AI Hiring for Coders: Coding Is Baseline Analysis

The candidate who solves the coding problem is not automatically done proving the point anymore. The sharper question now is whether they can explain why the solution is right, where it might fail, and how they would work when an AI assistant can generate plausible code in seconds. That is the useful lesson behind the phrase that AI is rewriting the hiring playbook for coders: coding still matters, but it is no longer the whole audition. The interview is becoming less about showing that you can type an answer and more about showing that a team can trust your judgment.

The interview is lagging the job, CNN Business reports CNN

Business describes a software hiring market under pressure, with tens of thousands of tech job cuts increasing competition for open roles and AI raising fears of cheating during interviews. The publication frames the harder problem this way: if AI can write code, hiring managers have to rethink what skillset makes someone a good software engineer. CNN quotes Stefan Mai, a former engineer for Meta and Amazon and cofounder of Hello Interview, saying, "I would say AI has hit engineering interviewing like an atomic bomb." Strip away the shock value and the practical issue is clear: a process built around solo coding performance is trying to measure production judgment. The Pragmatic Engineer reported a related split in its Sep. 18, 2025 edition of The Pulse, drawing on details from 63 engineers in Big Tech, scaleups, and startups who interview on interviewing.io. Its summary says Big Tech interviewers are using tougher questions, while startups are dropping algorithmic questions and takehomes. It also points to growing cheating concerns and AI-assisted interview processes. That is not one clean trend, which is exactly why candidates need to prepare for the interview style, not just the job title.

Algorithms are table stakes,

The Pragmatic Engineer finds The tempting shortcut is to assume algorithmic interviews are going away. The Pragmatic Engineer's reporting suggests something messier: some employers are making questions harder, while some startups are moving away from algorithms and takehomes. For a candidate, that means the old advice to grind one interview format until it becomes muscle memory is weaker. You still need the fundamentals, but you also need to read the room. This is where title sprawl hurts job seekers. A listing that says software engineer can mean a classic data structures screen, a product engineering conversation, a systems design loop, or an AI-tooling discussion dressed up as a normal coding round. Treating all of those as the same interview is like calling every role with AI in the title an AI engineer. The label is less useful than the workflow being tested.

The job market rewards workflow, Indeed Hiring Lab says Indeed

Hiring Lab's AI at Work report says virtually every job will face some level of exposure to potential GenAI-driven change, while also saying GenAI is unlikely to fully replace many jobs, especially those requiring manual skills or deep personal connections. For software candidates, the relevant point is not that coding disappears. It is that coding sits inside a wider workflow of scoping, reviewing, debugging, communicating risk, and deciding when generated code is not good enough. Indeed Hiring Lab has also reported that generative AI job postings surged starting in 2023, while a broader measure of AI job postings eased over the past 18 months amid a wider tech slowdown. That combination matters. The market can demand AI fluency without handing every candidate an obvious AI-labeled job. In interviews, that shows up as employers expecting practical tool awareness even when the role is still called software engineer.

How to prep

when code is not enough CNN Business reports that the interview process has not kept up with how AI has changed programmers' daily responsibilities. So candidates should not wait for employers to produce a perfect new format. Prepare to narrate your reasoning while you code, explain tradeoffs before you optimize, and describe how you would validate AI-assisted output. If a tool is allowed, make your use of it visible and accountable; if it is not allowed, do not smuggle it in and hope the interviewer misses it. The Pragmatic Engineer's split between tougher Big Tech questions and startup changes points to a second prep habit: ask what the loop measures. Before an interview, clarify whether the screen emphasizes algorithms, systems design, product work, debugging, or collaboration. A 25-year-old candidate may be optimizing for repetition and speed, while a 45-year-old career switcher may be optimizing for proof that prior domain judgment transfers. Different constraints, same hype cycle, and the same need to convert experience into interview signal. The next software interview will still reward people who can code. The candidates who stand out will be the ones who can also explain, audit, collaborate, and decide. Watch for employers to keep experimenting with AI-assisted interviews, harder live screens, and more conversational evaluations. Your best preparation is not a certificate that says you know AI; it is a portfolio of decisions you can defend under questioning.