According to Andrej Karpathy (@karpathy), the economic impact of AI is best understood through the lens of a new computing paradigm dubbed 'Software 2.0,' where automation hinges more on task verifiability than on rule specification. Karpathy draws a direct analogy between the rise of AI and previous technological shifts like the introduction of computing in the 1980s, noting that early computing automated tasks with fixed, explicit rules such as bookkeeping and data entry (source: @karpathy, Nov 16, 2025). In contrast, AI systems today excel at automating tasks that are verifiable—where performance can be measured and optimized, often via reinforcement learning or gradient descent. This shift means that roles involving clear, measurable outcomes (such as coding, math problem solving, and tasks with objective benchmarks) are most susceptible to rapid automation. Meanwhile, jobs requiring creativity, complex reasoning, or nuanced context lag behind. For AI businesses, this trend underscores lucrative opportunities in automating highly verifiable workflows, especially in sectors like software development, finance, and data analysis. Companies seeking to leverage AI should prioritize problem spaces where success can be clearly defined and measured to maximize automation ROI (source: @karpathy, Nov 16, 2025).