2026-06-09 Terça

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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.

marsbit05/10 09:38

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

marsbit05/10 09:38

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.

marsbit05/10 08:09

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

marsbit05/10 08:09

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.

marsbit05/10 07:37

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

marsbit05/10 07:37

In the Age of AI, the Organization Itself Is the Moat

In the AI era, where products, interfaces, and narratives are easily replicated, a company's true moat is its organizational structure. The article argues that exceptional companies like OpenAI, Anthropic, and Palantir differentiate themselves not merely through technology but by inventing new organizational forms that allow a specific type of talent to thrive and become a version of themselves they couldn't elsewhere. These companies compete on identity, offering ambitious individuals a sense of being special, chosen, close to power, and part of a historic mission. However, this emotional commitment must be matched by structural commitment—real power, ownership, status, and economic participation. For founders, the key question is not how to tell a better story, but what kind of person can only truly realize their potential within their specific company structure. For individuals evaluating opportunities, the distinction between "being chosen" (an emotional feeling) and "being seen" (a structural reality of tangible power and rewards) is crucial. The most dangerous promises are those priced in future time. While AI makes copying visible elements easy, it does not make building a great, novel organization any easier. The next frontier of competition is creating organizational vessels that attract, structure, and compound the judgment of the right people—those whom traditional boxes cannot contain. The company itself becomes the moat.

marsbit05/10 07:09

In the Age of AI, the Organization Itself Is the Moat

marsbit05/10 07:09

I've Been a Divorce Lawyer for 26 Years: How Has Cryptocurrency Become a New Tool for the Wealthy to Hide Assets?

Natalie Brunell reports on insights from divorce lawyer James Sexton, who has 26 years of experience. He argues that money itself is not the root of marital breakdown; rather, emotional disconnection is the core issue. While financial hardship increases divorce risk, excessive wealth can also make divorce easier by reducing the incentive to work on the relationship. Sexton discusses financial management in marriages, advocating for transparency and a "yours, mine, and ours" system that balances shared finances with individual autonomy and privacy. He notes the growing normalization of prenuptial agreements, especially among younger generations. A significant portion focuses on cryptocurrency's role in divorce. Sexton explains that crypto became a new tool for hiding assets due to its early anonymity and complexity. He highlights that many lawyers and spouses lack understanding, allowing knowledgeable parties to gain advantages. He cites a New York legal form that only added a specific crypto disclosure field in 2026. On saving relationships, Sexton emphasizes small, consistent acts of reconnection, affirmation, and expressing appreciation, which he finds more effective than criticism. He concludes that fostering warmth and kindness is a simple yet powerful way to strengthen bonds and, in his words, "put divorce lawyers out of business."

marsbit05/10 06:36

I've Been a Divorce Lawyer for 26 Years: How Has Cryptocurrency Become a New Tool for the Wealthy to Hide Assets?

marsbit05/10 06:36

Turing Award Laureate Sutton's New Work: Using a Formula from 1967 to Solve a Major Flaw in Streaming Reinforcement Learning

New research titled "Intentional Updates for Streaming Reinforcement Learning" (arXiv:2604.19033v1), involving Turing Award laureate Richard Sutton, addresses a core challenge in deep reinforcement learning (RL): the "stream barrier." Current deep RL methods typically rely on replay buffers and batch training for stability, failing catastrophically when learning online from single data points (streaming). The authors propose a fundamental shift: instead of prescribing how far to move parameters (a fixed step size), their "Intentional Updates" method specifies the desired change in the function's output (e.g., a 5% reduction in value prediction error). It then calculates the step size needed to achieve that intent. This idea is inspired by the Normalized Least Mean Squares (NLMS) algorithm from 1967. Applied to value and policy learning, this yields algorithms like Intentional TD(λ) and Intentional AC. The method inherently stabilizes learning by adapting the step size based on the local gradient landscape, preventing overshooting/undershooting. In experiments on MuJoCo continuous control and Atari discrete tasks, Intentional AC achieved performance rivaling batch-based algorithms like SAC in a streaming setting (batch size=1, no replay buffer), while being ~140x more computationally efficient per update. The work demonstrates significant robustness, reducing reliance on numerous stabilization tricks. A remaining challenge is bias in policy updates due to action-dependent step sizes. Overall, this approach advances efficient, online, "learn-as-you-go" RL, enabling adaptive systems without massive data buffers or compute clusters.

marsbit05/10 06:28

Turing Award Laureate Sutton's New Work: Using a Formula from 1967 to Solve a Major Flaw in Streaming Reinforcement Learning

marsbit05/10 06:28

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