Episode #25: OpenAI Hiring, FastSearch Speed, AI Coding Reality, Trendy Hiring Risks, Illegible Work
OpenAI’s Bold Move: Taking on LinkedIn with AI-Powered Hiring
Is LinkedIn about to face tough competition? OpenAI has announced its own hiring platform powered by AI. The new service, called the OpenAI Jobs Platform, is planned for release by mid-2026 and will help connect businesses and job seekers in smarter ways. It will include special tracks for small companies and local governments looking for AI talent. This step shows OpenAI moving beyond ChatGPT and aiming to change how companies recruit. A key part of the platform is AI training. Through its OpenAI Academy, the company will offer certifications and partner with big employers like Walmart to train 10 million Americans in AI skills by 2030. This also supports the White House’s goal of increasing AI knowledge across the country. For developers and tech workers, the message is clear: learning AI is becoming a must. The impact could be huge. With OpenAI entering the hiring market, job platforms might go through major changes. As AI takes over more office tasks, companies will compete for workers skilled in AI, giving tech professionals both new chances and new pressure to keep improving their skills.
Google FastSearch is Faster than Google Search
Did you know Google uses a special technology called FastSearch to power its Gemini models? Unlike standard Google Search, FastSearch retrieves fewer documents, making it much faster but with lower result quality. This trade-off works well for Gemini’s grounding needs, where speed is more important than delivering fully ranked web results. FastSearch is not available directly to third parties. Instead, it is built into Google’s Vertex AI platform, which lets customers ground on Google Search results or other data. However, external users only get information from FastSearch—not the ranked results themselves. For developers and AI teams, the key takeaway is that Google is optimizing for rapid AI response times, but is also keeping its most advanced retrieval technology under wraps to protect its intellectual property.
Is AI Coding Tools Any Effective?
AI coding tools were hyped to supercharge output, but the expected flood of new apps never came. Release data looks flat, and several field studies show mixed or even negative productivity, especially for experienced devs working in large, mature codebases. A key finding: teams often feel faster with AI, yet real measurements show slowdowns from prompt wrangling, validating “almost right” code, and integrating changes. That perception gap matters. What this means: don’t adopt on hype. Run short, controlled trials on real backlog work. Track cycle time, review rework, defect escape, and incident rates—not just sentiment. Use AI where it shines: scaffolding, boilerplate, small refactors, and exploration. Contain costs with tests, linters, and stricter reviews for AI-sourced code. Until there’s consistent evidence of shipped-software gains, cautious, measured adoption beats blind enthusiasm.
Hiring the Trends: The Risks of Chasing Trendy Developers
Many companies say they “hire the best,” but in practice they hire the trendiest—pedigrees, hot stacks, and big-name logos—while overlooking proven builders who don’t fit the mold. That drives them into bidding wars for the same small talent pool, or into underpaying and ending up light on senior engineers, even when those seniors have scaled systems far beyond the company’s own load. It’s classic Moneyball: the edge comes from spotting undervalued talent, not chasing hype credentials that everyone else wants and can outbid for. The fix isn’t mystical. Build great tools and processes so engineers are actually productive; a fast build, strong CI, and sane reviews beat prestige hiring alone by orders of magnitude in day-to-day throughput. Replace vibe checks with work-sample tests and standardized interviews to surface capable “non-trendy” candidates who outperform once given the chance. And invest in real training and mentorship; compounding learning turns “unremarkable inputs” into consistently strong outputs over time, a durable advantage most firms ignore. Career luck is real—so design hiring to find skill, not status.
The Hidden Power of Illegible Work in Software Companies
Big companies love legible work—plans, OKRs, Jira, and estimates—because it’s easy to track and helps win long enterprise deals. But the work that actually unblocks teams is often illegible—quick favors, backchannel asks, and tacit knowledge that doesn’t fit the plan. Legibility brings real benefits: leaders can see who’s doing what, plan quarters ahead, and shift people in a crisis. But too much process slows engineers down. That’s why orgs spin up “tiger teams” or incident modes to move fast, then return to normal once the fire is out. Common myths break here: titles don’t equal the same output, estimates aren’t exact, and adding people doesn’t linearly speed delivery. Small, focused teams often ship faster than big ones buried in process. The takeaway: keep the useful parts of process for planning and trust, but also protect space for fast, informal work—simple cross‑team changes, trusted backchannels, and small reversible bets. Balancing both is how software companies stay reliable and still ship quickly when it counts.