While AI's market performance has been concentrated in the tech sector, its greatest future value will be unlocked as it transforms other industries like healthcare, logistics, and consumer goods. Buchwald believes investors are underestimating this broadening impact, which will create new winners and losers across the entire economy.

The most significant societal and economic impact of AI won't be from chatbots. Instead, it will emerge from the integration of AI with physical robotics in sectors like manufacturing, logistics (Amazon), and autonomous vehicles (Waymo), which are currently under-hyped.

Contrary to expectations, analysis shows that sectors with low profit per employee, such as healthcare and consumer staples, stand to gain the most from AI. High-tech firms already have very high profit per employee, so the relative impact of AI-driven efficiency is smaller.

Contrary to expectations, professions that are typically slow to adopt new technology (medicine, law) are showing massive enthusiasm for AI. This is because it directly addresses their core need to reason with and manage large volumes of unstructured data, improving their daily work.

AI's most significant impact won't be on broad population health management, but as a diagnostic and decision-support assistant for physicians. By analyzing an individual patient's risks and co-morbidities, AI can empower doctors to make better, earlier diagnoses, addressing the core problem of physicians lacking time for deep patient analysis.

The most effective AI strategy focuses on 'micro workflows'—small, discrete tasks like summarizing patient data. By optimizing these countless small steps, AI can make decision-makers 'a hundred-fold more productive,' delivering massive cumulative value without relying on a single, high-risk autonomous solution.

The most significant opportunity for AI in healthcare lies not in optimizing existing software, but in automating 'net new' areas that once required human judgment. Functions like patient engagement, scheduling, and symptom triage are seeing explosive growth as AI steps into roles previously held only by staff.

Jensen Huang uses radiology as an example: AI automated the *task* of reading scans, but this freed up radiologists to focus on their *purpose*: diagnosing disease. This increased productivity and demand, ultimately leading to more jobs, not fewer.

AI is improving medical imaging accuracy and speed by nearly 70%, enabling earlier detection of chronic diseases. This leads to more effective preventive care, which is crucial for an aging global population and offers a promising path to making overall healthcare more cost-effective.