The End of Programming
Claims that AI coding agents herald “the end of programming” are drawing both excitement and skepticism. Commenters point to large-scale examples like an AI-assisted rewrite of the Bun JavaScript runtime as proof that models can now generate and refactor huge codebases, but argue this still relies on human-crafted tests, architectures, and product decisions. Many expect the nature of software work to shift toward specification, verification, and system understanding rather than line-by-line coding, while warning that overreliance on AI may erode expertise, degrade software quality, and upend developer job markets.
Overall reaction to “end of programming”
- Many see the headline as overblown or clickbait; others say it accurately describes a real shift in how code is produced.
- Historical parallels are drawn (e.g., FORTRAN “ending coding”); consensus is that “programming” changes form rather than disappearing.
- Several argue this is more about the end of manual coding as default than the end of software engineering.
Bun Zig→Rust rewrite as evidence
- Supporters see the Bun rewrite as a striking proof that agents can generate and iteratively refine large, complex codebases when there is a strong oracle (tests, reference implementation).
- Critics counter that:
- Translation with a full test suite is a best‑case, not representative of typical projects.
- The resulting Rust code reportedly contained serious soundness and quality issues.
- Much of the benefit may come from general refactors, not just the language switch or AI itself.
- Several note we must assume more human review and engineering happened than marketing implies.
Code quality, architecture, and maintenance
- Repeated reports of LLM output being “slop”: buggy, overconfident, weak at refactoring, and bad at architecture unless tightly guided.
- People see LLMs excelling at boilerplate, translation, and small tools, but struggling as systems get more complex or older.
- Strong concern that dependence on AI erodes human expertise, leading to poor architectural decisions and “cognitive debt.”
- Tests and verification are seen as critical; some believe code will become disposable, regenerated from specs/tests, others worry about endless regressions without near‑perfect coverage.
Changing role of programmers
- Many expect a shift from line‑by‑line coding to:
- Defining precise specs, constraints, and verification harnesses.
- System architecture, trade‑offs, and product decisions.
- Reviewing, debugging, and maintaining AI‑generated code.
- Some think this will favor highly skilled engineers and push out “just here for the money” devs; others fear the opposite—loss of craft and passion, replaced by prompt jockeying.
Economic and labor impacts
- Predictions range from:
- Smaller onshore teams + big token budgets replacing large engineering groups or outsourcing.
- Significant deflationary pressure on white‑collar wages globally.
- Some foresee software skills becoming table‑stakes for product roles, shrinking dedicated “software engineer” headcount.
- There is anxiety about social readiness: talk of UBI or even “universal high income,” plus worry that current political systems won’t respond adequately.
Long‑term trajectory and open questions
- Several frame this as the rise of metaprogramming / declarative or intent‑based development, not extinction.
- Others argue that fully agentic systems deciding rewrites and product direction would be unsafe or economically misaligned.
- Unclear whether future models can truly handle high‑level product, political, and organizational context, not just code.