--- title: Thinking Beyond Automation to Safeguard Tomorrow’s Software Talent date: 2026-05-11T21:12:03+08:00 description: I now prefer interns over senior developers because they act as efficient thin clients to AI. Since traditional coding tests are obsolete, I evaluate candidates on their ability to nudge agents and own the final result. tags: [claude-code, iit-madras, coding-agents] --- ## Or, Why I Now Prefer Interns Over Senior Developers Ankor runs a company of several thousand people. After a bunch of calls with one of our interns, Varun (a student at IIT Madras), Ankor messaged me: "This guy is fantastic. How is he doing it?" This is what Varun was doing: he records calls, feeds the transcript to Claude Code / Codex, and delivers results. That's the whole process. He _doesn't_ interpret the content. He _doesn't_ apply domain knowledge. He gets out of the way. I gave Varun that instruction deliberately. I told him: "Ankor will say something. Don't try to understand it. You will not understand it anyway. Record the call. Transcribe it. Give it to Claude Code. Deploy to GitHub. Show Ankor. Take feedback. Repeat." **Varun is thrice as fast at this than anyone with five years of experience.** Weirdly, the experience _is_ the bottleneck. --- [Usha Rengaraju](https://www.google.com/search?q=usha+rengaraju) who leads Research at Exa Protocol, and the world's first female triple Kaggle Grandmaster, told me at a panel in Hyderabad this March: until 2024, she employed six to seven interns, predominantly from top Indian Institutes of Technology (IIT), at ₹3-3.5 lakh per month in salaries. Now, she's replaced them with artificial intelligence (AI), saving ₹60-70 lakh a year. She went the opposite direction from me. I'm hiring more interns than before. We're both right. Her work is greenfield prototyping where the model handles everything. My work requires a human in the loop - but one who doesn't _slow_ the loop down. **The intern is a thin client to AI.** --- I teach a data science course at IIT Madras to ~5,000 students each year. I design the exams. I iterate on them constantly. In March 2026, I pointed a coding agent at one of my own exams (22 questions, 45 minutes) to test if the questions were correctly specified. The agent solved _everything_ in well under 45 minutes. The highest score any actual student achieved: 50 percent. Second highest: 33 percent. And I no longer have a clue of how to judge if an evaluation is easy or hard. **This is a measurement crisis.** My instruments are calibrated for a world where people produce the output directly. That world ended. The exam still ran. The scores still came back. But what they're... I have no clue anymore. So, I now use this as a feature. Before releasing any exam question, I copy-paste it into ChatGPT. If it gives the answer, I revise the question. I'm using AI to invalidate my own evaluation instruments in real time. (This is a strange sentence to write. I'm writing it anyway.) --- The hiring equivalent of this is what enterprise software teams are in their own interview loops. [Karat's 2026 engineering interview report](https://karat.com/the-human-ai-workforce-transformation-is-here-is-your-hiring-process-ready/) found that 62 percent of organizations prohibit AI in their technical assessments. But hiring leaders estimate more than half of candidates use it anyway. That's not a skills test. Maybe it's an honesty test? Actually, the candidates who pass are the ones who _hide_ their tools, not the ones who use them best. [CodeSignal's 2026 survey](https://www.prnewswire.com/news-releases/codesignal-launches-industry-first-agentic-coding-assessments-for-ai-era-engineering-hiring-302732265.html) says 91 percent of software engineers use agentic AI coding tools, and 75 percent have shipped production code generated at least partly by AI in the past six months. [Stack Overflow's 2025 developer survey](https://stackoverflow.blog/2025/12/29/developers-remain-willing-but-reluctant-to-use-ai-the-2025-developer-survey-results-are-here/) puts AI tool adoption at 80 percent across developers globally. The take-home project doesn't measure what it used to measure. Nor does the GitHub portfolio. [HackerRank's 2025 developer skills report](https://www.hackerrank.com/reports/developer-skills-report-2025) makes it clear: standards good enough two years ago no longer are. **The output no longer proves the capability.** [Anthropic's own two-hour engineering exam](https://www.anthropic.com/news/claude-opus-4-5), when given to Claude Opus 4.5, produced a score higher than any human candidate ever. To be fair, the model was given several chances at each problem, and the exam measures technical ability under time pressure rather than collaboration or communication. But the direction is clear. Bnchmarks that screen humans now favor machines. What's left that proves anything? --- The question I now actually ask (when deciding who to bring in) is no longer "Can you write code?" It's: **"Can you stop slowing down AI?"** Accumulated domain knowledge is often a bottleneck. It makes people second-guess the model, rewrite agent output without reviewing, and retry approaches the model discarded. Usha still runs her coding interviews for important apps. ("I would still have them go through system design interviews, coding rounds." She paused, then added "in 2026". I agree - 2027's too far out.) In banking, pharma, and other regulated domains, expertise matters. A lot. Large language models (LLMs) can hallucinate and we need experts to catch them. But that's not most of software is delivering today. --- The industry is starting to get it. [Meta runs coding interviews](https://www.wired.com/story/meta-ai-job-interview-coding) that allow AI coding agents. That's how real work happens anyway. Karat's NextGen assessment framework suggests realistic environments and expert interviewers evaluating judgment rather than the code. The question has changed from "Can you write code?" to "Can you define the problem, nudge the model, spot the wrong answer, and own the result?" --- I don't know what exactly to hire for. My answer keeps changing. But I know the old signals - hard work, practicing problems, time pressure - weren't what we were hiring for either. They were proxies that AI made easy. That's good. Clearly, we weren't measuring what we really wanted. What seems to work for me is: give them a vague problem, a coding agent, and 4 hours. See if they deliver _useful_ output, and if they can explain what the agent got wrong. If they do that, I don't care about their resume. I'll hire them on the spot. --- Published at on 29 May 2026.