US AI Startups Are All Using Chinese Large Models | Rewire Morning News

marsbitPubblicato 2026-03-24Pubblicato ultima volta 2026-03-24

Introduzione

U.S. AI Startup Reliance on Chinese Models & Key Tech Updates NVIDIA CEO Jensen Huang declared on a podcast that AGI has been achieved, citing open-source platforms like OpenClaw as evidence, while simultaneously defending AI-generated content against criticism. A U.S.-China security review report revealed that about 80% of American AI startups are using Chinese open-source models from companies like Alibaba, Moonshot AI, and MiniMax, which dominate global rankings. This dependency is seen as a self-reinforcing competitive advantage for China. In a specific case, the $29.3 billion coding tool Cursor was found to be using Moonshot's Kimi model without disclosure. Meanwhile, the Pentagon labeled Anthropic a "supply chain risk," drawing political criticism. In energy, the IEA warned the Iran crisis has caused a larger daily oil supply loss than the 1970s shocks, with Russia benefiting as oil prices surge. BlackRock's CEO warned AI will worsen wealth inequality and proposed a government retirement fund and tokenization to broaden market access, aligning with the firm's business interests. Sam Altman stepped down as chairman of Helion Energy to avoid a conflict of interest as OpenAI negotiates a power purchase agreement for fusion energy, a highly ambitious bet given fusion is not yet commercialized. Other notable updates: Trump established a fund to reduce foreign chip reliance; prediction market CEOs invested in a new VC fund despite regulatory challenges; Luma AI released ...

Jensen Huang announced on a podcast that "we have achieved AGI," Eighty percent of US AI startups are running Chinese open-source models. The technological revolution is accelerating, and so are the cracks.

1| Jensen Huang Announces "I Think We Have Achieved AGI," Then Spends Half an Hour Explaining Why AI-Generated Content Isn't Trash

NVIDIA CEO Jensen Huang made a judgment on the Lex Fridman podcast that no one in the industry dares to utter lightly. When Fridman asked, "How far are we from an AI that can found and run a billion-dollar company?" Huang replied, "I think it's now. I think we have achieved AGI." He cited the popularity of the open-source Agent platform OpenClaw as evidence, while also admitting that such systems might create short-term value rather than lasting enterprises.

This statement sets him apart from his peers who have been busy distancing themselves from the term AGI in recent months. OpenAI and Microsoft have clauses in their contracts triggered upon the achievement of AGI, and Huang chose this moment to shout it out. The person announcing AGI sells GPUs; his motives need no guessing.

In the same episode, he also defended DLSS 5. This generative graphics enhancement technology, collectively criticized by the gaming community as "AI garbage," was called an "artist-guided optional enhancement" by Huang. Announcing that AGI has arrived while explaining why AI-generated content isn't trash—this juxtaposition is the most precise snapshot of the current AI narrative.

(Source: Lex Fridman Podcast / The Verge / Ars Technica / Tom's Hardware)

2| Eighty Percent of US AI Startups Are Running Chinese Models, But the Pentagon Aims Its Guns at Anthropic

A report from the US-China Economic and Security Review Commission provided a glaring number: approximately 80% of US AI startups are using Chinese open-source models. Models from Alibaba, Moonshot AI, and MiniMax already dominate the global rankings on HuggingFace and OpenRouter. The committee warned that this is forming a "self-reinforcing competitive advantage," with the open-source ecosystem and manufacturing data creating a dual cycle, allowing them to approach the cutting edge even under chip restrictions.

Last week's Cursor incident is the most specific case. When the AI programming tool, valued at $29.3 billion, launched Composer 2 and claimed a self-developed breakthrough, developers discovered through API debugging within hours that the underlying model was Moonshot AI's Kimi K2.5. The co-founder admitted that not disclosing the base model was a mistake.

Meanwhile, the Pentagon labeled Anthropic a "supply chain risk." Senator Warren sent a letter to the Defense Secretary calling this a "retaliatory act," pointing out that the contract could simply be terminated without using a punitive label. The real supply chain risk isn't in Anthropic's contract terms; it's in the model dependencies of 80% of startups.

(Source: US-China Economic and Security Review Commission / VentureBeat / TechCrunch / Reuters)

3| IEA Chief: Iran Crisis More Severe Than the Two 1970s Oil Shocks Combined, Putin is the Biggest Winner

IEA Chief Fatih Birol gave a quantitative comparison at the Australian National Press Club. The 1973 and 1979 oil crises together caused a global loss of about 10 million barrels per day in supply; the current Iran crisis is about 11 million barrels per day. Natural gas losses are about 140 billion cubic meters, nearly double that of the Russia-Ukraine conflict. Energy assets in at least 40 locations across 9 Middle Eastern countries are severely damaged. Chevron's CEO was more direct at CERAWeek, saying oil prices "have not yet fully reflected" the actual shortage.

The biggest beneficiary of this crisis is not in the Middle East. According to CREA data, Russia's fossil fuel exports brought in about $7 billion in the first two weeks of March. Urals crude soared from about $57/barrel to nearly $100, almost on par with Brent, erasing the long-term discount. Trump's 30-day sanctions waiver (expiring April 11th) allows countries to purchase Russian oil already in transit. Treasury Secretary Yellen claimed it would not bring "significant financial benefit," while analysts called the restriction "almost unenforceable."

(Source: IEA / Fortune / Al Jazeera / CNBC / Guardian / CREA)

4| Fink's Annual Letter: AI Will Widen Wealth Inequality, the Cure is to Get More People Investing

BlackRock CEO Larry Fink placed AI at the center of the inequality narrative in his annual letter to investors. His core argument: the vast wealth created in past generations primarily flowed to those who already held financial assets, and the AI boom will accelerate this trend. If market participation isn't broadened, the dividends will only make the rich richer.

Fink's solution comes with a clear product logic. He suggested establishing a government retirement investment fund of about $1.5 trillion, operating in parallel with the existing Social Security trust fund. He also pointed to tokenization as a key tool for expanding market access. This happens to be BlackRock's core business bet in recent years. When the person managing $11.6 trillion in assets says "get more people investing," it translates to "get more people's money flowing into the products I manage."

The signal isn't just from BlackRock. On the same day, Bloomberg reported that JPMorgan launched a new tool to help clients hedge AI-related debt risks. When Wall Street starts pricing hedging products for an AI bubble, the threshold of being "big enough to short" is not far away.

(Source: BlackRock Annual Letter / CNN / Reuters / Bloomberg)

5| Altman Steps Down as Helion Board Chairman, OpenAI Negotiates Nuclear Fusion Power Purchase Agreement

Sam Altman has stepped down as Chairman of the board of nuclear fusion company Helion Energy to allow OpenAI to negotiate a power purchase agreement as an independent buyer. According to TechCrunch, the proposed agreement would give OpenAI 12.5% of Helion's total power generation, corresponding to 5 GW by 2030 and 50 GW by 2035.

A few weeks ago, Altman admitted "data centers are hard" after a Texas data center outage, and subsequently pulled back from infrastructure self-building. The signal is clearer now: OpenAI does not intend to solve the energy bottleneck within the traditional grid framework but is betting on nuclear fusion, which is not yet commercialized. The 2030 target of 5 GW means Helion needs to go from the lab to large-scale power generation in less than four years—something no nuclear fusion company has ever achieved.

Altman previously served simultaneously as Helion Chairman and OpenAI CEO; the decision-maker for the seller and the buyer was the same person. Resignation was a prerequisite for the deal to proceed and means commercialization is near enough to require handling conflicts of interest. Betting on nuclear fusion looks less like science fiction and more like a hedge when traditional energy is losing 11 million barrels per day due to the Iran crisis.

(Source: TechCrunch / CNBC / Axios)

Also Worth Knowing ↓

According to the New York Times, Trump established a "Pax Silica" fund to reduce dependence on foreign chips. This multilateral framework already includes eight countries including the US, Japan, South Korea, and Singapore, with India joining in February. On the same day, Musk announced that SpaceX and Tesla will build an advanced chip factory in Austin. (Source: NYT / Reuters)

The CEOs of Kalshi and Polymarket jointly invested in a $35 million prediction market VC fund on the same day the Senate introduced a bipartisan bill to ban sports prediction on prediction markets. The fund is named 5c(c) Capital, founded by an early Kalshi employee. The two companies are fierce product competitors but reached an agreement on betting on the sector. (Source: Fortune / TechCrunch / WSJ)

Luma AI released the Uni-1 image generation model, surpassing Google and OpenAI on several benchmarks with 30% lower cost. Google's Nano Banana series had been the undisputed leader for months; Luma entered the image generation赛道 from video tools and directly rewrote the rankings. (Source: VentureBeat)

Apple announced WWDC 2026 will open on June 8, teasing "AI progress." After last year's visual redesign, Apple Intelligence needs to deliver on the long-delayed Siri upgrade promise. (Source: TechCrunch / The Verge)

Strategy restored potential Bitcoin purchasing power to $42 billion, purchasing $76 million worth of BTC last week through the sale of common stock. The previous week, it bought using $1.6 billion raised from STRC preferred stock financing. The buying pace hasn't slowed due to market turmoil. (Source: CoinDesk / Fortune)

According to Bankless, two US senators and the White House have reached a "principled agreement" on stablecoin yield provisions. This is another step in the transition from "regulation by enforcement" to "regulation by rules" for crypto oversight. (Source: Bankless)

Domande pertinenti

QWhat did NVIDIA CEO Jensen Huang announce regarding AGI on the Lex Fridman podcast?

AJensen Huang announced that he believes AGI has already been achieved, citing the popularity of the open-source Agent platform OpenClaw as evidence, though he acknowledged such systems may create short-term value rather than lasting enterprises.

QAccording to the U.S.-China Economic and Security Review Commission report, what percentage of U.S. AI startups are using Chinese open-source models?

AApproximately 80% of U.S. AI startups are using Chinese open-source models, with models from companies like Alibaba, Moonshot AI, and MiniMax dominating global rankings on platforms like HuggingFace and OpenRouter.

QWhat did IEA Executive Director Fatih Birol say about the severity of the current Iran crisis compared to historical oil shocks?

AFatih Birol stated that the current Iran crisis is more severe than the combined impact of the 1973 and 1979 oil crises, causing a daily loss of about 11 million barrels of supply compared to the historical total of 10 million barrels.

QWhat solution did BlackRock CEO Larry Fink propose in his annual letter to address the wealth inequality exacerbated by AI?

ALarry Fink proposed creating a government retirement investment fund of approximately $1.5 trillion to operate alongside the existing Social Security trust fund, and pointed to tokenization as a key tool for expanding market access.

QWhy did Sam Altman step down as chairman of Helion Energy's board?

ASam Altman stepped down as chairman of Helion Energy's board to allow OpenAI to negotiate a power purchase agreement as an independent buyer, avoiding a conflict of interest as the decision-maker for both the seller and buyer.

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U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

U.S. Government Bans Foreign Access to Fable 5, Anthropic Issues Rebuttal On June 12th, the U.S. government ordered AI company Anthropic to immediately suspend all foreign access—including foreign nationals within the U.S. and Anthropic's own foreign employees—to its newly released Fable 5 and Mythos 5 AI models, citing national security concerns. This forced Anthropic to temporarily disable access to both models for all users globally, as it cannot technically differentiate user nationality at scale. The models, released just three days prior, represent Anthropic's highest public capability tier. Fable 5 is the first publicly available model from the advanced "Mythos" family, while Mythos 5 is a less-restricted version for approved cybersecurity and critical infrastructure partners. The government's directive was reportedly triggered by claims from another company that it could "jailbreak" Mythos 5, raising alarm within the Trump administration. Anthropic, in a detailed public statement, strongly challenged this rationale. The company argues the demonstrated "jailbreak" is a narrow, non-generalized technique that merely involves identifying minor, known software vulnerabilities—a capability common to other publicly available models like OpenAI's GPT-5.5 and routinely used by cybersecurity defenders. Anthropic stated it has complied with the order but disagrees with the government's standard, warning that applying it industry-wide would halt all new frontier model deployments. The company criticized the lack of a transparent, fact-based legal process and expressed confidence the situation stems from a misunderstanding. It is working to restore access and will release more technical details within 24 hours. Other Anthropic models remain unaffected.

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U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

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The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

**Raydium Exploit Reveals DeFi's Hidden Risk: Forgotten "Zombie" Contracts** A recent attack on Raydium's deprecated V3 AMM pools resulted in a loss of approximately $1.34 million. The hacker exploited pools that were no longer supported by Raydium's current UI or SDK but remained fully functional and accessible on-chain. This incident highlights a critical, often overlooked category of risk in DeFi: inactive or legacy smart contracts that projects fail to properly decommission. Since March 2025, there have been at least 8 publicly reported attacks targeting such abandoned contracts, with total losses around $10.8 million. Including older pools and deprecated features, the count rises to 10 incidents with roughly $22.5 million in losses. These "zombie contracts" represent a lifecycle management failure rather than a code vulnerability, yet they are typically misclassified under general "code bug" categories in security reports, masking the true scale of the problem. The root cause is that projects often merely document a contract as "deprecated" without taking essential technical steps to secure it: withdrawing remaining assets, disabling external call functions, and implementing ongoing monitoring. These forgotten, under-monitored components become prime targets for attackers. To address this, the industry needs to recognize "zombie contracts" as a distinct risk category and establish standardized decommissioning protocols. Essential steps should include: 1) a formal retirement announcement, 2) removal of all front-end integrations, 3) withdrawal of locked assets, 4) disabling key contract functions, 5) ongoing security monitoring, 6) clear user communication, and 7) a post-mortem analysis. The value of a DeFi project lies not only in its current TVL but also in the security of its historical codebase, which has now become a new attack surface.

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The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

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Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

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Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

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