Reinforcement learning with verifiable rewards allows models to achieve super-human mathematical and coding performance rapidly, because outcomes can be objectively checked in simulated environments. In contrast, progress in non-verifiable domains advances much slower because improvement remains tethered to human data generation bottlenecks. The resulting jagged intelligence frontier creates super-intelligence within narrow formal environments while general capabilities lag behind.

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.

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.

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.

Unlike math or code with cheap, fast rewards, clinically valuable biology problems lack easily verifiable ground truths. This makes it difficult to create the rapid reinforcement learning loops that drive explosive AI progress in other fields.

AI models improve dramatically in domains with objective feedback, like coding (unit tests) or science (lab results). Progress is slower in subjective fields like creative writing where feedback is opinion-based, explaining the uneven impact of AI across different types of knowledge work.

Unlike more abstract domains, AI research is particularly suited for automation by AIs. The tasks are verifiable, allow for iterative improvement, and can be broken down into containerized environments for reinforcement learning.

The path to AI self-improvement isn't uniform. It is happening first in software engineering and AI research because these fields have cheap, fast, and verifiable feedback (e.g., unit tests). This capability won't automatically transfer to domains like biology until similar closed-loop systems are built.

Verifiability alone doesn't explain AI's rapid progress in math and coding. The key factor is 'grindability'—the ability to run thousands of parallel, containerized, and deterministic simulations. This allows for efficient credit assignment and learning, a luxury not available in domains like e-commerce or business strategy, which are constrained by real-world interactions and bot detectors.