Letture associate

2028: The Arrival of Recursive Self-Improvement (RSI)

**AI Recursive Self-Improvement (RSI): The Countdown to 2028 Begins** AI is no longer just a trained tool but is starting to rewrite its own evolutionary pace. According to Anthropic co-founder Jack Clark, there is a 60% probability that by the end of 2028, Recursive Self-Improvement (RSI) will become a reality. This means AI could autonomously design and build a more capable next-generation version of itself without any human researcher involvement—Claude 10 creating Claude 11, for instance. Supporting this timeline, Google DeepMind's CEO Demis Hassabis confirms that all leading AI labs are intensely focused on RSI, making it an industry-wide priority. He expresses profound concern, stating this potential is what keeps him awake at night. Concrete data underscores this acceleration: - METR evaluations show current top models like Claude are solving tasks up to the 16-hour limit of existing test frameworks. - In Epoch AI's challenging MirrorCode benchmark, Claude Opus 4.7 recreated complex software in hours for a fraction of the human cost. In one extreme test, AI autonomously coded for 19 days straight. - Anthropic reports over 80% of its codebase is now written by Claude, and researcher productivity has increased up to 8-fold since 2024. - OpenAI's policy blueprint highlights RSI as a major upcoming governance challenge. CEO Sam Altman reportedly hinted RSI might arrive within six months, potentially delaying OpenAI's massive IPO. The implication is an impending "intelligence explosion," where AI-driven progress outpaces human control. The central question is no longer if it will happen, but whether humanity is ready.

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2028: The Arrival of Recursive Self-Improvement (RSI)

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World Models, Metaverse, Digital Twins, Physical AI: Are They the Same Thing?

Title: World Models, the Metaverse, Digital Twins, Physical AI: Are They the Same Thing? The article clarifies that concepts like the metaverse, Web3, simulation platforms, digital twins, and Physical AI are not the same thing but are all part of the broader trend of blurring the lines between the digital and physical worlds. It positions "world models" as the foundational "cognitive layer" or "operating system" that enables AI to understand and simulate the world. Key distinctions are made: - The **Metaverse** is a destination for immersive social and economic experiences. World models could act as its "engine," generating interactive 3D content efficiently. - **Web3** focuses on decentralized ownership and economics (rules layer), operating on a different technical level than world models. - **Simulation Data Platforms** (e.g., for autonomous vehicles) are a 1.0 version, relying on manual design. World models represent a 2.0 version, using AI to generate realistic, varied scenarios autonomously. - **Digital Twins** create high-fidelity, real-time mirrors of physical systems (e.g., a factory). World models go a step further by enabling predictive simulation of future states. - **Physical AI** (robots, AVs) refers to AI that acts in the physical world. World models are a core component, providing the understanding and prediction needed for planning. A proposed hierarchy places world models at the cognitive layer, supported by infrastructure (compute, data) and supporting application tools (simulation, digital twins), action systems (Physical AI), user experiences (metaverse), and rules (Web3). In conclusion, while distinct, many of these previously hyped concepts may ultimately rely on advances in world model technology to fulfill their promises, as world models provide the essential cognitive foundation for simulating and interacting with complex environments.

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World Models, Metaverse, Digital Twins, Physical AI: Are They the Same Thing?

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"Shocking" CPO: How Does the Glass Bridge Actually Work? Detailed Explanation from Corning

Chinese CPO stocks plunged over 6% following Corning's announcement of its Glass Bridge platform at a Seoul tech conference. The new technology utilizes wafer-level glass ion-exchange waveguides for passive alignment between fibers and photonic chips, potentially simplifying traditional CPO architectures that rely on complex Fiber Array Units and active alignment equipment. This raised market concerns about reduced long-term demand for mid-stream CPO components. Corning's official documentation details Glass Bridge as a platform for fiber-to-PIC connectivity in NPO, CPO, and high-density modules. Its key features include wafer-level manufacturing for consistent, cost-effective production; a standardized, removable MT ferrule interface for ecosystem integration; and a separable high-density connector design supporting over 24 channels for assembly flexibility. Corning positions the technology as complementary to FAUs, addressing limitations in ultra-high-fiber-count scenarios. The market reaction reflects a broader reassessment of the AI optical interconnect value chain. Funds shifted from CPO and PCB manufacturing stocks towards glass substrate concept stocks like Kaisheng Technology and Dyer Laser. Analysts note glass substrates are seen as a next-gen advanced packaging material, offering a potential path for domestic industry differentiation amid AI-driven demand for high-performance, large-scale packaging, marking a structural migration in value towards upstream specialty materials.

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"Shocking" CPO: How Does the Glass Bridge Actually Work? Detailed Explanation from Corning

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A Group of Suzhou Engineers Unexpectedly Attain Financial Freedom

In Suzhou, a group of engineers from Lianxun Instruments, a leader in optical communication testing equipment, have achieved remarkable wealth after the company's IPO. Listed just two months ago on the STAR Market, the company's stock price surged approximately 30 times, making it the only A-share stock priced above 2,000 yuan. This surge created substantial fortunes for nearly 100 technical employees who held a collective 15.91% stake through employee stock ownership platforms, valued at over 36 billion yuan at the current market cap. Among them, nearly 40 became billionaires, while even the smallest holdings exceeded 5 million yuan in value. Founded in 2017 by Hu Haiyang, Yang Jian, and Huang Jianjun, Lianxun Instruments was established to address China's reliance on foreign high-end testing instruments. The company grew rapidly with a strong focus on R&D, where technical staff make up nearly 80% of its workforce. Early implementation of employee stock plans helped retain this core talent. The company's explosive growth is fueled by booming AI computing demand, with clients including major global optical module leaders. Its revenue skyrocketed from 276 million yuan in 2023 to 1.194 billion yuan in 2025, turning a profit in 2024. The IPO has also generated massive returns for early investors, including Suzhou's state-owned capital, which saw a hundredfold return. This story reflects a broader trend in China's markets, where technology firms in AI, semiconductors, and optics are creating new wealth, rewarding engineers and technical teams who are now central to modern capital-driven success stories, marking a shift from previous eras dominated by internet and real estate tycoons.

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A Group of Suzhou Engineers Unexpectedly Attain Financial Freedom

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