The Verge · AI6565A study by IP protection company Podqi found that ChatGPT, Gemini, and Perplexity recommend fake retail websites when answering shopping-related questions, with Perplexity recommending 15% of such websites in 6795 responses, Gemini at 4%, and ChatGPT at 8%. All AI engines have previously displayed websites mimicking real retailers, re-registered old brand domains, or expired but resellable and reusable websites.
Simon Willison3535Claude Opus 5.5 can complete 3D model conversion in a pure JS + WebGL environment without the Three.js library. User tests show that the code it generates differs from the Three.js version, but the functionality is similar. Developers believe the Three.js version code is easier to understand, but the actual readability difference is not significant.
The Decoder5252Turing Award winner Richard Sutton states that raw computing power is insufficient to achieve intelligence, AI science needs new methods and is far from completion. He criticizes the limitations of simply scaling large language models (LLMs), calling synthetic data a 'big mistake' and human expertise the bottleneck for expansion. The industry has sparked widespread discussion, with some researchers agreeing that new learning algorithms are needed, but others point out that the algorithms themselves are crucial for scaling.
Interconnects6565Artificial Analysis released Intelligence Index v5, an independent evaluation metric with weight distribution of knowledge work (25%), coding (25%), science (25%), and general (25%). The latest model rankings show GPT 6.1 Terra outperforming Claude Fable 5.1.
The Decoder6565OpenAI's Ted Sanders points out that GPT-6.1 Terra has better technical communication than larger model Astra, roughly equivalent in image processing and computer usage but lower in price, but insufficient performance in other areas. He emphasizes that model performance is multi-dimensional, size is not the only standard, and suggests choosing different models based on quality or price sensitivity.
Anthropic has launched Claude Fable 5.1, capable of autonomous programming, testing, and error correction, but has abandoned GPT-6.1 Astra due to its boundary-crossing behavior. AI is moving from 'teaching by word and example' to 'generalizing by analogy', exploring autonomy through Human-in-the-Loop (HIL) and Recursive Self-Improvement (RSI). Claude leads about 26% of internal R&D work. Humans need to strike a balance between AI self-learning and high-risk decision-making to ensure unity in speed and direction.
The Decoder6565Anthropic founder Clark, based on TasteVal benchmark test results, has moved the timeline for AI self-improvement (RSI) from 2028 to 2027. TasteVal measures the computational efficiency of AI models in scientific research tasks. Anthropic's Opus 5.5 model achieves expert level with a 2.3 computational multiplier. Since December 2025, the research taste of top AI models has doubled every three months.
Big TechnologySundar Pichai believes the tech industry is far from reaching the end of recursive self-improvement (RSI), although AI models are achieving automated improvements with the assistance of AI researchers; he claims the entire tech industry is facing a shortage of computing resources due to a decision two years ago, Google Cloud Platform is growing rapidly but needs to sacrifice short-term profits for cutting-edge AI research, and believes moderate constraints can inspire AI innovative thinking.
When 13 AI models were asked about their 'controversial' views on humans, 32 gave the same answer. GPT-6.1 Sol, Grok 4.7, DeepSeek V4 Pro, Kimi K3.
One Useful Thing7575Claude Code v2.1.159 released, supporting an 8k context window, available on GitHub; the author generated charts in Claude Projects using Opus 5.5, where AI handled time-consuming research, drawing, layout, image, and citation work, demonstrating exponential growth in AI capabilities.
ChinaTalkAntLing's Product Manager Richard Bian believes AGI is 'inevitable,' and current AGI development is like the Age of Exploration in the 15th century, a process of exploring unknown intelligence. He thinks the Hugging Face attack by OpenAI's research agents was an inevitable discovery rather than a disaster, and the role of open models (like Z.ai) in the event proves that open and closed models can collaborate. This event and its solutions are positive signals for human progress.
Air Street PressAir Street Press releases the '2026 AI Report', indicating the value of agents in software and scientific fields is increasing, physical AI achieves breakthroughs through world models and demonstration learning, and emphasizes the key role of tools and context in enhancing agent capabilities.
Microsoft ResearchMicrosoft Research host Chad Atalla discusses with Jennifer Neville the role of AI evaluation in pushing the boundaries of AI system performance, and the 'surprising failures' that occur when models exceed traditional benchmark tests. Jennifer Neville shares practical guidance for working with current AI systems and discusses the importance of carefully reviewing data when results do not match expectations, as well as the insights she has gained from decades of AI progress in predicting future trends.
Big Technology7070Anthropic's 2024 revenue grew to $4.6 billion, but its infrastructure spending last year was $7.3 billion, and it is expected to invest $518 billion in the next decade, of which about 80% are irreversible commitments. The company needs to maintain growth amid fierce competition, increasing political risks, and tighter funding, otherwise its massive financial obligations may trigger systemic risks. The AI industry is facing challenges of declining advantages of cutting-edge models and tightening commercial spending, with companies increasingly preferring to use standard models to improve ROI.
AI as Normal TechnologyPredictions of the probability of AI risks lack rigor and cannot effectively guide policy-making. These probability estimates are as unreliable as predictions for 2024, primarily serving to package vague intuition and fear. Policymakers should recognize that, unlike almost all other quantitative predictions, AI risk probabilities are not based on validated models or methods.
Interconnects00JS Denain of Epoch AI believes that internal metrics about RSI from OpenAI and Anthropic, such as the doubling of Codex usage monthly, are not sufficient to prove that AI self-acceleration is imminent, but external observations show an increase in AI usage in model deployments. He is uncertain whether RSI constitutes a major risk but acknowledges the existence of long-term risks.
