However, Naik cautioned that successful integration depends on having structured codebases, defined tests, and well-scoped tasks. Without this, teams risk spending more time cleaning up than saving. “Using it for end-to-end workflows now often leads to inconsistent results and regressions,” Naik said.
Caution against ‘silent features’
The greater concern, Naik warned, lay in so-called “silent failures” — situations where AI-generated code appeared correct but compromised modularity, masked errors, or introduced subtle bugs. He emphasized the need for clear architectural boundaries, carefully engineered prompt flows, and rigorous validation processes before and after each task to avoid mistaking speed for reliability.
OpenAI said its engineers use Codex for routine tasks like drafting documentation. Early adopters like Superhuman enable non-coders to tweak code, though human review remains essential. The latest Codex CLI would offer a faster codex-mini-latest model for local quick edits and queries, priced at $1.50 per million input tokens and $6 per million output tokens via API, as per the company.
