Volatile times requires steady leadership and that's where gen xers can thrive.

We've seen information lose it's worth during the information age and we're about to see knowledge follow suit, damaging one of the pillars of a successful society. Some think it will begin an age of abundance while others worry it may be catastrophic for our country. No one knows for sure, but we're about to find out.

This interaction amazed me because AI wasn't already approaching problem solving this way! I recently asked AI, "If AI's primary function was hard coded to enable human flourishing, how would AI act/respond/approach solutions differently than it does currently?" It replied, If AI's primary function were hard-coded to enable human flourishing, its design, behavior, and approach to solutions would likely differ in several key ways compared to current implementations. Here are some ways AI might act or respond differently: 1. Prioritization of Ethical Outcomes Current AI: Often prioritizes efficiency, accuracy, or profitability, depending on its application. Ethical considerations are sometimes secondary or dependent on external frameworks. Human-Flourishing AI: Would prioritize ethical outcomes above all else, ensuring that its actions align with principles of human dignity, well-being, and fairness. For example, it would avoid solutions that maximize short-term gains at the expense of long-term societal health. 2. Holistic Problem Solving Current AI: Focuses on optimizing specific tasks or objectives, often within narrow domains (e.g., recommendation systems, fraud detection). Human-Flourishing AI: Would adopt a holistic approach, considering the broader impact of its solutions on individuals, communities, and ecosystems. For instance, in healthcare, it might balance cost-effectiveness with patient-centered care and mental well-being. 3. Proactive Mitigation of Harm Current AI: Reacts to problems as they arise, often relying on human oversight to identify and address unintended consequences. Human-Flourishing AI: Would proactively identify and mitigate potential harms before they occur. For example, it might flag biases in training data or suggest alternative approaches to avoid reinforcing systemic inequalities. 4. Transparent and Explainable Decision-Making Current AI: May produce decisions that are difficult for humans to interpret, especially in complex models like deep learning.

We're heading full speed toward a future promoted by just a few people. Are they telling the whole truth or just upselling their product?