Coinbase Launches Stock Trading While Crypto Prices Plummet — Why Is Industry So Worried?

ccn.comPubblicato 2025-12-18Pubblicato ultima volta 2025-12-18

Introduzione

Coinbase has expanded into stock trading and event-based prediction markets, allowing U.S. users to trade equities and ETFs commission-free within its app. The move, framed as part of its strategy to become a multi-asset platform, has sparked concern within the crypto community. Many interpret the timing—amid a sharp decline in crypto prices—as a defensive pivot rather than organic growth. Crypto markets have fallen over 30% from their all-time high, with analysts warning of further declines if key support levels break. Community reaction on social media has been mixed, with some viewing the expansion as a sign of capitulation in the crypto industry. CEO Brian Armstrong had previously teased the announcement as Coinbase’s "next chapter."

Coinbase unveiled a major expansion into stock trading and event-based prediction markets on Wednesday, a move that has intensified debate within the crypto community about the health of the digital asset industry as prices slide across major tokens.

The U.S.-listed exchange said the new offerings are part of its ambition to transform into a multi-asset platform serving a broader range of retail traders.

However, the timing of the launch has led some commentators to interpret the shift as a defensive pivot rather than organic growth.

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Coinbase Expands Beyond Crypto

Coinbase said it has begun rolling out commission-free stock trading for U.S. users, allowing customers to buy and sell equities and exchange-traded funds directly within its main app.

The company said users will be able to manage stock and crypto positions side by side and trade outside standard market hours.

Coinbase has also introduced event-based prediction markets, enabling users to take positions on the outcomes of real-world events such as elections, economic releases and sporting contests.

At launch, the contracts are linked to markets operated by Kalshi.

Community Pushback

Reaction from the crypto community was mixed, with several commentators on social media framing the announcement as a troubling signal for the sector.

“You know the crypto market is cooked when the biggest exchange in the U.S. pivots to stocks,” wrote one market commentator on X.

Other users questioned whether the move signaled capitulation across the industry.

“Everyone is pivoting. What’s happening?” one post read, while another suggested the announcement could mark a market low, calling it a “sign of bottom.”

Some traders also expressed concern that the new branches of the exchange would take away demand from crypto trading.

Crypto in the Red

Crypto prices have fallen sharply in recent weeks, with analysts warning that the sell-off may not yet be over as technical signals and macroeconomic risks continue to point lower.

“The crypto market has now declined more than 30% from its all-time high,” said CCN analyst Valdrin Tahiri, noting that total market capitalisation peaked near $4.27 trillion on Oct. 6 before reversing lower.

“The drop accelerated around Oct. 10, and since then the structure has been dominated by lower highs.”

While the market attempted a rebound in late November, Tahiri said the move lacked the characteristics of a sustainable recovery.

“The bounce on Nov. 21 looks corrective rather than impulsive,” he said.

“Price action is still contained within an ascending parallel channel, which usually forms during temporary relief rallies.”

That channel is now under threat.

“The market is at risk of closing below the channel’s support trendline,” Tahiri said. “If that happens, traders should be prepared for another sharp decline.”

He warned that losses could be steeper if selling accelerates.

“In a deeper retracement, a 30% decline toward the $2.05 trillion support level cannot be ruled out.”

For now, he added, “technicals and macro conditions are aligned to the downside, and the market remains vulnerable as December unfolds.”

Armstrong’s “Next Chapter” Finally Here

Coinbase Chief Executive Brian Armstrong had been hinting at an announcement arriving on Dec. 17, which he labeled the company’s “next chapter.”

The teaser had sparked widespread speculation online, with users debating whether the company would announce new tokens, expanded on-chain services or broader product offerings.

The company said at the time that all new features would be rolled out gradually, starting in the United States, with additional markets and products to follow.

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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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