OpenAI's AI Wrote 700 Math Papers. Engineers Still Won't Trust AI With Their Code.

YouTube · Watch/Read · (gathered 2026-10-14)

• While AI has achieved “narrow superintelligence” in specific domains like advanced mathematics (evidenced by OpenAI’s 700 math papers), this does not automatically translate to general superintelligence or effective transfer to other complex areas like software engineering or business judgment.
• The “coding gap” highlights this: despite massive investment and easy verifiability, engineers still implement “zero-trust environments” and extensive “evals” for AI-generated code. AI excels at code generation but struggles with critical human contributions like system design, maintainability, security implications (e.g., handling revoked permissions), and knowing what questions to ask beyond basic functionality.
• In business, AI’s generative speed can be counterproductive if it doesn’t address underlying problems or discover the right questions to ask for customer value. AI often lacks the “business intuition” and judgment required for context-dependent decisions, partly because it’s trained for “helpfulness” rather than challenging assumptions.
• The practical takeaway is to view AI as a “supercharged tool” and focus on developing uniquely human skills like driving business impact, critical judgment, and understanding context. Individuals have “surprising agency” in choosing how to apply AI, recognizing its limitations, and deciding when to trust it or not.