Anthropic's Claude Code team reports that AI agent skills designed for "verification"—teaching an agent to test and validate its own output—provide an extremely high return on investment. This suggests that building reliability and correctness into AI workflows is as critical, if not more so, than the initial generation capability.

While AI can generate code, the stakes on blockchain are too high for bugs, as they lead to direct financial loss. The solution is formal verification, using mathematical proofs to guarantee smart contract correctness. This provides a safety net, enabling users and AI to confidently build and interact with financial applications.

The easier AI makes it to generate content like resumes or slide decks, the more effort is required to verify their authenticity and quality. This economic principle shifts value and labor from the act of creation to the act of verification.

Teams often build financial models to confirm their enthusiasm for a particular AI initiative. However, the model's greatest value comes from rigorously challenging these assumptions. Often, the most hyped projects are revealed to have a fraction of the financial impact of less visible but more strategic alternatives.

Instead of solely focusing on AI fallibility, a major application is using AI agents to audit human work. Perplexity's "Final Pass" feature analyzes documents for factual errors and internal inconsistencies, finding glaring mistakes in things like Gartner's earnings press releases and work done by professional accountants.

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.

AI excels at generating code, making that task a commodity. The new high-value work for engineers is "verification”—ensuring the AI's output is not just bug-free, but also valuable to customers, aligned with business goals, and strategically sound.

Verification isn't just a compliance tax or a fix for hallucinations. It's a tool to amplify genius, much like mathematical proofs enabled Ramanujan to scale his intuitive brilliance into theorems that future generations could build upon. Its purpose is to compound superintelligence.

While the dream is an AI that designs chips from a prompt, its immediate, practical use at ARM is in speeding up verification and creating sophisticated financial forecasts for royalty accruals. This shows AI's current strength is in augmenting complex operational workflows rather than pure creation.