Jobs and Software Is Fucked
Experienced software engineers describe a sharply deteriorated job market, with fewer interviews, vanished recruiter outreach and opaque, often AI-driven screening that rejects even strong candidates. Many blame post-pandemic monetary tightening and overhiring hangovers more than AI itself, but note that generative tools are reshaping expectations, interview processes and fears about long‑term job security. Responses range from embracing AI as a required skill, to pivoting into other fields or trades, to hoping for a future correction when companies rediscover the need for human expertise.
State of the Software Job Market
- Many describe this as the worst market they’ve seen: strong resumes (10–15+ YOE) getting no interviews, online recruiter spam drying up, and automated rejections after perfect screening tests.
- Others report pockets of normalcy or ease (e.g., some new grads, some seniors in specific cities), suggesting highly uneven conditions.
- Perception that there are “too many programmers, too few jobs,” especially post‑COVID boom.
Causes: Macro vs. AI
- Several tie the downturn mainly to macroeconomics: pandemic money printing, cheap‑money hiring frenzy, then rate hikes, layoffs, and over‑supply of devs.
- AI is seen by some as an accelerant or scapegoat layered on top of those macro forces.
Hiring Practices and Interviews
- Complaints about leetcode/Hackerrank, especially unsupervised online tests where cheating is easy.
- Reports of HR/ATS/ML filters choking off candidates, mysterious rejections, fake or “process only” job postings, and heavy reliance on referrals.
- High value placed on brand‑name employers on resumes; networking often viewed as more important than raw skill.
AI and Coding: Tool, Threat, or Hype?
- One camp: refusal to use AI is career suicide; most software jobs will vanish or compress to a small number of engineers orchestrating agents.
- Another camp: AI code is unreliable “slop”; real engineering, testing, and domain expertise can’t be automated, especially in complex domains (e.g., game engines, hardware‑adjacent work).
- Strong culture‑war tone in creative fields (games, art, writing). Some see using AI as betraying peers whose work was used for training and whose jobs are at risk; others reject this framing.
Sector and Geography Differences
- Hardware/ML‑adjacent roles (PCIe, DDR, Ethernet, silicon design/verification, firmware) reported as in strong demand with very high pay, but requiring niche skills.
- Some regions (e.g., London, parts of Asia, some Japanese/Chinese game studios) are perceived as more active or more aggressive in adopting AI.
Coping Strategies and Career Pivots
- Advice ranges from “adapt and learn AI deeply” to “build your own product/business” to “pivot out of tech” (examples: diesel mechanic, actuary, physical goods business).
- Several stress long‑term networking, side projects, and accepting that steady, modest careers in other fields may be more stable and satisfying than chasing volatile tech roles.