According to @timnitGebru, AGI is potentially the most important area to work on, a statement that highlights AGI’s prominence in the current AI discourse, source: https://twitter.com/timnitGebru/status/1996768491601645906. For traders, timestamped AGI commentary like this can be fed into event-study frameworks to test intraday reactions across AI-linked crypto assets and related equities, source: https://ssrn.com/abstract=180948. Empirical research shows social media sentiment can correlate with asset returns, supporting the use of posts like this as inputs to crypto market sentiment models and AI-narrative watchlists, source: https://arxiv.org/abs/1010.3003.

According to Anthropic (@AnthropicAI), improving Chain of Thought (CoT) monitoring is essential for identifying safety issues in AI systems. The strategy requires making CoT more faithful and obtaining evidence of higher faithfulness in realistic scenarios. This could potentially lead to better trading decisions by enhancing AI troubleshooting, ensuring systems operate as intended. The paper suggests that other measures are also necessary to prevent misbehavior when CoT is unfaithful, which could impact AI-driven trading models. [Source: AnthropicAI Twitter]

According to Demis Hassabis, the Gemini 2.0 series models are now leading in cost efficiency and performance, providing powerful reasoning and multimodal capabilities essential for agentic applications in trading. These advancements can improve algorithmic trading models by reducing operational costs while enhancing decision-making accuracy. Source: @demishassabis.