Efrain Pineda Jaimes’ Post

Modern tech hiring has officially become an extreme sport. 🏃♂️💨 The playbook usually looks like this: - Post an open role for a Senior Engineer. - Receive 700 applications in 4 hours. - Realize 500 of them are "AI Visionaries" whose primary experience is prompting ChatGPT to write corporate emails. - Burn 40 hours of your actual developers' time running technical screening loops. - Finally find the perfect candidate... only to get ghosted on day one. Meanwhile, your product milestones are completely frozen, and your internal team is burning out trying to cover the gap. Sourcing shouldn't look like a reality TV elimination show. If your internal engineers are spending more hours interviewing than writing code, the system is broken. What’s the wildest thing you’ve seen on a tech resume lately?

Hiring in 2026 doesn't look anything like hiring did four years ago, and most people are still missing what's changed. The layoffs get the headlines. The rewrite underneath them is bigger. Resumes stopped meaning much when ChatGPT could write one in 30 seconds. Screening rounds moved to AI at more than a third of companies. The interview stopped rewarding charm and started rewarding clear thinking. Middle-manager roles are turning into player-coach roles. Every layer of the funnel got quietly rebuilt in the last two years, and hiring teams are only halfway through catching up. One thing building Expert Hire keeps making obvious is that the screening layer between resume and interview is where most of the shift is landing. The layer that used to be a rubber stamp is turning into the part that decides who gets hired. The quieter story is that AI isn't taking hiring away from people. It's rebuilding the process people have been running on autopilot for the past years. #ai #hiring #futureofwork

I kept telling you which jobs AI erases. I was watching the wrong number. Layoffs are the metric everyone announces. Hiring is the one that's hard to fake — and it just told the opposite story. New SignalFire data, tracking careers across 80M+ companies: at the 12 biggest tech firms, engineers went from 46% of all new hires in 2019 to 55% in 2025. Total hiring at those firms is down 25% from 2019. Engineering hiring is down just 11%. Early-stage startups hired 7% more engineers than before the pandemic. So "AI is coming for the people who build it" had the causation backwards. Companies are cutting the tasks AI can absorb — and concentrating what's left on the people who can point it somewhere useful. AI didn't shrink the engineer. It raised the premium on being one. That's the part I underweighted: AI isn't deleting the work. It's redistributing every role toward whoever can drive the tools fastest — and hiring is just the first place it shows up. It's the whole reason LockedIn AI exists: let a candidate prove they can drive the tools live in the interview, instead of getting screened out by a filter that only reads the resume. Hiring leads — the engineer you almost passed on because "AI will do that soon": how many of those calls do you want back?

Hot take: ChatGPT helps more with your job search than 90% of LinkedIn “hiring” posts. And that’s embarrassing. “Google is actively HIRING! 10 roles inside! Apply with referrals! 🙏” Posted by someone who: ❌ Doesn’t work at Google ❌ Can’t refer you ❌ Just copied public links These posts don’t help. They farm likes. Google’s careers page is cleaner, fresher, and one search away. Same for every big tech company. Real help is rare. Actual referrals. Real interview breakdowns. Warm intros. Everything else is just noise. Job seekers deserve better. #JobSearch #TechJobs #LinkedInTruth #CareerAdvice

AI is no longer just about better tools. It is about building the right teams to run them. New roles like Context Architects, AI QA Specialists, and Workflow Integration Engineers are turning AI from experiments into real operational systems. But most local hiring pipelines are not built for these fast-emerging skills. That is why companies are increasingly turning to global talent and dedicated development teams to scale AI faster and more efficiently. Read the full blog, link in the comments.

The apprenticeship angle is the part most leaders miss while they argue about headcount. If AI absorbs all the junior work, the reps that turn juniors into seniors disappear, and the review bottleneck Ofek describes only compounds. We see the same pattern with code: teams that let agents generate everything end up with more to review and fewer people who understand what they are reviewing. The version that works uses AI to explain the system to newer engineers, not to skip their learning. Worth reading his full breakdown of why review and validation, not code creation, is the real constraint now.

Unpopular opinion: The biggest AI risk for companies is not layoffs. It is that nobody knows how juniors become seniors anymore. AI is eating the boring work: first drafts QA checks reconciliations customer support triage simple code research summaries slide cleanup documentation And yes, a lot of that work was repetitive. But it was also apprenticeship. That is where people learned: what good looks like what bad looks like where mistakes hide how context changes the answer how senior people think how to build judgment under friction Now we want a 23-year-old to skip the reps and immediately become a reviewer of machine output. I am not convinced that works. The research is already hinting at the same problem: AI can increase output, but review and validation become the bottleneck. GitLab found that many teams now say code review and governance are bigger constraints than code creation. A recent enterprise coding study found output doubled, but reviewer load roughly doubled too. The Big Four are openly asking how junior staff will learn when agents do the grunt work. Stanford research found early-career workers in AI-exposed roles are feeling the labor market pressure first. So maybe the question is not: “What jobs will AI replace?” Maybe the better question is: “What work should we deliberately protect because it teaches judgment?” Companies that automate all junior work may save money this quarter. But they might also destroy their senior talent pipeline. Curious where people disagree: What is the boring work in your field that juniors still need to do?

Every company wants more AI engineers. But most are about to miss the real hiring problem. Technical recruiting alone won't fill your AI talent gap. Here's what's coming... The AI systems you actually want in production aren't just code. They're moving targets, full of unpredictable behavior, failed experiments, and edge cases devs don't see until it's too late. Traditional product managers? Out of their depth. Pure technical recruiters? They screen for keywords, not outcomes. The missing role: AI project managers. Not another layer of bureaucracy. Operators who actually get how AI projects break. People who know why model drift happens, why a "working" prototype decays when exposed to users, why QA for AI is a totally new beast. They translate data science speak to business impact. They fight for testing in the gray areas, not just getting a demo to work. They know compliance isn't paperwork, it's the difference between shipping and a recall. By 2026, companies that have this talent will outship everyone else. You can staff up with another recruiter. Or you can admit AI projects need a new kind of owner. Your call.

I keep seeing companies promoting their new AI-powered hiring platforms, promising to revolutionize recruitment and improve the candidate experience. Some even advertise roles that later turn out to be outdated or no longer active. Then you click Apply… ❌ Resume upload doesn’t work ❌ The application freezes ❌ DevTools shows a nice 500 Internal Server Error As a QA Engineer, my first instinct is, of course, to open DevTools instead of giving up 😄 Out of curiosity, I sometimes look into the QA teams behind these platforms, and many of them have 10 to 15+ years of experience. Which makes me think: well, with that level of expertise, the application experience itself should be one of the most polished parts of the product. AI absolutely has the potential to transform hiring, but only if the platforms themselves are stable enough for candidates to actually complete the application process. A smooth candidate experience still starts with quality. Sometimes fixing the basics brings more value than adding another AI feature. Try to do that:))