According to Andrew Ng, AGI remains decades away or longer, and frontier models will not eliminate most application-layer businesses without substantial customization, indicating a longer buildout cycle for tools and services rather than rapid displacement, source: Andrew Ng on Twitter dated Nov 13, 2025 and deeplearning.ai The Batch issue 327. Ng reports current LLMs are narrow versus humans, excel mainly at text, require heavy context engineering, and he would not trust a frontier model alone for calendar prioritization, resume screening, or lunch ordering, noting his team achieved a decent resume screening assistant only after significant customization, source: Andrew Ng on Twitter dated Nov 13, 2025. Ng adds that while thin wrappers may be replaced, many valuable applications will not be displaced for a long time despite rapid model progress, which counters fears that model providers will quickly wipe out app startups, source: Andrew Ng on Twitter dated Nov 13, 2025. For traders, this points to sustained demand for vertical AI integration, data pipelines, and domain-specific agents over commoditized chat fronts, aligning with Ng’s view of persistent limitations and customization needs, source: Andrew Ng on Twitter dated Nov 13, 2025 and deeplearning.ai The Batch issue 327. For crypto markets, AI-focused tokens tied to compute, data, and application ecosystems may find more durable narratives than generic LLM wrappers given customization and feedback bottlenecks highlighted by Ng, source: Andrew Ng on Twitter dated Nov 13, 2025. Ng encourages newcomers to learn to build with AI now, signaling ongoing demand for skilled builders over many years, source: Andrew Ng on Twitter dated Nov 13, 2025.

According to @GoogleDeepMind, new research reveals that Large Language Models (LLMs) often do not fully utilize their capabilities in goal-directed tasks. The study, discussed by Tom Everitt, highlights that while LLMs possess the potential to execute complex tasks, they frequently underperform due to a lack of effort in employing their full capabilities. This insight is crucial for traders relying on AI for predictive analytics, as it underscores the importance of understanding the limitations of AI-driven models in executing precise trading strategies.