The rise of async AI programming

Offshoring Analogy & Role of the “Product Owner”

• Several compare async AI workflows to classic offshore development: write specs, hand off, review next day.
• It worked when specs were clear and the product owner had real decision authority; otherwise misunderstandings and tech debt piled up.
• Some argue this model only really works when the “product owner” is effectively the true owner (solo dev / founder), not a middle‑manager relaying executive wishes.
• Others say the workflow is basically what tech leads already do when delegating to human devs.

Difficulty of Clear Specs

• Many point out that “define the problem clearly” is the hardest part of software, and is already a huge multiplier even without AI.
• Detailed specs can become so long that decision‑makers don’t read them; what’s asked for often isn’t what’s actually wanted.
• Critics say the vision is “DOA” if it assumes stable, correct requirements upfront; defenders counter that AI lowers the cost of experimentation before specs are fixed.

Skill Atrophy, Tech Debt, and Code Quality

• Strong concern that mostly reviewing AI output will erode hands‑on coding skills, making rare “escalation” debugging impossible.
• Several fear AI agents will enable tech debt at massive scale, especially when business leaders can’t judge quality.
• Others report AI has improved their bug‑spotting by exposing them to lots of subtly broken code.
• One thread argues that the real solution is strong static analysis, agent‑driven refactoring, and robust tests rather than humans reviewing all generated code; skeptics call high‑quality tests themselves hard, non‑automatable work.

Comparison to Compilers and “Real Programming”

• One critique frames the workflow as a slow, unreliable “natural language compiler” whose output must still be inspected.
• Others argue this is closer to product management / tech‑lead work: specifying and reviewing behavior and architecture, not line‑by‑line coding.
• A Lamport-inspired view distinguishes “programming” (specifying and designing) from “coding”; AI may force more time in the former stages.

Naming, Framing, and Personal Preference

• Many object to calling this “async programming,” expecting discussions of async/await and event loops; several call the title misleading or clickbait.
• Alternative terms floated: AI-assisted coding, agentic coding, prompt-driven development, “Ralph coding,” AI delegation.
• Some find this future depressing—turning their favorite part (hands-on coding, small puzzles) into spec writing; others enjoy offloading boilerplate and using AI to stay productive with limited time (e.g., during parental leave).