According to @godofprompt on Twitter, leveraging the Theory of Knowledge (TOK) framework in understanding AI-driven business concepts exposes the hidden assumptions and mental models that often hinder effective decision-making. By decomposing AI business models into questions around perceived knowledge, validation sources, and observer effects, businesses can avoid surface-level understanding and instead foster deeper insights into AI system adoption, deployment, and strategy. The prompt encourages first principles thinking, highlighting how social proof and industry consensus around AI trends may not always correlate with real-world causation or business outcomes. This approach enables organizations to identify knowledge gaps between executives, developers, and end users, ultimately leading to more robust business strategies and reducing the risk of failure in AI implementation. (Source: @godofprompt, Twitter, Dec 8, 2025)

According to Andrew Ng on the 20VC podcast hosted by Harry Stebbings, significant bottlenecks remain in the widespread adoption of artificial intelligence, particularly in real-world business deployments (source: x.com/HarryStebbings/status/1990472838914945442). Ng emphasized that while AI technology has made rapid progress, many organizations face challenges in integrating AI into existing workflows, often due to data quality issues, lack of skilled talent, and operational inertia. The discussion also explored US-China geopolitics, highlighting how global dynamics influence AI market expansion and partnership opportunities. Ng stressed that there are still numerous untapped business opportunities for entrepreneurs and enterprises to build AI-driven solutions, especially in sectors like healthcare, manufacturing, and education. These insights underscore the ongoing need for practical AI implementation strategies and the vast market potential for innovative startups and established companies alike.