Writing code is cheap now

Code vs Software: Cheap Typing, Expensive Thinking

• Many argue that “writing code” has been cheap for years; what’s costly is designing good software, understanding domains, and making correct tradeoffs.
• LLMs mainly remove the time‑consuming “typing” step, not the hard parts: requirements, architecture, edge cases, performance, security.
• Several compare this to outsourcing or McDonalds: it’s always been easy to get lots of mediocre output cheaply; high‑quality work still costs.

Maintenance, Liability, and Technical Debt

• Strong consensus that code is a liability: more LOC means more to understand, test, and maintain, regardless of who wrote it.
• AI accelerates creation of “vibeslop” and prototypes that “sort of work” but are hard to change; reviewers and maintainers bear a growing burden.
• Some foresee more outages and new kinds of risk as agents rewrite critical systems; others think AI will eventually help with maintenance too.
• Reading/understanding code remains as expensive as ever, maybe more so when AI produces verbose, over‑abstracted structures.

AI as Autopilot: Where Humans Still Matter

• The pilot/autopilot analogy recurs: AI can fly the happy path (CRUD apps, boilerplate, simple glue code), but humans are needed for messy realities and emergencies.
• Good outcomes require tight human steering: clear specs, tests (often TDD), adversarial review, and careful use of agents rather than blind trust.
• A key new skill is “directing cheap inputs”: using agents to rapidly try approaches, then judging which won’t explode later.

Prototypes, Throwaway Code, and System Design

• Cheap code makes multi‑prototype exploration viable: build three versions in a day and pick one.
• Others warn that if you never personally wrestle with the problem, you don’t develop taste or understanding; the process, not the artifacts, teaches you.
• Some advocate “tracer bullets” and eval‑driven development: quick, end‑to‑end slices that are intended to be kept, not disposable demos.

Jobs, Skills, and Market Dynamics

• Commenters expect fewer roles for “ditch digger” programmers who just translate specs to code; top‑tier designers/architects remain in demand.
• Bootcamp‑style “I can type the code” skills are seen as commoditized; domain understanding, system thinking, and reliability judgment are the real moats.
• Debate continues on whether productivity gains are visible yet and how much AI will compress demand for mid‑level developers.