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Silicon Valley startups are turning to Chinese open-source AI models

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Misha Laskin, a theoretical physicist and machine learning engineer who contributed to the development of some of Google’s most powerful AI models, encountered a concerning picture when examining the American AI landscape earlier this year.

Laskin observed a growing interest among US AI companies in free, customizable, and increasingly powerful open-source AI models.

The vast majority of these models are produced in China and are rapidly gaining ground against their US competitors.

Assessing the current situation, Laskin stated, “These models are not far behind the frontier (the cutting edge of technology). In fact, they are surprisingly close to the frontier. What is coming now is noticeably close to the frontier.”

Following this development, Laskin founded a startup called Reflection AI to offer an open-source American alternative to the Chinese models gaining traction in Silicon Valley.

The founder of the company, which recently reached an $8 billion valuation, said, “You are starting to see signs that open model companies in China are actually pushing the frontier of intelligence and the limits of intelligence technology in general.”

Over the past year, a significant portion of America’s most popular AI startups have turned to Chinese open AI models, which compete with and sometimes replace expensive US systems as the foundation for American AI products.

More than 15 AI startup founders, engineers, and industry experts who spoke to NBC News stated that American companies’ models still hold the lead in terms of capability.

However, experts emphasized that many Chinese systems are cheaper to access, more customizable, and have become sufficiently competent for many use cases over the past year.

Cost and speed advantages are changing preferences

Investors have poured tens of billions of dollars into OpenAI and Anthropic with the expectation that leading American AI companies will dominate the global market.

But the increasing use of free Chinese models by American companies raises questions about how exceptional these models are and whether America’s insistence on a “closed model” approach is flawed.

Michael Fine, head of machine learning at the search company Exa, which is valued at $700 million and backed by established Silicon Valley investors Lightspeed Venture Partners and Nvidia, said that running Chinese models on their own hardware is, in many cases, much faster and cheaper than using large models like OpenAI’s GPT-5 or Google’s Gemini.

Fine described the process:

“We often launch a feature with a closed model, but then we realize it’s too expensive or too slow, and we ask, ‘What tricks do we have up our sleeve to make this faster and cheaper?'”

Fine stated that the solution is often to replace the closed model with an equivalent open model and then run it on their own infrastructure.

Chinese-origin systems like DeepSeek’s R1 and Alibaba’s Qwen models can be used for free because they are “open-source” or “open-weight,” meaning anyone can download, copy, modify, and run them.

These systems differ from “closed” systems accessed through data centers controlled by major tech giants, such as Anthropic’s Claude or OpenAI’s GPT models.

The technology gap is closing fast

For years, the closed-source models from OpenAI and Anthropic performed far better than both American and Chinese open alternatives.

Even open-source initiatives like BloombergGPT, trained by institutions with resources like Bloomberg on their own financial data, lagged behind OpenAI’s closed models in financial knowledge.

However, over the past year, Chinese companies like DeepSeek and Alibaba have made significant technological strides. According to metrics tracked by Artificial Analysis, an independent AI benchmarking company, their open-source products now approach or match the performance of leading closed American models in many areas.

“The gap is really narrowing,” said Lin Qiao, co-creator of PyTorch, the dominant framework for training AI models, and CEO of Fireworks AI, regarding the capability difference between American closed-source and Chinese open-source models.

As a result of this performance increase, platforms like OpenRouter, which allow users to choose between different models, are seeing a shift toward Chinese open-source models.

Jerry Liu, founder of the productivity app Dayflow, estimates that about 40% of his users now prefer to use open-source models.

Dayflow offers an application built on basic tasks like scanning screenshots and summarizing user activity.

Users can choose between Google’s Gemini model and smaller open-source options like Alibaba’s Qwen.

Liu noted that for tasks like describing a user’s screen, the Qwen model is extremely consistent, stating, “Qwen is as good as GPT-5 for my use case.”

Unlike GPT-5 or Gemini, a smaller version of Qwen can be run at a relatively low cost or for free.

Liu mentioned that paying for closed model usage could cost Dayflow up to $1000 per person, making cheaper open-source models critical for the application’s sustainability.

Privacy sensitivity encourages local processing

The open-source models used by Dayflow perform all processing on each user’s own computer. Liu stated that this is attractive to users who do not want to send their data to the cloud for privacy reasons.

Emphasizing his preference for using open-source models on his own device, Liu said, “Would I use a product where my entire screen is beamed to some random guy’s cloud? Never.”

In addition to increased performance, stronger privacy, and lower costs, open-source models are also gaining ground due to ecosystem advantages.

The rising adoption rate among developers and the open-source systems they create encourage more software engineers to use these models.

Antonio Vespoli, co-founder of the browser assistant startup Circlemind AI, said that Chinese models now dominate online developer resources.

There is a practical reason for this: Chinese models like Qwen, which Airbnb CEO Brian Chesky stated they rely on “heavily,” have abundant training guides and community support.

Charles Zedlewski, chief product officer at the AI infrastructure company Together AI, noted that developers now find it simpler and more efficient to start with open models and adapt them with their own data.

Zedlewski stated that companies understand their needs more clearly as they launch their first AI applications.

Of the top 20 models among users of Kilo Code, a popular application that helps software engineers write code, seven are of Chinese origin, and six of them are open-source.

Beijing’s strategic support and production speed

While most of America’s AI developments occur in the private sector and with a closed-model approach led by industry giants like OpenAI and Anthropic, the Chinese government plays a more active role in charting the country’s AI vision.

In a speech on November 1, Chinese President Xi Jinping called for “more cooperation in open-source technologies.”

In March, China’s top economic planning authority announced its intention to support an ecosystem of open-source models.

While Chinese labs generally release their models openly, American companies like OpenAI achieved early success with closed models and have remained committed to that approach.

Furthermore, many Chinese companies are releasing their products at a faster pace than their American competitors.

Alibaba has released a new model roughly every 20 days this year, while the average time between Anthropic’s releases has been 47 days.

Nathan Lambert, a senior research scientist at the Allen Institute for AI and an expert on the open model ecosystem, told NBC News that the recent progress of Chinese models is no coincidence.

“The Chinese are real innovators in AI,” Lambert said.

Lambert, who writes extensively about China’s AI developments on the Substack platform and is considered an expert on China’s open-source ecosystem, added that the balance of power has shifted rapidly in the last 12 months.

Some in Silicon Valley note that American models still hold a significant advantage at the cutting edge of AI capabilities and that closed American models offer a user-friendliness that cumbersome open models cannot match.

Tim Tully, a partner at Menlo Ventures, argued that closed models are still much more capable and generally more useful:

“The tools are better, the productivity is better, the agent frameworks being built and used by everyone are better with Anthropic and OpenAI. They just work better. So the ecosystem is strong in the closed-source environment.”

However, many companies may avoid using Chinese models due to the real or perceived risks of using a product built on a Chinese-origin foundation.

“There is a perceived risk that buyers, whether from the private or public sector, are hesitant to purchase a product based on a Chinese-origin open-weight model,” said Tully, an investor in Anthropic, one of the world’s leading closed-model companies.

The US open-source ecosystem is waking up

American AI companies and the federal government have taken notice of the recent rise of Chinese models. Experts have described America’s lack of powerful open-source models as an “existential” threat to democracy.

Although Meta’s high-profile Llama series has historically led American open-source efforts, CEO Mark Zuckerberg has signaled that Meta does not intend to make all of its “superintelligence” AI models open-source.

The stagnation in the performance of Llama models in recent years is also seen as one of the reasons open-source users have shifted to better-performing Chinese models.

But the US open-source ecosystem may be gradually awakening, with efforts by American innovators to enhance their competitiveness.

In July, the White House released an AI Action Plan that called on the federal government to “Promote Open-Source and Open-Weight AI.”

In August, OpenAI, the creator of ChatGPT, released its first open-source model in five years. Announcing the model’s launch, OpenAI referenced the importance of American open-source models, stating, “Broad access to these capable open-weight models created in the US helps expand democratic AI.”

The Seattle-based Allen Institute also released its latest open-source model, Olmo 3, at the end of November, designed to help users “quickly build reliable features for research, education, or applications,” according to the launch announcement.

Lambert from the Allen Institute also launched the “ATOM Project” (American Truly Open Models).

The ATOM Project’s manifesto states: “America has lost its lead in both performance and adoption in open models and is on track to fall further behind.”

“If we want to be the leading nation in the age of AI, we cannot cede such a critical piece of the ecosystem to any one nation,” Lambert said in a statement to NBC News.

America

US fiscal outlook unlikely to see major relief from AI boom, Yale model shows

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If the United States experiences an artificial intelligence-driven productivity boom in the coming years, it will translate into stronger economic growth, but the benefits to the nation’s fiscal outlook will remain limited.

With US public debt already high and rising rapidly, and given the lack of political will to reduce deficits through traditional measures such as spending cuts and tax increases, many have pinned their hopes on an AI boom to allow the country to grow its way out of its fiscal challenges.

However, new modeling from the Yale Budget Lab, reported by Axios, reveals that while an AI-driven productivity surge would improve the fiscal situation, the positive impact would not be as substantial as widely anticipated.

The primary reason is that a large portion of national income is highly likely to shift away from labor—which the US taxes at relatively high rates—and toward machines and software, or capital, which face lower tax rates.

The top federal income tax rate on labor income is 37%. In contrast, the corporate tax rate is 21%, while the top rate on long-term capital gains is 23.8%.

Furthermore, a significant portion of capital ownership is held through tax-exempt vehicles, such as retirement accounts and charitable foundations.

Consequently, even if companies generate higher profits through AI while spending less on human labor, these profits will not translate into the kind of revenue growth seen during past economic expansions, when the labor share of national income remained more stable.

In a scenario where AI provides only a slow boost to GDP growth, the Yale team’s model indicates there would be very little change in federal revenues by 2030.

Under a rapid AI-driven growth scenario, where annual GDP growth reaches 3.3% in the coming years and the labor share of income falls, federal revenues would increase by $216 billion in 2030.

According to the Congressional Budget Office’s baseline projection, the US budget deficit in 2030 will stand at $2.2 trillion.

This deficit figure is approximately ten times larger than the revenue increase projected under the Yale team’s most optimistic AI growth scenario.

“On the one hand, all else equal, faster productivity growth will yield more tax revenue,” wrote John Iselin and Ryan Nunn of the Yale Budget Lab. “On the other hand, our current tax system may not be structured to efficiently raise revenue from the economic activity AI creates.”

Speaking to Axios, Iselin added: “While we project that the growth of AI will increase tax revenues, without significant changes to how the US taxes capital income, the federal government will leave substantial revenue on the table.”

These projections are not definitive forecasts. The range of possibilities for how an AI boom might unfold and affect the fiscal landscape remains vast.

Axios highlights several critical questions:

How far will the labor share of income fall? How will this shift affect inequality among wage earners?

On the spending side, will the existing social safety net face massive liabilities to support displaced workers, or will job losses become so widespread that Congress is forced to offer more extensive aid than current laws dictate?

Tax policy is not set in stone. In a world where AI displaces human employment and the US faces a fiscal dilemma, Congress could consider shifting a greater share of the tax burden onto capital.

Ultimately, the objective is not to treat the Yale Budget Lab’s data as absolute truth. Rather, it is to demonstrate that the interaction between an AI-driven growth surge and federal tax revenues is not as direct or positive as those confronting an intractable deficit problem might hope.

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Anthropic reaches historic $1.5 billion settlement with authors in landmark AI copyright lawsuit

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Anthropic has reached a $1.5 billion settlement with a group of authors who accused the artificial intelligence company of using their books without authorization to train its Claude chatbot. The class-action settlement was approved by a federal court in San Francisco.

The agreement marks the first major lawsuit among dozens filed by rights holders against technology companies in the US to resolve with a significant settlement.

The authors initiated the lawsuit in 2024, accusing Anthropic of using pirated versions of their books to train its AI models without securing the necessary permissions.

According to a report by Reuters, Aparna Sridhar, Anthropic’s Head of Issues and Policy, said in a statement: “We reached this agreement in 2025. The settlement follows a landmark court ruling, which remains valid today, establishing that training artificial intelligence on books constitutes fair use under copyright law.”

Justin Nelson, an attorney representing the plaintiff authors, described the development as a “historic settlement.” Nelson added that the agreement reached with Anthropic could be considered the largest monetary payout in the history of copyright law.

Meanwhile, some authors and publishers declined to participate in the class-action lawsuit, choosing instead to file independent lawsuits against Anthropic. The judicial processes for these individual cases against the company are ongoing.

Prior to this development, Anthropic filed a lawsuit against the Pentagon in March to challenge an attempt by the US Department of Defense to blacklist the company on national security grounds.

In June, the US government decided to block foreign users from accessing the company’s most advanced AI models, Fable 5 and Mythos 5.

David Sacks, a US investor and Co-Chair of the President’s Council of Advisors on Science and Technology, explained that the restriction was implemented after it was discovered that the integrated safety mechanisms within the models could be bypassed.

Two weeks after that restriction was imposed, the US government restored access to the most powerful model, Mythos 5, for select American entities, including major corporations and government agencies.

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US voter support for Iran conflict collapses as fuel prices surge and midterm risks mount

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American voter support for the war in Iran is eroding rapidly following the collapse of the ceasefire process, with public opposition reaching historic levels in a remarkably short timeframe.

According to a survey conducted by Reuters/Ipsos, four out of five respondents anticipate that the hostilities will persist for a long time. Meanwhile, nearly half of those surveyed in a The Economist/YouGov poll estimate that the war will last for a year or longer. As public backing for the military campaign disintegrates, Donald Trump’s net approval rating for his decision to attack Iran has plummeted to minus 30%.

While it took six years of active involvement in the Vietnam War for public opposition to reach such a critical threshold, the war in Iran has generated a comparable level of rejection in just six months.

Speaking to The Economist, Larry Sabato of the University of Virginia emphasized that the key takeaway is not merely the depth of the opposition, but the unprecedented speed with which it has formed. Sabato noted that the conflict in Iran has registered the lowest level of public support of any American military engagement since polling on such interventions began, a trend that has remained constant since day one. Warning that a prolonged conflict will inevitably drive up costs, Sabato projected that this dynamic will translate into a severe political penalty for Trump and the Republican Party in the upcoming midterm elections.

Historically, US military interventions have initially enjoyed robust public support before gradually decaying over time. For instance, the US-led operations launched against the Taliban in Afghanistan in 2001 initially secured the backing of approximately 90% of the public.

At the time, President George W. Bush presented a clear, direct justification for the invasion, targeting the Taliban for harboring the terrorists responsible for the September 11 attacks. According to Gallup data, it took 13 years for public support for the occupation—which ultimately claimed the lives of more than 2,000 US service members and wounded another 20,000—to fall below 50%.

Economic consequences directly impact voters

Thus far, 17 US service members have been killed in Trump’s war in Iran. While this figure is low from a strictly military standpoint, the economic ramifications of the conflict have directly and rapidly disrupted the daily lives of American consumers.

The closure of the Strait of Hormuz, which was fully open prior to the military operations, has triggered a sharp rise in fuel prices. Although Trump has asserted that the US military presence has broken the regional blockade and enabled oil to flow at higher volumes than ever, concrete economic data does not support his claims.

The price of Brent crude oil has climbed from $72 to $88 per barrel since the beginning of July. In the domestic retail market, the average price of gasoline in the US has risen from approximately $3 per gallon before the war to nearly $4 per gallon.

Gallup historical data shows that during the Vietnam War, which involved large numbers of American ground troops, voters consistently identified the conflict as the most important problem facing the nation.

While the war in Iran has not yet been designated in those exact terms, voters consistently identify the high cost of living and a lack of leadership as their primary concerns in current polling. This shift indicates that despite the relatively low number of military casualties, the war in Iran is poised to become a major electoral liability for the Republican Party.

Support for the military campaign is also sharply polarized along political lines. Among Democratic voters, the net approval rating for the war stands at minus 84%, while among independents it rests at minus 52%.

Even within the “MAGA” Republican base—the only major demographic group to back the initiative, with a 72% approval rating—cracks are beginning to appear. According to a Washington Post/Ipsos poll, more than half of Trump’s core supporters indicated for the first time that they approve of his job performance only “partially” rather than “strongly.” Among Republicans who do not self-identify with the MAGA movement, support for the war has swung from a positive net approval of 26% in April to a net negative of 25%.

Budgetary debates in Congress

In response to the shifting public mood, Democratic lawmakers are intensifying their opposition. During the July 14 confirmation hearing for Jules Hurst, the nominee to oversee the Pentagon’s budget, Democratic senators accused the administration of systematically understating the financial toll of the conflict.

The Pentagon has put the cost of the war at approximately $30 billion, asserting that the figure primarily reflects spent munitions and fuel.

However, Senator Elissa Slotkin, a Democrat from Michigan, estimated that the true cost is more than six times that amount when factoring in the repair costs for American bases and the broader economic damages suffered by consumers. Slotkin also criticized the Pentagon’s commercial relationships with companies in which Trump’s sons hold business interests.

Conversely, Representative Mike Lawler, a Republican fighting to retain his seat in a highly competitive district in New York, dismissed the opposition’s criticisms as “nonsense.”

Lawler argued that Trump made a difficult but necessary decision to eliminate the threats posed by Iran’s nuclear program and its active sponsorship of terrorist groups. While acknowledging that he does not know how long the conflict will last, Lawler maintained that the Iranian regime is untrustworthy and only understands the language of military force. His Democratic opponent, military veteran Cait Conley, countered that Trump has dragged the United States into a conflict lacking clear military objectives or a viable exit strategy.

According to The Economist‘s midterm election forecasting model, Lawler faces a 68% probability of losing his seat in November. The same model projects an 82% probability that Democrats will win a majority in the House of Representatives, and a 45% chance of taking control of the Senate.

Aaron David Miller of the Carnegie Endowment for International Peace observed that Trump’s compounding difficulties in extricating the US from Iran recall the famous lament of former US President Lyndon Johnson during the Vietnam War: “I feel like a hitchhiker caught in a hailstorm on a Texas highway. I can’t run, I can’t hide, and I can’t make it stop.”

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