AI excels where success is quantifiable (e.g., code generation). Its greatest challenge lies in subjective domains like mental health or education. Progress requires a messy, societal conversation to define 'success,' not just a developer-built technical leaderboard.

Andrej Karpathy's 'Software 2.0' framework posits that AI automates tasks that are easily *verifiable*. This explains the 'jagged frontier' of AI progress: fields like math and code, where correctness is verifiable, advance rapidly. In contrast, creative and strategic tasks, where success is subjective and hard to verify, lag significantly behind.

To predict AI's future impact on the broader economy, observe its current capabilities in software development. AI models are consistently about a year ahead in coding ability compared to other domains, providing a reliable preview of the automation coming to other knowledge-work sectors.

Judgment Labs CEO Alex Shan argues that AI agents will first dominate domains with easily verifiable results, like coding, where a solution's correctness can be quickly checked. Progress will be slower in non-verifiable fields like law or complex drug discovery, where feedback loops are long and ambiguous.

Software engineering is a prime target for AI because code provides instant feedback (it works or it doesn't). In contrast, fields like medicine have slow, expensive feedback loops (e.g., clinical trials), which throttles the pace of AI-driven iteration and adoption. This heuristic predicts where AI will make the fastest inroads.

AI can produce scientific claims and codebases thousands of times faster than humans. However, the meticulous work of validating these outputs remains a human task. This growing gap between generation and verification could create a backlog of unproven ideas, slowing true scientific advancement.

AI labs deliberately targeted coding first not just to aid developers, but because AI that can write code can help build the next, smarter version of itself. This creates a rapid, self-reinforcing cycle of improvement that accelerates the entire field's progress.

AI will automate and replace jobs most rapidly in domains where its output can be objectively verified for correctness, like coding. In fields requiring subjective judgment with no single "right answer," such as creative or strategic roles, its impact will be augmentation, not outright replacement.

AI can generate vast amounts of content, but its value is limited by our ability to verify its accuracy. This is fast for visual outputs (images, UI) where our eyes instantly spot flaws, but slow and difficult for abstract domains like back-end code, math, or financial data, which require deep expertise to validate.