Atlassian's founder suggests a model for AI's impact. In "input-constrained" fields like legal or support, AI drives efficiency on a finite set of tasks. In "creation-constrained" fields like software development, AI amplifies output on an infinite roadmap, leading to market expansion.

AI models excel in domains with discrete, quantifiable outcomes like coding or chess. However, they struggle with most knowledge work, which is often "unverifiable" and lacks a single correct answer for reinforcement learning models to train on. This distinction explains AI's current limitations in many professional roles.

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.

An AI startup's durability depends on its workflow. Those in specialized, data-gated fields like law are defensible. In contrast, startups improving common knowledge work in tools like Excel face existential threats, as these workflows are not differentiated and may become obsolete.

AI thrives in domains with fixed, written rules and searchable histories, like programming. In ambiguous areas like organizational conflict or political negotiation, where context is unwritten and lives in people's heads, its performance plummets. Its confident output masks this unreliability, posing a danger to decision-makers.

Don't replace reliable, rules-based automation with probabilistic AI. Instead, use AI for tasks requiring reasoning over unstructured text, like mining job descriptions for buying signals. This is where AI excels and traditional if-then logic fails due to its rigidity.

Contrary to expectations, the most complex but verifiable fields like math and software engineering will likely be automated before subjective business functions. Verifiability provides clear training signals and objective success metrics for AI models, a luxury not present in areas like marketing or negotiations where "correctness" is fluid.

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.

A key asymmetry exists in AI deployment: it has become much easier to use AI to generate exact, predictable automation software (design time). However, using probabilistic AI agents to directly execute enterprise processes (run time) remains just as difficult and ungovernable as before.