--- title: "A Fresher and a Senior Talk About Learning the Craft in the AI Era" url: https://www.thinnd.site/en/conversations/fresher-senior-dialogue-learning-in-ai-era/ language: en author: "Nhu Duy Thin (Thinnd)" published: 2026-10-29 updated: 2026-10-29 section: "Conversations" tags: ["fresher", "senior", "AI", "learning", "mentoring", "dialogue"] summary: "Through a rebuilt dialogue between a fresher and a senior, this article draws four lessons: nobody can promise AI will not change the market, but understanding, debugging and explaining code are still what gets tested; letting AI write your exercises can leave you learning less (a small Anthropic trial found the AI group scored about 50% on a quiz against about 67% for those who wrote by hand); ask your senior after trying it yourself and bring what you tried; and seniors must verify AI too because feeling fast is not a measurement." license: "Cite with the author name and the canonical URL above." --- # A Fresher and a Senior Talk About Learning the Craft in the AI Era > For people new to the field, and for seniors guiding them. > > **Mai** and **Khoa** are rebuilt characters to make this easy to picture, not real people. The conversation is an illustration, and the figures come from the public studies listed at the end. I say which parts are data and which are the characters' opinions. Many studies are small or published by vendors, so read them with care. --- ## Quick summary - Nobody can promise the market will not change. But **understanding, debugging and explaining code** are still what hiring tests. - **Letting AI write your exercises can leave you learning less.** Write first, ask AI to explain afterward. - **Ask your senior after trying yourself**, and bring what you tried. - **Seniors must verify AI too.** Feeling fast is not a measurement. - Each month ask yourself three questions to see whether you are really progressing. --- ## Setting A Friday afternoon at a café near the office. **Mai** has been at the company three months, on probation. **Khoa** is a senior on Mai's team, eight years in the field. --- ## 1. "I am afraid AI will take my job" **Mai:** Khoa, honestly, I am really worried. A friend says companies are hiring fewer freshers because AI can write code now. **Khoa:** I cannot promise you anything firm, because I do not know. But let me share what I have read. A Stanford study using payroll data found employment of 22 to 25 year olds in the most AI-exposed occupations fell about 13% relative to other groups. **Mai:** So it is true, then? **Khoa:** Hold on. That is American data, and it is a **correlation**, meaning two things moved together, not proof that one caused the other. Press coverage often names programming specifically, but the paper's summary does not single out that job. I have not seen an equivalent figure for Vietnam. So my view is: **moderate worry is reasonable, just do not let it decide how you learn**. > **Takeaway:** the door may be narrower, but the data is only a correlation and from elsewhere. Focus on what you control: your own skills. --- ## 2. "I let AI do all my exercises, it is so fast" **Mai:** There is something I am a bit embarrassed about. I often paste the task into AI, it writes code that works right away, and I submit. It is so fast. **Khoa:** Remember my review of your code last week? I asked why you used nested loops and you could not explain. **Mai:** Yes... I only knew it ran. **Khoa:** Let me tell you about an Anthropic trial published in early 2026. They had 52 programmers, mostly early-career, learn an unfamiliar Python library. The AI group then scored about 50% on a knowledge quiz and the hand-coding group about 67%. The biggest gap was on **debugging** questions. **Mai:** So AI makes me dumber? **Khoa:** I would not say that for sure. The sample is small, 26 per group, and I have read the summary, not the whole paper. The AI group was also about two minutes faster, but that difference was not statistically significant. Still, it **matches what I observe**: people who let AI write the very part they need to learn end up short on debugging. **Mai:** So what should I do? **Khoa:** Try this rule: **write for 20 minutes first, then ask AI to explain or point out your mistakes**. Do not ask for new code. Ask for an explanation. > **Takeaway:** AI is better at explaining than at learning for you. Write first so you have a version of your own to compare. --- ## 3. "So what should I learn?" **Mai:** If AI can write simple code, what should I learn? **Khoa:** I think five things, and to be clear this is my opinion, not a verified fact: 1. **Read other people's code.** Each week read an unfamiliar file and say in words what it does. 2. **Debug systematically.** Reproduce, narrow, hypothesize, test. Do not guess. 3. **Write tests.** Tests tell you what "correct" means. 4. **Use Git and reviews.** Working with others is daily life. 5. **Explain decisions.** Why this way and not that. **Mai:** Those are all things AI can do too, no? **Khoa:** AI does part of it, but **the person who answers for the result** needs to understand. Read the post on a [12-month plan for freshers](https://www.thinnd.site/en/journeys/fresher-ai-era-12-month-plan/), it goes into more detail than I do. --- ## 4. "Should I ask AI or you?" **Mai:** Sometimes I do not know whether to ask AI or bother you. **Khoa:** I split it like this. **Ask AI** about concepts and examples: "Explain pointers", "give an example of a loop". **Ask me** about context and decisions: "Why does our project do it this way", "should this part be changed". **Mai:** And how should I ask you? **Khoa:** Try for about 15 minutes first. Then bring three things: **what you tried, what you expected, and what you saw**. That lets me answer fast, and I can see how you think. **Mai:** But I am afraid of asking something stupid... **Khoa:** A question with those three things is never stupid. The stupid one is where you did not try first and cannot say what you tried. --- ## 5. "And how do you use AI?" **Mai:** Do you use AI? **Khoa:** Yes, daily. For repetitive parts such as scaffolding, sample tests and explaining odd code. But I **still read every change** and keep a checklist for reviewing. You can look at the [15-point checklist](https://www.thinnd.site/en/engineering/reviewing-ai-written-code/) I often use. **Mai:** So you must be much faster? **Khoa:** I thought so, then I read METR's study from July 2025. They had 16 experienced developers do 246 real tasks, half allowed AI and half not. The AI group was **about 19% slower**, yet they themselves believed they were about 20% faster. **Mai:** Huh, so AI does not help at all? **Khoa:** Careful, I am not saying that. The sample is small, they worked in projects they knew very well, and the tools were early 2025, much changed since. I draw only one lesson: **feeling fast is not a measurement**. So I started timing a few tasks myself, with and without AI. > **Takeaway:** even seniors need to verify. Measure instead of trusting a feeling. --- ## 6. "How do I know I am improving?" **Mai:** I do not know whether I am really improving. **Khoa:** Each month ask yourself three questions and write them on paper: 1. **What can I do this month that I could not last month** (without AI)? 2. **Of the code I submitted, how much can I explain** without looking back? 3. **How did I handle the hardest bug this month**, how long did it take, who helped? If questions one and two do not move for two months, reduce AI writing and increase hand-writing. If question three is always "asked AI to fix it", practice debugging by hand. --- ## 7. "What do they look at in interviews?" **Mai:** Later, in interviews, what do they look at in me? **Khoa:** I have interviewed a few freshers. I look at three things: can you **explain your own code**, can you **debug in front of me**, and **how do you verify what AI gives you**. The Stack Overflow 2025 survey, with more than 49,000 respondents, found 46% do not trust AI's accuracy and 66% often meet "almost right but not quite" answers. So I care how you **verify** more than how much AI you use. **Mai:** Do you ask "how do you use AI"? **Khoa:** Yes. A good answer usually has three parts: what you use it for, how you verify, and when you do not use it. --- ## 8. As the conversation winds down **Mai:** Give me some homework. **Khoa:** This week: pick a small task, write it yourself for 20 minutes, then ask AI to explain where you went wrong. At the weekend, tell me what you learned. **Mai:** Are you worried about anything? **Khoa:** Yes, a few things: the data is still thin, and I am not sure which parts will hold up long term. I am still learning too. Do not take what I say as truth. Take it as a **starting point for you to try and verify yourself**. --- ## Takeaways 1. **Worry moderately, and do not let it decide how you learn.** The jobs data is a correlation from elsewhere. 2. **Write first, ask AI to explain afterward.** The small Anthropic trial suggests letting AI write leaves a gap in debugging. 3. **Ask people after trying yourself, and bring what you tried.** 4. **Verify and measure, even as a senior.** METR's study is a reminder that feeling fast is not a measurement. 5. **Ask yourself three questions each month** to know whether you are really improving or just feeling fast. --- ## A balanced view - This is a **rebuilt dialogue**. Khoa's views are a character's, not verified conclusions. - The studies mentioned are **small or published by vendors**. The jobs data is American. Both may change as tools and the market change. - Everyone learns differently. Use these suggestions as a starting point, not hard rules. --- ## References - [Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of AI (Stanford Digital Economy Lab)](https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/) and [The Register's coverage](https://www.theregister.com/2025/08/26/ai_hurts_recent_college_grads_jobs/) - [Anthropic: AI assistance and coding skills (January 2026)](https://www.anthropic.com/research/AI-assistance-coding-skills) - [Simon Willison on the METR productivity study](https://simonwillison.net/2025/Jul/12/ai-open-source-productivity/) - [Stack Overflow Developer Survey 2025, AI section](https://survey.stackoverflow.co/2025/ai) - Related posts in this blog: [Fresher in the AI era: a 12-month plan to become a junior](https://www.thinnd.site/en/journeys/fresher-ai-era-12-month-plan/), [Reviewing AI-written code](https://www.thinnd.site/en/engineering/reviewing-ai-written-code/)