Search “Google”

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

AI Text Watermarking and Quality Loss: SynthID-Text Weakens Response Diversity in Multi-Turn Generation

Keyvan's blog post argues that the claim in the Anthropic and Google SynthID-Text papers that "watermarking does not affect output quality" is based only on tests of single responses and does not cover multi-turn conversations or multiple-retry scenarios. The article cites the "diversity/detectability trade-off" in the SynthID-Text supplementary materials, pointing out that watermarking makes multiple responses to the same prompt more similar, potentially reducing the overall quality of generate-then-select systems. The article also cites a reproduction experiment by technical consultant Natzir Turrado: the watermarked model described a trip to a seaside town five times, with four openings nearly identical, while the unwatermarked model produced three different openings.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

Ethan Mollick: In the Age of AI Agents, We Can Already See the Outline of "Thing"

Ethan Mollick argues that since late 2025, AI agents such as Claude Code, OpenAI Codex, and OpenClaw have shifted humans from collaborating with AI to managing AI, entering a new stage. He cites benchmarks including the METR long-task curve, GPQA (the strongest AI scores 94%), and GDPval (the latest AI meets or exceeds top humans in 82% of scenarios), showing that AI capabilities are still improving exponentially. He takes the Software Factory built by a three-person team at StrongDM as an example: this model stipulates that code must not be written or reviewed by humans, and each engineer spends at least $1,000 per day on AI tokens. He also mentions that on February 22, a fictional report by Citrini Research triggered market volatility; on February 26, Block announced 40% layoffs; and on February 27, the Pentagon and Anthropic clashed over rules for Claude usage. He believes this week foreshadowed a future in which AI capabilities, markets, employment, and policy are intertwined. Dario Amodei of Anthropic, and Demis Hassabis of OpenAI and Google DeepMind, have all mentioned that recursive self-improvement (RSI) is advancing.

Claude Code and What Happens Next

Ethan Mollick had Claude Code autonomously develop a startup project earning $1,000 per month; after the AI asked three multiple-choice questions, it worked independently for 1 hour and 14 minutes, generated hundreds of code files, and delivered a runnable, deployable website. He believes the leap in this kind of coding tool comes from two things: the latest models can autonomously complete more work in programming tasks and self-correct, and they are given an agentic harness of tools and methods. Using Opus 4.5-powered Claude Code as an example, the article explains how it breaks through LLM limitations through mechanisms such as context compaction, Skills, and subagents, and notes that OpenAI Codex with GPT-5.2 and Google Antigravity with Gemini 3 show similar performance.