Nobody is hiring vibe coders. Sorry
Coding interviews are not dead. They are just moving, and the move is not in the direction social media keeps promising. Since 2023, technical interviews have nearly doubled in the US, and the bar is getting higher, not lower, because companies are using AI as a reason to test judgment more carefully, not less.
That is the part people keep missing. If you assume interviews are gone, you prep for a world that does not exist. If you assume the bar dropped because ChatGPT can write code, you walk into a market that still wants proof you can think, debug, explain, and ship.
The market is asking for more technical proof, not less
The easiest way to see the shift is to look at what companies are actually doing. In a recent interview run across 11 companies, every process that went far enough reached a technical stage. Those stages were not all the same, but they all asked for evidence that went beyond surface-level coding.
A few patterns kept showing up:
- Classic coding challenges, including the old-school Sudoku problem.
- System design questions that pushed on architecture and trade-offs.
- Company-specific niche questions, especially when the role sat inside a specialized domain like databases.
- Pair programming sessions.
- Real-world debugging and code walkthroughs.
That mix matters because it shows where the industry is actually headed. The old single-function puzzle still exists, but it is no longer the whole story. A lot of companies want to see how you behave when the code already exists, when the problem is messy, and when the best answer is not a clean algorithm but a solid decision.
The numbers back that up. CoderPad reports that technical assessments are up 48% globally since mid-2023, and 43% of those companies still use classic algorithmic assessments. The rest did not get softer, either. They shifted toward system design, live collaboration, and debugging situations that look a lot more like work.
That is the real headline. The bar did not vanish. It changed shape.
The companies building AI are still testing fundamentals hardest
A lot of people assume the most AI-forward companies would be the most relaxed about technical interviews. The opposite is happening. The companies closest to the models are often the strictest, because they understand exactly what AI can and cannot do.
That shows up in several ways:
1. OpenAI still cares about live reasoning
A commonly reported question at OpenAI is implementing an LRU cache from scratch while explaining your thinking in real time. That is not a gimmick. It tests whether you understand how data structures fit together, and whether you can reason under pressure instead of just typing something that looks plausible.
2. Anthropic makes the no-AI rule explicit
Anthropic publishes an official candidate policy that says no AI assistance unless it is specifically allowed. The point is simple: they want to see how you think, not how well you can outsource.
3. Meta goes a different route with AI-assisted interviews
Meta has taken the opposite approach by creating a round where AI use is encouraged. Reportedly, that round is harder than the classic one. When the model starts helping, the interviewer can focus on whether you notice bad output, challenge assumptions, and catch obvious mistakes instead of blindly trusting the tool.
The important thing is not which company uses which rule. The important thing is that the most AI-literate companies are not lowering their standards. They are raising the level of scrutiny.
Nobody is hiring vibe coders.
Interviews are really testing judgment, not typing speed
The biggest myth in the "AI killed coding interviews" conversation is the idea that interviews were mainly about producing code in the first place. They were not. Syntax was never the core test, and a lot of interviewers even let candidates choose the language precisely because the language itself was not the point.
What matters is the reasoning behind the solution. Can you break down a problem cleanly? Can you compare trade-offs? Can you explain why one path is better than another? Can you tell where the approach fails at scale?
That is why so many interviews have already drifted away from pure coding drills. A large share of them are really system design conversations, whiteboard walkthroughs, or scratch-pad sessions where the interviewer is watching how the idea evolves.
The best interviews, on both sides of the table, usually come from conversation rather than performance theater. The candidate does not just hand over an answer. They walk through the alternatives, call out edge cases, and show they understand the shape of the problem. That is what makes someone useful on a team.
A strong technical interview is not a memory test. It is a window into how someone thinks when the answer is not obvious.
AI is moving the bar upward, not deleting it
There is a simple way to think about the last several decades of tooling. Every big tool removes one layer of work and pushes evaluation higher.
- Compilers removed the need to handwrite assembly.
- Frameworks removed huge amounts of boilerplate.
- React jobs stopped asking you to obsess over memory management in the same way lower-level roles do.
- AI is now removing a slice of hand-written code generation.
Each step does not erase skill. It changes which skill gets tested.
That is why the "just prompt harder" crowd is so off-base. Writing code was never the whole job, and it certainly is not becoming the whole job now. If anything, the more code a tool can generate, the more valuable your ability to evaluate, refine, and correct that output becomes.
The shift also explains why interviews are becoming more realistic. Companies are swapping toy problems for multi-file codebases, debugging sessions, and extensions of existing systems. Instead of asking you to invert a binary tree in a vacuum, they hand you code and ask you to make it better. That is much closer to real engineering work.
And that is progress.
AI fluency is becoming part of the job description
There is another group of people in this argument who are just as out of step, the ones who refuse to touch AI tooling at all. That stance might have sounded principled a year or two ago. Now it mostly sounds like resistance to the market.
A developer who refuses to use AI is not automatically bad, but that refusal is becoming a problem in its own right. Hiring managers increasingly expect candidates to know how to use the tools without letting the tools do the thinking.
That split is already visible in hiring data. Around 74% of developers say they are struggling to land jobs, while 78% of tech leaders say they cannot find qualified people. Both can be true at the same time. There are jobs. There is also a shortage of candidates who can meet the current bar.
The reason is not that the industry stopped hiring. It is that the bar now includes two things at once:
- Core engineering fundamentals.
- Practical AI fluency.
That second skill matters because the market no longer rewards pretending AI does not exist. The winning candidate is not the person who rejects the tool or worships the tool. It is the person who knows when to lean on it and when to slow down and reason for themselves.
I think that is the point people keep resisting. AI did not make developers obsolete. It made shallow developers easier to spot.
The winners will know the fundamentals and the tools
A lot of the panic around coding interviews comes from a false binary. Either you grind old-school interview prep forever, or you give up on learning to code because AI can generate answers. That binary is nonsense.
The real world is pushing toward a hybrid standard. You still need enough fundamentals to understand what the code is doing, where it breaks, and why a suggestion from a model is wrong. You also need enough AI fluency to use the tools without outsourcing your judgment.
That is why the pilot analogy works so well. Autopilot is useful, but it is not the reason anyone gets to sit in the cockpit. The tool supports the operator. It does not replace the operator.
The same thing is true for software. The developers who thrive now are the ones who can do both:
- Understand the underlying systems.
- Work effectively with AI-assisted tooling.
- Catch mistakes before they ship.
- Explain decisions clearly under pressure.
The bar is not disappearing. It is moving higher, and the people who can handle that are going to look a lot more valuable than the people still arguing about whether the floor fell out.
The market does not want vibe coders. It wants engineers who can think, adapt, and still know what to do when the model is wrong.
This article was adapted from Nobody is hiring vibe coders. Sorry
