AI-assisted coding will change software engineering: hard truths
AI-assisted coding tools are rapidly becoming part of everyday software development, but engineers are split on what that really means for code quality, careers, and the future of the field. Many find current LLMs useful for boilerplate, learning APIs, and speeding up routine tasks, yet highlight their brittleness, tendency to produce “70% solutions,” and risk of eroding deep understanding and senior-level expertise. Longer term, commenters debate whether progress will stall at current limitations or accelerate toward agents that can replace large swaths of developer work, with concerns about enshittified products, security vulnerabilities, and a shrinking pool of truly skilled practitioners.
Future trajectory of AI coding tools
- Debate over whether current LLM limits are temporary or near a plateau.
- Optimists expect rapid, possibly exponential improvement and serious job displacement within 3–10 years.
- Skeptics argue we may be near a local maximum (data limits, diminishing returns from scale) and that big further gains are not guaranteed.
- Some compare this to other tech that improved fast then plateaued (planes, cars); unclear if LLMs are early “biplanes” or already near maturity.
Current capabilities and the “70% problem”
- Many report LLMs are great for boilerplate, API usage, and simple scripts, but consistently wrong or incomplete on the final 20–30%.
- Tools often hallucinate APIs, miss small syntax details, or get stuck cycling between different broken versions.
- Effective use requires micromanagement, verification, and domain knowledge.
Impact on developers, skills, and careers
- Concern that juniors will skip “hard parts,” leading to fewer truly competent intermediates/seniors and atrophy of existing expert skills.
- Some see a future where experienced engineers become more valuable precisely because they can supervise and repair AI output.
- Fears that employers will demand AI use to justify lower pay and fewer engineers.
Code quality, maintenance, and security
- Widespread worry about “AI-generated slop”: superficially impressive demos that fall apart in edge cases.
- Anticipation of more security bugs and brittle “house of cards” systems needing expensive cleanup.
- LLMs struggle with large, idiosyncratic brownfield codebases and internal patterns.
Frameworks, languages, and abstractions
- One view: AI will encourage custom frameworks per company, hurting skill transfer; another: LLMs will push consolidation around popular ecosystems they know best.
- Some argue languages are “verbose and stupid” and real progress would be better DSLs and formal methods, not just AI autocomplete.
- Others stress that programming is about precise communication; natural language remains too vague.
Agents and automation
- Heavy skepticism about “agent” hype: many see them as glorified scripted workflows with an LLM front-end, far from autonomous Jarvis-like systems.
- Comparisons made to NFT/metaverse hype cycles.
Practical uses today
- Common replacements: Stack Overflow and basic search, especially for library boilerplate and quick Python/pandas/matplotlib tasks.
- Single-line or small-snippet completion inside IDEs seen as a good balance; full in-editor generation often degrades architecture over time.
- Tests generation and deep refactors are cited as areas where current tools underdeliver.
Ethics, incentives, and product quality
- Strong concern over unethical training, labor exploitation, environmental costs, and “paying to train your replacement.”
- Expectation that productivity gains will fund more features, not better quality, reinforcing existing “enshittification” trends.
- Reference to automation paradox (Bainbridge): more automation may reduce human practice while increasing the need for expertise during failures.