Code is cheap. Show me the talk

Headline, framing, and intent

• Some readers fixate on the inversion of “Talk is cheap, show me the code,” seeing it as devaluing code and betraying ignorance.
• Others note it’s explicitly riffing on the Linus quote and argue the article’s core claim is narrower: LLMs make typing cheap, so design and “talk” (models, specs) become relatively more important.
• A few think the piece is mostly reiterating what practitioners already know, with a catchy title to ride AI hype.

Code vs “talk” / design

• Many agree that in mature products, writing code is only ~10–20% of the effort; the rest is understanding, design, coordination, testing, and operations.
• One view: programming languages are tools for thought, not just for telling computers what to do. From this angle, “programming is planning,” and you can’t just replace it with prose and prompts.
• Another view reframes “talk” as the broader engineering process: specs, architecture, communication, not just idle chatter.

AI-generated code: quality, ownership, and maintenance

• Multiple reports of “vibe-coded” or LLM-heavy projects: initially impressive, but riddled with race conditions, bad tests, or subtle bugs that make maintenance miserable.
• Distinction stressed between generating code (now cheap) and owning it over time (still expensive). Every line is a liability; slop at scale is technical debt.
• Some argue agents plus strong test harnesses can iteratively refine code and will eventually outperform average humans; skeptics point to brittle tests, models grading their own homework, and 2am outages on code no one understands.

Skill, learning, and juniors

• Widely shared concern that juniors and interns will be replaced by AI, or will offload learning to it and never develop critical judgment.
• Suggested coping strategy: use LLMs as tutors and reviewers, not as primary authors, especially early in a career.
• Several characterize LLMs as amplifiers: good developers get much better; sloppy ones produce more and worse slop, faster.

Hype, economics, and trust

• Strong suspicion that much AI enthusiasm is marketing- and valuation-driven, echoing past tech bubbles; others counter that the programming paradigm has nonetheless shifted.
• Some emphasize that the real “cost” is not code production but the risk others take when they rely on your software; track record and trust become the key differentiators.
• A recurring theme: both code and talk are cheap; what now matters is demonstrated results, reliability, and trustworthiness over time.