Software engineering job openings hit five-year low?

Data, charts, and what’s being measured

• The article’s Indeed data only goes back to 2016; some wanted a longer view, others note there is a 25‑year graph and that the post‑COVID spike and fall are the main story.
• Several argue raw posting counts are unreliable: heavy duplication by agencies, many “ghost” or paused roles, scams, and H‑1B/PERM compliance ads.
• One view: since fake/duplicate postings are rising, the true decline is likely worse. Another: only the trend, not absolute numbers, is useful.
• There’s debate over labor statistics (BLS, FRED) and whether they’re trustworthy or politically distorted; others counter with specific government series and say federal hiring hasn’t exploded.

Ghost jobs, compliance postings, and regulation

• Many report roles reposted during freezes, or positions re‑advertised after layoffs, with no real intent to hire.
• Some think such practices border on securities fraud or should be penalized (e.g., time limits to engage applicants).
• H‑1B/PERM postings are seen by some as a sizable chunk of listings used to “pre‑justify” a chosen candidate; others calculate they’re ~20% of SWE postings and call that “pretty small.”
• There’s mention of a promised EEOC crackdown on fake H‑1B jobs, but people are skeptical anything meaningful will change.

Macro factors vs. AI

• Many see classic macro drivers: COVID over‑hiring, stimulus, then higher interest rates and tighter money removing “bullshit” companies and forcing cuts.
• US tax changes (Section 174/175) that make software R&D more expensive are cited as significant, especially for US‑HQ firms and fast‑growing startups, though they can’t explain identical trends in Europe.
• Some argue the chart mostly shows software being unusually sensitive to monetary policy (“pork cycle”) because so many roles are funded by new investment rather than stable operations.

Outsourcing, nearshoring, and remote work

• Strong theme: hiring shifting to LATAM and parts of Europe at roughly half US cost; founders and big firms reportedly interviewing mostly abroad for many roles.
• Some companies reversed offshoring after poor results, saying cheaper hires were as productive as low‑end US devs but dragged team quality.
• Broader pattern noted: two modes of outsourcing
  • (1) Ultra‑cheap labor → long‑term tech debt and cycles of disappointment.
  • (2) High‑quality but only modestly cheaper talent → works, but savings are small.
• Experiences with Indian vendors are polarized: some call the output chaos; others blame clients for dumping worst projects, poor onboarding, and treating offshore teams as second‑class.

AI’s role and LLM “productivity”

• Opinions diverge sharply:
  • Some senior devs claim ~25–50% productivity gains in certain stacks (especially TypeScript with tools like Cursor/Copilot).
  • Others find LLMs mostly generate plausible but wrong code, costing review time and occasionally shipping bugs they’d never have written.
• Multiple comments dispute the idea that “devs immediately spot hallucinations”; anecdotes show teams being misled into bad patterns by AI suggestions.
• There’s debate whether productivity gains reduce headcount (4 people doing work of 5) or, via Jevons‑style effects, increase demand for software and thus devs.
• Another angle: capital is being reallocated from “normal” software to AI, independent of whether AI really replaces engineers.

Labor market structure: juniors, seniors, and quality

• Many report essentially no junior hiring; shops prefer fewer seniors, often offshore, augmented by AI. The era of $180–190k US new‑grad roles is widely described as over.
• Juniors are told they must self‑train and endure low‑quality first jobs; few companies want to bear the cost of real training.
• Some argue a lot of pre‑2022 jobs were low‑value “enterprise CRUD” or “code masturbation,” with teams where only a minority could really program; a shake‑out is seen as unsurprising.

Comparisons to dot‑com and long‑term outlook

• One camp: this resembles the dot‑com bust—overhype, then a harsh correction—but long‑term demand will return with new platforms.
• Skeptics respond that generative AI is less like the Internet (new demand) and more like automation (replacing knowledge workers, including engineers).
• Others emphasize that even during dot‑com, “boring” domains (banks, industry, automation) kept hiring; similar hidden demand may exist now, but without splashy headlines.
• Several expect a multi‑year or decade‑long imbalance: many more applicants per posting, especially in US HCOL areas, even if total global SWE employment doesn’t collapse.