Acting CFTC chair to join MoonPay after leaving agency

cointelegraphPubblicato 2025-12-17Pubblicato ultima volta 2025-12-17

Introduzione

Caroline Pham, acting chair of the U.S. Commodity Futures Trading Commission (CFTC), will leave the agency to join crypto payments firm MoonPay as chief legal and administrative officer. Her departure follows the Senate confirmation of her successor. Pham, the sole Republican commissioner, became acting chair in January. She had initially planned to leave after Brian Quintenz’s confirmation, but his nomination was withdrawn. Pham is the latest high-level regulator to move into the crypto industry, following former CFTC Commissioner Summer Mersinger, who joined the Blockchain Association. During her tenure, Pham’s agenda aligned with White House directives, and she launched initiatives like the Crypto CEO Forum. The move highlights concerns about the "revolving door" between regulators and the crypto industry, as previously criticized by Senator Elizabeth Warren.

Caroline Pham, the acting chair of the US Commodity Futures Trading Commission, will leave the financial regulator to join MoonPay, following the Senate confirmation of her successor.

In a Wednesday X post, MoonPay confirmed reports that Pham would join its team as a chief legal and administrative officer. She became acting chair in January amid the changeover in presidential administrations and has been the sole Republican commissioner at the CFTC for months, following the end of other leaders’ terms and resignations.

Pham said in May that she planned to leave the CFTC following the Senate confirmation of Brian Quintenz, US President Donald Trump’s first pick to replace her as chair. However, after a pushback from Gemini co-founders Cameron and Tyler Winklevoss, the White House withdrew Quintenz’s nomination and later named Securities and Exchange Commission official Michael Selig as the president’s pick for CFTC chair.

The acting CFTC chair would not be the first person in a high-level regulatory position to immediately move into a role with the crypto industry. Summer Mersinger, another CFTC commissioner, left the agency in May to become the CEO of the Blockchain Association, a crypto advocacy group.

Related: Exodus, MoonPay to roll out stablecoin in early 2026, joining gold rush

During her time as acting chair, Pham’s agenda was consistent with White House directives, including those related to the cryptocurrency industry. She reported in September that the CFTC had taken only 18 actions while she was in charge, and no enforcement cases.

The acting chair also launched the Crypto CEO Forum and CEO Innovation Council, which included leaders from crypto companies.

US Senator calls out crypto industry for ‘revolving door’ hiring strategies

Before Mersinger joined the Blockchain Association and MoonPay announced Pham would accept a role after her departure from the CFTC, Massachusetts Senator Elizabeth Warren suggested that some government officials could be laying the groundwork to “audition” for lobbying and regulatory positions at crypto companies and organizations.

Warren signed onto a 2022 letter with several other lawmakers raising similar concerns about public officials’ priorities while in office. The letter cited reports claiming that “over 200 government officials,” including members of Congress and White House staff, had taken positions as advisers, board members, investors, lobbyists, legal counsel and executives at crypto companies.

Magazine: When privacy and AML laws conflict: Crypto projects’ impossible choice

Letture associate

Gensyn AI: Don't Let AI Repeat the Mistakes of the Internet

In recent months, the rapid growth of the AI industry has attracted significant talent from the crypto sector. A persistent question among researchers intersecting both fields is whether blockchain can become a foundational part of AI infrastructure. While many previous AI and Crypto projects focused on application layers (like AI Agents, on-chain reasoning, data markets, and compute rentals), few achieved viable commercial models. Gensyn differentiates itself by targeting the most critical and expensive layer of AI: model training. Gensyn aims to organize globally distributed GPU resources into an open AI training network. Developers can submit training tasks, nodes provide computational power, and the network verifies results while distributing incentives. The core issue addressed is not decentralization for its own sake, but the increasing centralization of compute power among tech giants. In the era of large models, access to GPUs (like the H100) has become a decisive bottleneck, dictating the pace of AI development. Major AI companies are heavily dependent on large cloud providers for compute resources. Gensyn's approach is significant for several reasons: 1) It operates at the core infrastructure layer (model training), the most resource-intensive and technically demanding part of the AI value chain. 2) It proposes a more open, collaborative model for compute, potentially increasing resource utilization by dynamically pooling idle GPUs, similar to early cloud computing logic. 3) Its technical moat lies in solving complex challenges like verifying training results, ensuring node honesty, and maintaining reliability in a distributed environment—making it more of a deep-tech infrastructure company. 4) It targets a validated, high-growth market with genuine demand, rather than pursuing blockchain integration without purpose. Ultimately, the boundaries between Crypto and AI are blurring. AI requires global resource coordination, incentive mechanisms, and collaborative systems—areas where crypto-native solutions excel. Gensyn represents a step toward making advanced training capabilities more accessible and collaborative, moving beyond a niche controlled by a few giants. If successful, it could evolve into a fundamental piece of AI infrastructure, where the most enduring value in the AI era is often created.

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Why is China's AI Developing So Fast? The Answer Lies Inside the Labs

A US researcher's visit to China's top AI labs reveals distinct cultural and organizational factors driving China's rapid AI development. While talent, data, and compute are similar to the West, Chinese labs excel through a pragmatic, execution-focused culture: less emphasis on individual stardom and conceptual debate, and more on teamwork, engineering optimization, and mastering the full tech stack. A key advantage is the integration of young students and researchers who approach model-building with fresh perspectives and low ego, prioritizing collective progress over personal credit. This contrasts with the US culture of self-promotion and "star scientist" narratives. Chinese labs also exhibit a strong "build, don't buy" mentality, preferring to develop core capabilities—like data pipelines and environments—in-house rather than relying on external services. The ecosystem feels more collaborative than tribal, with mutual respect among labs. While government support exists, its scale is unclear, and technical decisions appear driven by labs, not state mandates. Chinese companies across sectors, from platforms to consumer tech, are building their own foundational models to control their tech destiny, reflecting a broader cultural drive for technological sovereignty. Demand for AI is emerging, with spending patterns potentially mirroring cloud infrastructure more than traditional SaaS. Despite challenges like a less mature data industry and GPU shortages, Chinese labs are propelled by vast talent, rapid iteration, and deep integration with the open-source community. The competition is evolving beyond a pure model race into a contest of organizational execution, developer ecosystems, and industrial pragmatism.

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3 Years, 5 Times: The Rebirth of a Century-Old Glass Factory

Corning, a 175-year-old glass company, is experiencing a dramatic revival as a key player in AI infrastructure, driven by surging demand for high-performance optical fiber in data centers. AI data centers require vastly more fiber than traditional ones—5 to 10 times as much per rack—to handle high-speed data transmission between GPUs. This structural demand shift, coupled with supply constraints from the lengthy expansion cycle for fiber preforms, has created a significant supply-demand gap. Nvidia has invested in Corning, along with Lumentum and Coherent, in a $4.5 billion total commitment to secure the optical supply chain for AI. Corning's competitive edge lies in its expertise in producing ultra-low-loss, high-density, and bend-resistant specialty fiber, which is critical for 800G+ and future 1.6T data rates. Its deep involvement in co-packaged optics (CPO) with partners like Nvidia further solidifies its position. While not the largest fiber manufacturer globally, Corning's revenue from enterprise/data center clients now exceeds 40% of its optical communications sales, and it has secured multi-year supply agreements with major hyperscalers including Meta and Nvidia. Financially, Corning's optical communications revenue has surged, doubling from $1.3 billion in 2023 to over $3 billion in 2025. Its stock price has risen nearly 6-fold since late 2023. Key future catalysts include the rollout of Nvidia's CPO products and the scale of undisclosed customer agreements. However, risks include high current valuations and potential disruption from next-generation technologies like hollow-core fiber. The company's long-term bet on light over electricity, maintained even through the telecom bubble crash, is now being validated by the AI boom.

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