Generative AI tools aren’t just autocomplete for code—they’re reshaping the very first rung on the engineering career ladder. Host Mike Vizard asks Juan Salinas, VP of business development at Jalasoft, and software engineer Rolando Lora whether entry‑level developer jobs are about to disappear.

Salinas is blunt: the routine, low‑risk tickets that once taught newcomers the ropes are now exactly what teams hand to AI agents. University graduates were “20–30 percent job‑ready” before; today the same agents that help them finish coursework will compete for their first paychecks.

Lora counters that the picture isn’t all doom. Lightweight “vibe‑coding” chats let designers and product owners spin up prototypes in hours, while senior engineers use agentic AI to explore multiple implementation paths and ship higher‑quality features. The catch, he says, is that newcomers must learn to review—not just generate—code, and master “context engineering,” the craft of feeding agents the right background so they solve problems in one pass.

Both envision a mixed future: specialized agents will handle boilerplate and reviews, while humans focus on architecture, judgment and systems thinking. For that to work, academia must stop graduating “juniors” and instead turn out mid‑level developers who already know how to collaborate with AI responsibly. Hiring managers, Salinas adds, should map each task to a spectrum—augmentation, automation or full agency—and invest in upskilling rather than backfilling rote work.

The take‑away: early‑career coding isn’t dead, but its fundamentals are changing fast. Anyone entering the field will need to treat AI as a teammate they trust—yet constantly verify. That means pairing curiosity with critical thinking and realizing that debugging a machine’s work may become the new entry ticket to the profession.