AI models will quickly automate the majority of expert work, but they will struggle with the final, most complex 25%. For a long time, human expertise will be essential for this 'last mile,' making it the ultimate bottleneck and source of economic value.

The common fear of AI eliminating jobs is misguided. In practice, AI automates specific, often administrative, tasks within a role. This allows human workers to offload minutiae and focus on uniquely human skills like relationship building and strategic thinking, ultimately increasing their leverage and value.

Analysis of job data shows that roles experiencing the most significant growth are not purely technical. Instead, they are hybrid roles that blend technical expertise with human-centric skills like project management, coordination, and security oversight, which are difficult to automate.

Emerging AI jobs, like agent trainers and operators, demand uniquely human capabilities such as a grasp of psychology and ethics. The need for a "bedside manner" in handling AI-related customer issues highlights that the future of AI work isn't purely technical.

Career security in the age of AI isn't about outperforming machines at repetitive tasks. Instead, it requires moving 'up the stack' to focus on human-centric oversight that AI cannot replicate. These indispensable roles include validation, governance, ethics, data integrity, and regulatory AI strategy, which will hold the most influence and longevity.

As AI automates technical and mundane tasks, the economic value of those skills will decrease. The most critical roles will be leaders with high emotional intelligence whose function is to foster culture and manage the human teams that leverage AI. 'Human skills' will become the new premium in the workforce.

Using the historical parallel of ATMs, CEO Sim Shabalala argues that AI won't eliminate human roles but will automate routine tasks. This frees humans for higher-order work involving empathy, complex problem-solving, and valuable client interaction.

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.

As AI automates technical execution like coding, the most valuable human skill becomes "systems thinking." This involves building a mental model of a business, understanding its components, and creatively devising strategies for improvement, which AI can then implement.