If you follow semiconductor news at all, 2026 has been the year of the chip-design agent. Every major EDA vendor now has an AI system that doesn't just autocomplete code but plans and carries out multi-step engineering tasks: writing RTL, generating testbenches, running simulations and chasing down why a test failed. The question we hear most from students in our sessions is simple: is this going to take the fresher jobs? The honest answer is no, but it is going to change which freshers get hired.
In March 2026, NVIDIA announced partnerships with all three big EDA vendors to build AI agents for chip design, and each vendor now has a named product:
On top of the big three, startups are raising money specifically for agentic RTL and verification work, and governments are funding AI-assisted chip design as a strategic priority. Treat the productivity numbers as vendor claims, not measured industry averages, but the direction is unmistakable: AI agents are entering every stage of the flow you're training for.
The tasks being automated first are the ones with clear inputs, checkable outputs and a lot of repetition: boilerplate RTL from a well-written spec, first-draft UVM components and sequences, assertion suggestions, lint and CDC report triage, sorting a thousand regression failures into a dozen root-cause buckets, and summarizing long tool logs. Not coincidentally, a lot of this used to be the work handed to a fresher in their first six months.
An agent can generate a testbench, but it can't tell you whether the test plan covers what the architect was actually worried about. It can propose a timing fix, but it doesn't know that the block owner three desks away has already reserved that area for a late feature. And critically, it can produce RTL that compiles, simulates and is subtly wrong. Somebody has to own correctness, and in a tape-out a bug that reaches silicon can cost crores and months. That responsibility stays with engineers, which means the skill of judging generated work becomes the core skill.
Interviewers know candidates have access to AI tools, so pure recall questions carry less weight. Expect more of: “here's a piece of RTL, what's wrong with it?”, “this test passes but the chip fails in the lab, how do you debug it?”, and “walk me through why you chose this architecture.” Some teams are openly asking candidates how they use AI tools in their projects. A good answer is specific: what you delegated, how you verified it, and one case where you caught the tool being wrong.
Agentic AI is real, it's shipping, and it will make good engineers much faster. That raises the bar for freshers but doesn't remove the rung. The candidates who'll do best are the ones who treat AI as a fast but unreliable junior colleague: useful, worth delegating to, and never trusted without checking. Build the fundamentals that let you do that checking, and the trend works in your favour.
Our live 1:1 mock interviews with working VLSI engineers focus on debugging, code review and reasoning, the skills AI can't replace.