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Nvidia’s $5.5B Hit: US Export Ban On AI Chips To China Shakes Global AI Race

Nvidia just took a $5.5 billion punch to the balance sheet—courtesy of the U.S. government’s latest move to tighten the leash on AI chip exports to China. The company’s most advanced processor available in the Chinese market, the H20, has now fallen under indefinite export restrictions, triggering a 6% slide in Nvidia shares in after-hours trading.

The decision, announced Tuesday, marks a major escalation in the U.S.-China tech standoff and underscores Washington’s growing concern over how AI hardware could fuel China’s supercomputing ambitions. The U.S. Commerce Department has now slapped licensing requirements not only on Nvidia’s H20, but also on AMD’s MI308 and similar chips. AMD shares dropped 7% after the news.

A Commerce Department spokesperson said the move reflects President Biden’s directive to safeguard U.S. national and economic security. Nvidia, meanwhile, confirmed the charges would cover unsold H20 inventory, outstanding purchase commitments, and related reserves.

A Workaround, Now Blocked

Nvidia had designed the H20 chip specifically to navigate around previous U.S. export limits—delivering toned-down performance but retaining high-speed interconnectivity. That design made the H20 attractive for AI inference tasks, an increasingly dominant segment of the market where models provide real-time answers rather than undergoing initial training.

Despite not being as powerful as Nvidia’s top-tier chips sold outside China, the H20 gained traction with major Chinese tech players including Tencent, Alibaba, and ByteDance. Reuters previously reported that demand surged after startups like DeepSeek ramped up development of low-cost AI models.

But that very design—optimized for high-bandwidth memory access and chip-to-chip connectivity—set off alarm bells in Washington. Analysts argue it still carries supercomputing potential, especially if deployed at scale.

“Likely In Violation”

A Washington, D.C.-based think tank, the Institute for Progress, didn’t mince words. In a statement Tuesday, it claimed that Tencent had already installed H20 chips in a facility likely used to train large AI models—potentially breaching U.S. export restrictions already in place. The group added that DeepSeek’s infrastructure, used for its latest V3 model, might also be in violation.

U.S. restrictions on chips used in supercomputing have been in effect since 2022. Now, the H20 is joining that list. Nvidia said it was formally notified on April 9 that the chip would require an export license—and on April 14, that the restriction would be indefinite. Whether the U.S. will issue any such licenses remains unclear.

A Fork In The Road

This latest move throws a wrench into Nvidia’s China strategy, just as demand in the region for generative AI tools is accelerating. It also highlights the growing friction between global innovation and geopolitical control—a tension Nvidia CEO Jensen Huang must now navigate carefully.

The setback comes one day after Nvidia unveiled plans to invest up to $500 billion into U.S.-based AI server infrastructure, working with partners like TSMC to align with American industrial policy.

Now, as Nvidia absorbs the financial blow and recalibrates, one thing is clear: the AI chip race isn’t just about performance anymore. It’s a front line in the broader battle over who controls the future of intelligent computing.

How Venture Capital Can Help Create Startup Fraud

Fraud Is Often A System Problem, Not Just A Founder Problem

A new report from Imperial College London and Emlyon Business School examines how venture capital-backed founders commit fraud and how investors can unintentionally create the conditions for it.

Published in June, the study draws on cases pursued by the U.S. Securities and Exchange Commission and the Department of Justice between 2000 and 2023. Its central conclusion is that fraud is not solely a founder problem, but can also stem from the incentives, expectations and governance structures surrounding startups.

High Expectations, Higher Risks

Several high-profile cases, including Charlie Javice of Frank, Gökçe Güven of Kalder, Do Kwon of Terraform Labs, and Alexander and Valerie Lau Beckman of GameOn, have intensified debate over where ambitious fundraising ends and fraud begins.

“Fraud is much more common and normalized in the startup world than we are ready to admit and accept,” Tim Weiss, one of the report’s authors, told TechCrunch.

Weiss also cited a University of Toronto study covering 654 fraud cases involving U.S. venture-backed startups between 2000 and 2023. Although fraud remained relatively rare, venture-backed companies were more likely to face fraud charges than non-VC-backed firms, while startups launched during overheated investment markets were 19% more likely to commit fraud later.

According to Weiss, pressure from investors and boards to deliver rapid growth can encourage misconduct, particularly in fast-moving sectors such as artificial intelligence.

The Three Stages Of “Façading”

The report, co-authored by Weiss and Nevena Radoynovska, identifies a three-stage process the authors call “façading.”

Surface façading begins with exaggerated claims about a company’s progress or traction. Reinforced façading involves creating evidence to support those claims, including fabricated contracts, invoices or revenue records. Deep façading extends the deception to the product itself through fake demonstrations and staged proof points.

Rather than beginning with a single act of fraud, the report argues that misconduct often develops gradually as founders attempt to sustain increasingly unrealistic expectations.

Investors Also Shape The Conditions For Fraud

One of the report’s central arguments is that investors are not always passive victims of founder misconduct. In some cases, they help create the conditions in which fraud becomes more likely.

According to the researchers, venture capital can “co-create fraud” by continuing to back founders who have previously been accused of misconduct, signaling that such behavior carries few long-term consequences. A separate University of Toronto study found little evidence that founders accused of fraud struggle to raise funding for new ventures, even when earlier cases attracted significant media attention.

“New investors and the broader VC market do not penalize past misconduct,” the report said, linking that pattern to Silicon Valley’s long-standing tolerance for failure.

Governance Plays A Critical Role

The University of Toronto study also identified governance as a key factor. Startups with founder-controlled boards were twice as likely to commit fraud as companies with investor-controlled or shared-control boards.

It also found that venture-backed companies going public were more likely to face securities class-action lawsuits within two years than private equity-backed firms. As startups remain private for longer while raising larger funding rounds, Weiss argues that governance has not kept pace with their growing scale.

“Founders do not have a professional body or association that could govern or enforce rules of entrepreneurial and investor conduct on how to be a good founder and what reasonable growth expectations are,” he said.

Calls For Stronger Oversight

Weiss argues that regulators should take a more proactive approach by introducing routine investigations and formal audits once startups reach significant funding thresholds, rather than waiting for whistleblower complaints or investor lawsuits.

The report also calls on investors to accept greater responsibility when aggressive growth targets contribute to governance failures. According to the authors, stronger oversight by both regulators and investors would help reduce the conditions in which fraud can develop.

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