Do I belong in tech anymore?
Rapid adoption of AI tools in software development is leaving many engineers feeling burned out, ethically uneasy, and unsure if they still belong in the industry. Commenters describe workplaces where AI-generated code, tickets, notes, and reviews are used uncritically, eroding craftsmanship, institutional learning, and respect for quality while management chases hype and cost savings. Alongside this, mid-career developers report severe hiring challenges and fear of downward mobility, prompting some to consider leaving tech entirely or seeking niches where human-centered, high-quality engineering is still valued.
Emotional impact and belonging
- Many relate to feeling alienated or burned out, especially when AI is mandated or overused.
- People fear being labeled “difficult” if they push back on AI practices, so they often stay silent.
- Some say work has become bland: they enjoyed hand-writing and designing code, but “agentic” AI processes feel hollow.
- A few suggest long breaks or even leaving mainstream tech can restore mental health.
Job market and career anxiety
- Multiple mid- and senior-level devs report being unable to get interviews despite years of experience and using AI to tailor resumes.
- Conflicting views on the market: some claim it’s “hot” in major hubs; others say remote roles are scarce and competition intense.
- Advice offered: move to cheaper regions, avoid AI-written resumes, lean on referrals, or even switch careers—though many feel boxed in by debt, disability, or lack of alternative skills.
AI use in everyday engineering
- Reports of AI-generated tickets, design docs, and huge PRs merged with little or no human review.
- Some see AI as a powerful accelerator that helps good engineers deliver more value; others see it amplifying incompetence and knowledge debt.
- There is frustration with AI note-takers and meeting summarizers that misrepresent discussions or add little value.
Code quality, safety, and “appearance of work”
- Concern that AI encourages an emphasis on visible output volume over correctness, maintainability, or institutional learning via code review.
- Some argue businesses already tolerated buggy, insecure systems long before AI; AI just makes the “fast, sloppy” equilibrium more extreme.
- Counterpoint: good implementation details and thoughtful reviews still matter for long-term maintainability and incident response.
Culture vs. tools
- Many insist the core problem is organizational culture and incentives, not AI itself: faddish “AI everywhere” mandates, performative work, and VC-driven hype.
- Others view tech as inherently about automation; if you’re not comfortable with that, you may not enjoy staying in the field.
Possible paths forward
- Suggestions include focusing on “master craftsman” niches, high-quality human-centric code, or open-source work.
- Some foresee a future of mass-produced, disposable software plus small pockets of premium, human-crafted systems.