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Introduction TL;DR Meta Platforms will deploy Google’s custom Tensor Processing Units (TPUs) in its own data centers by 2027 and begin leasing TPU compute from Google Cloud as early as 2026. This announcement triggered Nvidia [finance:NVIDIA Corporation] stock to plunge 6.8% on November 25, 2025, with Advanced Micro Devices [finance:Advanced Micro Devices Inc.] falling as much as 9%, signaling a fundamental shift in AI infrastructure procurement. This is not merely a vendor change—it represents the disintegration of the semiconductor industry’s most critical monopoly and the emergence of a competitive, multi-vendor AI chip ecosystem. ...

Introduction TL;DR: AI-related stocks experienced significant declines in November 2025 due to China directives and executive warnings. Nvidia traded near $198 (down ~4%), while Palantir plummeted over 8%. Most alarmingly, 45% of global fund managers surveyed by Bank of America identified the AI bubble as the biggest tail risk to the global economy—a sharp increase from 33% just one month prior. This marks the first time in two decades that institutional investors have cited corporate overinvestment as a systemic concern. ...

Introduction TL;DR: In early 2025, Andrej Karpathy, a founding member of OpenAI, coined a term that perfectly captured the zeitgeist of modern software development: “Vibe Coding.” It describes a shift where developers stop writing code line-by-line and instead “give in to the vibes,” delegating the implementation entirely to AI. This post explores what Vibe Coding is, how it works, and why it represents a double-edged sword for the software industry. Vibe Coding is an AI-first approach where developers prompt LLMs and check if the output “vibes” (works), often without reading the underlying code. Tools like Cursor, Claude, and Bolt.new enable this loop of “Prompt -> Run -> Fix.” While it democratizes app creation and boosts speed, it risks creating unmaintainable, insecure codebases known as “spaghetti code.” What is Vibe Coding? Traditionally, coding required a deep understanding of syntax, logic, and libraries. Vibe Coding abstracts this away. As Karpathy tweeted, it involves “forgetting that the code even exists.” The developer acts less like an architect and more like a product manager, describing requirements in natural language and critiquing the result. ...

Introduction TL;DR: Google unveiled its Willow quantum chip achieving a 13,000x speedup over top supercomputers on scientific simulations. This milestone demonstrates verifiable quantum advantage. The chip accelerates AI-quantum computing fusion, promising innovation in science and industry, with Alphabet’s stock rising more than 5% after the announcement. Google’s Willow chip and Quantum Echoes algorithm excel in simulating quantum systems much faster than classical HPC, representing a step towards practical quantum computing. Willow Chip and Quantum Echoes Breakthrough Google Quantum AI team released results showing the ‘Quantum Echoes’ algorithm running on the Willow chip can solve complex quantum simulation problems 13,000 times faster than the best supercomputers on specific benchmarks. This represents a verifiable quantum advantage in scientific computing, marking a significant breakthrough beyond random sampling demonstrations. ...

Introduction TL;DR: Chinese open-source AI models such as Qwen, DeepSeek and GLM are rapidly spreading across Southeast Asia, the Middle East, Latin America and beyond, helped by low cost, flexible deployment and strong multilingual capabilities. In contrast, many U.S. and European executives still favor proprietary frontier models from OpenAI, Anthropic and Google’s Gemini 3 because of their benchmark performance and mature safety tooling, but this “build for perfection” mindset may limit their reach in cost-sensitive markets. Outside the U.S. and Europe, enterprises increasingly prioritize control over cost and data, which pushes them toward open-source Chinese models instead of top-tier proprietary systems. Alibaba’s Qwen series and other Chinese LLMs now power hundreds of thousands of derivative models, making them de facto infrastructure for local AI applications. Google’s Gemini 3 leads in reasoning, multimodal capabilities and agentic workflows, but its closed nature makes it harder for some regions to align with local data sovereignty and budget constraints. “Bridge powers” in Asia and other middle-income regions are exploring a mixed approach: combine Chinese open-source stacks with U.S. proprietary models to avoid technological dependence on either side. US Perfection vs Chinese Diffusion At the 2025 Fortune Innovation Forum in Kuala Lumpur, investors and operators repeatedly contrasted U.S. AI firms that “build for perfection” with Chinese players that “build for diffusion.” U.S. and European executives tend to value even an 8% performance edge on coding or reasoning benchmarks, arguing that such margins can decide whether an AI system clears the bar for large-scale deployment. ...

Introduction TL;DR: Anthropic released Claude Opus 4.5 on 2025-11-23 as its new flagship model, positioning it as one of the strongest options for coding, AI agents, and computer-use workflows. The model combines a 200K context window, hybrid reasoning modes, and improved vision, math, and coding to handle complex enterprise tasks and long-running automation. With input priced at 5 USD per million tokens and output at 25 USD per million tokens—about a 67% cut from the previous Opus generation—plus caching and batch discounts, it significantly lowers the cost of frontier intelligence. By emphasizing “practical efficiency” over pure benchmark bragging rights and offering broad cloud availability, Opus 4.5 marks Anthropic’s assertive entry into the frontier AI competition. Opus 4.5 appears throughout Anthropic’s documentation and media coverage as a premium model tuned for professional software engineers and knowledge workers, with key use cases around large-scale coding, agentic workflows, and high-stakes enterprise decision support. The launch messaging explicitly highlights cost-efficiency and agent capabilities alongside model quality, signaling a shift from experimental to production-first frontier AI. ...

Introduction TL;DR: Former Apple chief designer Jony Ive and OpenAI CEO Sam Altman publicly confirmed they’ve completed a working AI hardware prototype. The device is smartphone-sized but lacks a display, focusing on simple, playful, and intuitive design to spark user desire. Targeted for launch in less than two years, this project could reshape the future of AI devices, as confirmed by multiple major media and direct statements from the creators. The first public confirmation of a new class of “screenless” AI hardware from the combined efforts of Jony Ive and Sam Altman is generating significant interest in the tech community. ...

Introduction TL;DR: Quantum computers process information using quantum mechanics, operating with qubits that can represent 0, 1, or both at the same time. They leverage superposition, entanglement, and interference, allowing massive parallel computations for specific tasks. As a result, quantum computers can solve certain complex problems far faster than traditional computers. Quantum computing is a revolutionary paradigm that utilizes the fundamental principles of quantum mechanics. Unlike classical computers that use bits, quantum computers use qubits, capable of exhibiting both 0 and 1 states simultaneously. ...

Introduction TL;DR: Donald Trump’s push to supercharge US AI development and wipe away Biden-era regulations has caused an unexpected MAGA rift. Steve Bannon and allies warn that unregulated AI will destroy working class jobs and concentrate power among tech billionaires. Meanwhile, figures like Elon Musk argue that only unchecked AI growth will allow the US to outcompete China. This division now shapes the GOP’s internal tech policy debate. The MAGA base faces a crisis: advance AI leadership or protect American workers from what critics call a jobs apocalypse. The stakes have made AI one of the defining battles of the post-2024 conservative movement. ...

Introduction TL;DR: Gemini 3 was launched in November 2025, introducing Nano Banana Pro engine, reducing text rendering errors from 56% to 8%, with 4K image generation, real-time grounding for market and weather data, and over 80% community preference win rate. The new model sets the industry standard for high-quality AI images, prompt adherence, and reliable data integration. Release Background and Features Enhanced Reasoning and Integration Gemini 3 offers advanced reasoning and multimodal capabilities, outperforming previous models such as Gemini 2.5. It was integrated into Google services, with infrastructure at the South Carolina data center supporting deployment. ...