letters could also serve as a warning from Apple, putting former employees on notice that Apple is watching their work closely. Based on the Financial Times report, the employees could also be subject to discovery requests as the lawsuit unfolds.

Tan is Apple's former Vice President of Product Design and currently works at OpenAI as the company's Chief Hardware Officer. Liu was formerly an iPhone engineer at Apple and left the company to join OpenAI at the beginning of the year.

According to Apple, more than 400 former employees now work at OpenAI. As more details emerge, it'll be interesting to find out Apple's reasoning in targeting these specific OpenAI employees, who make up about 10 percent of its former workers who jumped to the AI company.

Apple sends legal letters to dozens of OpenAI defectors, report says

OpenAI’s Chief Financial Officer, Sarah Friar, recently shared her framework for assessing the economic impact of investments in artificial intelligence. Unlike traditional software evaluations, which focus on metrics such as user counts and renewals, Friar emphasizes that AI effectiveness should be gauged by the actual value it generates.

In her blog post, Friar posed a crucial question for companies: Is the value derived from AI's outputs increasing at a pace that outstrips the costs incurred to generate them? She stresses that understanding this requires moving beyond surface-level statistics like cost per token.

Friar introduces a vital metric termed “useful intelligence per dollar.” This evaluation encompasses four components: the significance of the AI's output, the cost associated with each successful task, the reliability of the results, and whether increased usage enhances the value derived from each dollar spent.

To operationalize this, Friar advises that leaders should monitor the volume of quality-assured tasks completed by AI, total the expenses involved, and calculate the cost per successful task. They must also verify if stakeholders can rely on these outputs and whether the growth in high-quality work occurs at a rate that exceeds total costs, all while maintaining or improving quality. If these conditions are met, it indicates that each dollar invested in AI is yielding greater returns, with computational power playing a pivotal role in this dynamic.

“Our mission is to enhance this equation in every iteration: evolving better models, delivering quicker and more reliable outcomes, and reducing costs for essential tasks,” she articulated.

For OpenAI, compute resources are not merely an operational cost but a strategic asset. While the company does not publicly disclose detailed capital expenditure plans due to its private status, it had previously revealed the Stargate initiative, which outlines a comprehensive investment plan of $500 billion over four years to develop expansive AI infrastructure in the U.S. The initial funding phase aims for $100 billion, with a broader target of achieving a 10-gigawatt capacity by 2029. Remarkably, OpenAI has already surpassed its early milestones given the growing demand for AI solutions. Speculations suggest that the company could file for an IPO this summer or as late as 2027, with its current valuation standing at approximately $852 billion and poised to approach $1 trillion.

As Chief Financial Officers take on increasingly strategic roles, they are now expected to collaborate closely with CEOs in determining significant long-term investments, particularly in areas such as AI. 

Recently, McKinsey hosted its 24th Global CFO Forum, attracting around 100 finance executives from over 30 countries, representing leading organizations. Andy West, a senior partner at McKinsey and co-leader of its Strategy and Corporate Finance division, shared insights from an informal survey he conducted during the event, finding that about two-thirds of CFOs now oversee strategic functions, an increase from less than a third five years ago.

“AI has been a topic of discussion at this conference for a few years,” West noted. He observed a shift from last year’s experimentation with AI toward an emphasis on transformation across entire enterprises in the current discussions.

OpenAI's CFO: 4 questions to determine if your AI investment is yielding returns

A sunlit, modern corporate office with a sharp-dressed executive (the CFO) standing before a large, semi-transparent digital dashboard. Four glowing question-mark icons hover above distinct performance charts and budget graphs. Warm natural light streams through floor-to-ceiling windows, casting soft shadows. Subtle “OpenAI” text (no logo) is etched into the glass wall behind the CFO, anchoring the scene in a strategic, analytical mood.

OpenAI’s CFO: 4 questions that reveal if your AI spend is paying off - Fortune

- OpenAI CFO Sarah Friar emphasizes measuring AI's value through useful intelligence per dollar.  
- Effective AI metrics should track task success, costs, and reliability over time.  
- CFOs increasingly influence strategy, particularly in long-term AI investments and decision-making.  

Is your AI paying off? Today, OpenAI CFO Sarah Friar published the scorecard she uses for telling whether you are actually getting economic value from AI spend.

For years, software success was measured through adoption—seats, active users, and renewals, Friar notes. She argues that AI is different: it must be measured by the work it actually accomplishes.

“The basic economic question facing CFOs and other business leaders is whether the value of the work AI completes grows faster than the cost of producing it,” Friar wrote in a blog post. Answering that question, she says, requires going deeper than simple metrics like cost per token.

She argues that the metric that matters for AI is what she calls “useful intelligence per dollar.” It has four elements: Is AI completing work that matters? What does each successful task cost? Can people depend on the result? And does each dollar produce more value as usage grows?

In practice, that means leaders should track the volume of AI-completed work that meets a defined quality bar, add up the full cost of completing that work, and then divide by the number of successful tasks to get a cost per successful task. From there, the test is whether people can reliably depend on the output and whether, over time, high-quality completed work grows faster than total cost while quality holds or improves. If it does, each AI dollar is producing more value—and compute sits at the center of that equation, Friar explains.

“Our job is to make that equation better with every generation: more capable models, faster and more dependable results, and lower costs for the work customers need done,” she writes.

For OpenAI, a hyperscaler, compute is not just a technology expense—it is a strategic asset. As a private company, it does not publish formal capex guidance, but the Stargate initiative announced in January 2025 outlined a plan to invest up to $500 billion over roughly four years to build large-scale AI infrastructure in the U.S.—with the initial phase targeting about $100 billion and the broader buildout accelerating toward a 10-gigawatt capacity goal in the U.S. by 2029. Just over a year later, it has already surpassed that milestone, as demand for AI continues to accelerate. According to reports, OpenAI’s IPO could come as soon as this summer or as late as 2027. The company is already valued at $852 billion and approaching the $1 trillion range.

While finance chiefs have long led capital allocation and investor communication, they are increasingly expected to help determine strategy, including where the company places its biggest long-term bets, like AI spend, alongside the CEO.

McKinsey recently held its 24th annual Global CFO Forum, an exclusive gathering that brought together about 100 finance chiefs from over 30 countries, representing some of the world’s largest organizations. Andy West, a senior partner at McKinsey and global co-leader of the firm’s Strategy and Corporate Finance practice, told Fortune he conducted an informal poll, asking CFOs whether the strategy function now reports to them. About two-thirds raised their hands. Five years ago, it would have been less than a third, he said.

“We’ve been talking about AI at this conference for a couple of years now,” West said. Last year, finance leaders were still experimenting with AI. This year, the conversation shifted decisively toward enterprise-wide transformation, he said.

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