The evolution of AI has shifted the required skill set from simply writing prompts to managing, educating, and delegating complex workflows to autonomous agents. This new role orchestrates teams of AI 'replicants' to achieve business outcomes with massive leverage.
As Full Self-Driving (FSD) and autonomous vehicles become widespread, the culture of driving will fundamentally shift. Prohibitive risk and insurance costs will make manual driving a rare, expensive hobby for enthusiasts, much like thoroughbred racing is today.
While corporate leaders plan slow, top-down AI strategies with RFPs, early-adopter employees will bring consumer tools into the workplace. This grassroots adoption will make the transformation a 'fait accompli,' similar to how consumerized SaaS previously spread within enterprises.
Prediction markets thrive on information asymmetry, mirroring the stock market before 2000's Regulation FD, when selective disclosure was common. This structure means 'sharps' with privileged information will consistently profit from 'squares' (the public), making it difficult for casual participants.
Unfunded state and local pension obligations, like California's near-trillion-dollar shortfall, are a looming crisis. A future federalization of this debt, not included in current CBO forecasts, could be the 'concrete that breaks the camel's back' and trigger a severe debt spiral.
A UC Berkeley study found employees using AI worked faster and took on broader tasks, leading to more hours worked, not fewer. AI offloads menial labor, making jobs more purpose-driven and motivating employees to do more, which increases stress and burnout.
Using public AI models leaks sensitive corporate data, as prompts and agent traces are sent to model providers. To protect proprietary information and maintain control, enterprises may revert to costly but secure on-premise infrastructure, reversing a 20-year trend of cloud migration.
Heavy use of AI agents and API calls is generating significant costs, with some agents costing $100,000 annually. This creates a new financial reality where companies must budget for 'tokens' per employee, potentially making the AI's cost more than the human's salary.
Historically, the debt-to-GDP ratios of the world's largest economies have moved in unison. As long as this trend continues, a high ratio in one country is less of a crisis because it's a relative problem. The real risk is one nation decoupling with significantly different economic output.
Despite pessimistic CBO reports, strong GDP growth, massive AI-related Capex ($600B from just four hyperscalers), and robust private sector job creation signal an economic boom. This period may be looked back upon as a new 'golden age' masked by political noise, similar to the late 1990s.