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# Introduction
Are we all in a self-inflicted race to the bottom? Data professionals have been employed for years to develop large language models (LLMs).
Now, the number of open data positions seems to be shrinking daily. Of these advertisers, most seem quite unusual.
By abysmal, I don’t mean low pay or unreasonable technical expectations of candidates. No, I mean those vague phrases: “comfortable working with AI productivity tools,” “able to ship high volumes of code,” or “strong quick engineering skills a plus.” Translation: A chatbot is your primary coding partner, there will be no guidance, no standards, just coding.
A chatbot, our own creation, is now reducing us to copy-pastors of its results. It doesn’t sound like a very meaningful or fulfilling job.
In this environment, is it still possible to find meaningful work?
# What is Vibcoding?
Andrej Karpathione Open Eye Co-founder, coined the term “vibe coding”. This means you don’t code at all.
What you do: You’re drinking your matcha latte, webbing, ordering a coding chatbot, and copying its code into your code editor.
What a chatbot does: It codes, checks for errors, and debugs the code.
What you don’t do: You don’t code, you don’t check for errors, and you don’t debug code.
How does this kind of work feel? Like full time brain rot.
What did you expect? You hand over all the fun, creative and problem-solving aspects of your job to a chatbot.
# Vibe Coding has devalued coding
“It’s not too bad for a throwaway weekend project, but still quite entertaining,” is what Andrej Karpathi had to say about Vibe Coding.
Even so, companies you’d trust—those who don’t think of their products as “throwaway weekend projects”—decided that it’s still a good idea to start practicing webcoding.
AI coding tools came in, and data professionals were thrown out. For those who remain, their primary function is interacting with the chatbot.
Work gets done faster than ever before. You meet deadlines that were previously impossible. Your ability to pretend to be productive has reached a whole new level.
The result? Half finished prototype. Code that breaks in production. Data professionals who don’t know why code isn’t working. Hell, they don’t even know what the code is for is to work
Prediction: Professionals who really know how to code will soon be back in fashion. After all, someone has to rewrite the code written “so fast” by the chatbot. Talk about performance. Well, you don’t get much more efficient than that.
But how will you survive until then?
# How do you get a meaningful job now?
The principle is simple: Can’t a chatbot work? Here’s a comparison between what AI can’t do and what you can do.
Of course, doing all this requires some skill.
# Required skills
Finding meaningful work in the coding age requires these skills.
// 1. Technical specification writing
Most applications you will deal with will come with incomplete and confusing information. If you can turn this information into a precise technical specification, you will be valued for preventing conflicting assumptions and expectations from development work. Technical specifications help align all teams participating in the project.
This is what encompasses this skill.
Resources:
// 2. Understanding of data flow
Systems don’t just fail because of bad code. Arguably, they fail more often because of incorrect assumptions about the data.
No matter how Vib coding, one still needs to understand how data is generated, modified and used.
Resources:
// 3. Production debugging
LLM cannot debug in production. That’s where you come in, with your knowledge of how to interpret logs and metrics to diagnose the root causes of production events.
Resources:
// 4. Architectural reasoning
Without understanding their architecture, systems will be designed to work in production (fingers crossed!), but they will often fail under real traffic.
Architectural reasoning determines a system’s reliability, latency, throughput, and operational complexity.
Resources:
// 5. Schema and contract design
Poorly designed schemas and definitions of how systems interact can cause a domino effect: cascading failures that lead to excessive migration, which in turn creates coordination friction between teams.
Create a good design, and you’ve created stability and prevented clogging.
Resources:
// 6. Operational awareness
Systems always behave differently in a production environment than in development.
Since the whole idea is to make the system work, you need to understand how components degrade, how failures occur, and what and where the bottlenecks are. With this knowledge, the transition between development and production will be less painful.
Resources:
// 7. Necessity communication
“Prevention is better than cure” applies here as well. If your requirements were initially poorly defined you can expect almost endless shutdowns and rewrites. It is trying to repair once the system is ready.
To prevent this, you must skillfully intervene in the early development stages to adjust scope, communicate technical constraints, and translate vague requirements into technically feasible ones.
Resources:
// 8. Review of Code of Conduct
You should be able to read code not only for its functionality, but more broadly for its system implications.
That way, you’ll be able to identify vulnerabilities that don’t show up in linting or tests, especially in AI-infused patches, and prevent subtle bugs that would otherwise mess up your production.
Resources:
// 9. Cost and Efficiency Decision
Your work has financial and operational implications. You will be much appreciated if you understand the computer usage, latency, throughput, and infrastructure bills in your work.
It is more valued by companies than building expensive systems that don’t work.
Resources:
# Real jobs that still feel meaningful
Finally, let’s talk about actual jobs that still involve the use of at least some or all of the skills we discussed earlier. The focus may shift away from coding itself, but some aspects of these jobs can still feel meaningful.
// 1. Data Scientist (the real kind, not just notebooks)
AI can generate code, but data scientists provide structure, reasoning, and domain understanding to ambiguous and, often, imprecise problems.
// 2. Machine Learning Engineer
AI can train a model, but what about data preparation, training pipelines, infrastructure servicing, monitoring, failure handling, etc.? This is the job of a machine learning engineer.
// 3. Analytical Engineer
AIS can write SQL queries, but analytical engineers are the ones who guarantee accuracy and long-term stability.
// 4. Data Engineer
Data engineers are in charge of data reliability and availability. AI can transform data, but it cannot manage system behavior, upstream changes, or long-term data reliability.
// 5. Machine Learning Ops/Data Ops Engineer
These roles ensure that pipelines run reliably and models remain accurate.
You can use AI to suggest improvements, but performance, system interactions, and production failures still require human oversight.
// 6. Research Scientist (Applied Machine Learning/Artificial Intelligence)
AI can’t really bring anything new, especially not new modeling approaches and algorithms. It can only rehash something that already exists.
For anything excrific, expert knowledge is required.
// 7. Data Product Manager
This job description is to describe what the data or machine learning product should do, including translating business needs into clear technical requirements and aligning the priorities of various stakeholders.
You can’t employ AI to negotiate scope or assess risk.
// 8. Governance, compliance, and data quality roles
AI cannot ensure that data practices meet legal, ethical and trustworthiness standards. Someone needs to define and enforce the rules, with governance, compliance and data quality roles.
// 9. Roles of data visualization/decision science
Data needs to be linked to decisions for any purpose. An AI can produce whatever chart it wants, but it doesn’t know what matters for a decision.
// 10. Senior Data Role (Principal, Staff, Lead)
AI is a great helper, but it’s a terrible leader. More precisely, it cannot guide.
Decision making? Cross domain leadership? Guide to technical direction? Only humans can.
# The result
Finding meaningful work in the age of coding is not easy. However, coding is not the only thing that data professionals do. Try looking for job ads, even if they require web coding, some of these skills are ones that AI still can’t replace.
Nate Rosedy A data scientist and product strategist. He is also an adjunct professor teaching analytics, and the founder of StrataScratch, a platform that helps data scientists prepare for their interviews with real interview questions from top companies. Netcareer writes on the latest trends in the market, gives interview tips, shares data science projects, and covers everything SQL.