Hey friends, Happy Tuesday!
Everyone is asking the same question right now:
“will AI replace me?”
And I see the fear in every comment on my channel. Everyone is panicking about it. I mean everyone: students, juniors, even experts.
I don’t know why, but I am totally happy that we have AI. So here are my thoughts. Sorry, this one is long, but I wanted to share it all with you.
Once we get past the fear, I am going to post more about AI and coding, and AI and data.
Let’s go :)
The question is wrong
“Will AI replace the data analyst?” I think the whole question is wrong.
The better question is not “will AI replace my title”, it is “what happens to each task I do”.
Because the question looks only at the title. But in real companies we never work under one title.
You might find a data analyst who is more like a process expert, helping the business and giving advice all day. Or an engineer who helps the whole department, because they find the data problems and give amazing advice about the architecture.
To be honest, in many projects I ended up doing both roles.
Titles are never designed perfectly. We all do so many things at companies.
So when someone asks “will AI replace the data analyst”, I’m going to say: first tell me what this analyst really does all day.
Btw Today I published Roadmap for AI Engineers and Forward Deployed Engineers based on 40.000 Job ads, I built machine to analyse the job market :)
The task list test
Here is a test that works for any job. You could be a doctor reading this, it is the same.
Step 1: Make a list of what you really do. Not your job description, the real tasks: going to meetings, talking to people, writing code, answering questions, whatever it is.
Step 2: Give each task one of three marks.
AI alone: It does it without me.
AI with me: I’m faster, but I still decide.
Not AI: It needs me.
Step 3: Count. If every task is “AI alone”, then yes, your job is replaceable.
If it is half, and the rest needs deep understanding of your company or your client, then good news. It will not replace your job.
And look at which half it is. If AI takes the routine end of your job, you become more valuable, because now you have time for the expert end.
If it takes the expert end, that is the bad news.
And the “AI with me” tasks stay yours. You just do them faster.
Now you might say, okay, but how do I know which mark a task gets? Four questions:
Do I have to talk to people to do it? Colleagues, clients, other teams. If yes, it is “Not AI”.
Is everything it needs written down? If the knowledge is in people’s heads, AI cannot reach it. So if you want to fight AI, don’t write down your knowledge, keep it in your head 😅
If it gets it wrong, will I notice? If a wrong result looks exactly like a right one, it is “AI with me” at best, never “AI alone”.
Does someone have to answer for it? If a person has to put their name under the result, for the client, the auditor or the boss, it stays with a person. AI can write the report. It cannot sign it.
A quick example: my own job
Here is my own job as a data engineer, marked for today:
AI alone: Nothing yet.
AI with me: Writing the transformation code, building pipelines, tests and documentation, monitoring.
Not AI: Talking to the source experts, agreeing on data contracts, consulting the consumers of our data, the architecture, the data modeling, and the big incidents. It is never only fixing code.
If you can only do coding and building pipelines, then yes, a big part of your work gets automated. But the last group is all about people, sources and decisions, and this is where we engineers spend most of our time.
So will AI take your job? It depends. And it depends on you.
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What the job ads say
I checked almost 40,000 real job ads. Around 20% of the analyst ads mention AI, and around 25% of the engineer ads.
Still small, but it started.
And what do they ask? Look at the real sentences:
“Use AI tools to accelerate analysis.”
“Automate recurring reporting workflows.”
“Build pipelines for retrieval-augmented generation.”
Nobody is asking you to build AI. They ask you to be faster with it, and to build the data for it.
The new jobs say the same. AI data engineer, AI data analyst: the companies took the data jobs and put AI in front of the name.
Same core: Python, SQL, pipelines, data modeling. On top of it, LLMs, RAG and AI agents.
AI sits on top of your job. It does not replace it. And companies started naming the tools: Claude, Claude Code, Copilot, Cursor.
If they ask analysts to use AI, they are not replacing analysts.
Nobody is going to fire you tomorrow because you don’t use AI. But the companies started writing it in the ads.
But the company needs fewer of us, right?
Now you might say, okay Baraa, if I am twice as fast with AI, then the company needs half of us.
Think about data teams. We are usually small, and we cover only a small part of the company’s data.
So once AI makes us faster, the company has three options:
Reduce: Fewer people, the same small part of the data. You save cost, and nothing really improves.
Keep: The same team, now fast, so it covers a larger part of the data.
Expand: Grow the data team, because every AI project needs the data first.
What I saw is the second and the third. For years the data team was the “nice to have” at companies, with the smallest budget.
Then every AI project came and said: I can do the AI use case for you. But I need half a year just to get the data.
AI did not make us the extra. It made us the core.
Companies move very slowly
And you might still say: but my company is going to replace me with AI tomorrow anyway.
One thing you have to understand: it depends on the size. From what I see, mid-size to big companies are slow to adopt any new technique.
We don’t even need AI to see it:
The tools: We have the best tools in the world for data. Tableau, Power BI, Databricks, Snowflake, dbt.
The reality: Companies still use Excel for critical reports. Many don’t even have a clean data warehouse.
A company cannot change overnight and become AI-driven. It takes years: migrations, pipelines, governance, training.
So even though the tools exist, companies need years to really use them. With AI it is going to be the same.
Why companies cut people
But companies are cutting down. Yes, true, but look at why.
It is mostly not because a project turned completely to AI. Many companies are suffering financially right now, and they are going to use whatever reason to cut headcount.
Right now, that reason is AI.
And this is nothing new:
Before ChatGPT: Layoffs happened before it, and they are going to happen after. Budget cuts, company pivots, bad leadership.
Sam Altman: Even the CEO of OpenAI agreed that some companies blame AI for layoffs they would do anyway. Some real replacement too, he said, but a lot of it is just blame.
Klarna: They went very far with AI in customer service. Later their CEO said what you end up having is lower quality, and they started bringing humans back.
Why they are not hiring
Partly it is the same reason as the cuts, money. But here there is something new: companies are speculating.
They think they don’t need new people, because all the new projects can be handled with AI by the experts who are still inside.
Shopify made it a rule: “teams must demonstrate why they cannot get what they want done using AI.”
Salesforce: Their CEO said they are not adding any software engineers in 2025, because AI made the teams more than 30% more productive.
About a year and a half later, the same CEO said about AI: “The model still cannot operate autonomously.”
What AI does not know
And here is the problem with that speculation. AI learned from public data, and it never saw the inside of a real company.
The code: Real data projects live inside companies, never public. Search GitHub for a real lakehouse, and what you find are toys.
The knowledge: Most of it is not in documents. It is in the minds of people.
From a lakehouse I built in one of those companies, I can tell you this. We spent about three years just connecting ten source systems, more than 1,200 tables, with years of business rules on top.
So AI never saw how a real company works from inside. Data systems hit a level of complexity that you cannot just build with a few prompts.
The real question is not “can AI write SQL”. The real question is: can AI understand the whole company?
What happens when the experts leave
So let’s say the speculation works, and the experts inside carry everything with AI. Then we have a bigger issue, and this is the one that worries me most.
What happens when those experts leave the company?
Do those companies have juniors who practiced alongside them? No.
Nobody became a senior by reading about it. We all started with the small tasks, and we got them wrong again and again.
You learn when data is lying by getting it wrong a hundred times.
Those small tasks are exactly the ones AI does with us today. So if AI does all of this, how does the next engineer build that gut?
And companies look at the same tasks and say: okay, now we hire only seniors. They now expect juniors to be seniors already.
So where are the seniors of tomorrow going to come from?
Why I think companies will come back
Well, I can only speculate here, I don’t know the future. But I think it is only a matter of time until companies go back to hiring juniors.
The Financial Times made a film about this. In it, the CEO of a hiring platform took law firms as his example.
With AI, he said, they can probably do maybe 80% of what the junior lawyers do. And then came the important part:
“The average age in this law firm is 55, and so if you don’t hire people right now, the company will cease to exist in 15 years.”
I think this is the same for every team. Seniors leave, and somebody has to be ready.
They are going to need them.
And it already started. Remember Salesforce, the company that stopped hiring engineers?
In April 2026 the same CEO wrote: “We’re hiring 1,000 new grads & interns right now to ride the AI exponential.”
And the headlines?
So that is what I saw inside companies. Now compare it with social media.
A new headline every day, and most of them want you to panic: “AI is taking over!” “Developers getting replaced!” “Tech layoffs everywhere!”
Most of that is just noise, designed to trigger fear and get clicks. Before I believe one, I run three checks.
Check 1: Who is talking, and what do they sell? Three groups, three stories:
The sellers say you are fine if you use AI. Jensen Huang of Nvidia: most people will lose their job “to somebody who uses AI”, not to AI itself.
The buyers say they need fewer people. Andy Jassy of Amazon: “We will need fewer people doing some of the jobs that are being done today.”
The measurers say it is small so far. Two researchers found no measurable effect on pay or hours in Denmark in the first two years after ChatGPT.
Nvidia sells the chips. Amazon pays the salaries. The researchers sell nothing.
Check 2: Do they give you a date? I saw people saying AI replaces tech jobs in 2026, and I thought: okay, let’s see.
Well, the year is almost over, and we are all still here.
Check 3: Did they ever work inside a real company? Most of those people are either selling AI tools, or they never worked in one.
Inside, things are way more complex, and way slower.
Now to be fair, not everyone who warns is selling something. Geoffrey Hinton won the Turing Award for his work on AI, and he left Google with no product to sell.
He said: “We’re going to see it having the capabilities to replace many, many jobs.”
I take him seriously, and I am not going to tell you he is wrong about the technology. But the technology can be ready, and the company still is not.
Even Sam Altman said it in May 2026: “I’m delighted to be wrong about this. I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened.”
Over the years you develop a feeling for what is hype and what is real. Until then, the three checks do the job.
So …
So, will AI replace you? Most headlines say yes.
I say: it depends on you, and your company is slower than you think.
Your job: Take your list and ask what happens to each task. Do it this week, and again next year, because the marks change. Monitoring, for example, I expect to move to “AI alone”.
Your company: It is slow, and that gives you time. But don’t sit back, because if you stop learning, AI is going to take your job.
Your team: Faster does not mean smaller. Every AI project needs the data first, and we are the ones who prepare it.
Inside a company: Be the one who understands the domain, the business and the data, not the fastest one to write SQL. That knowledge is what AI does not have, and for a company it is gold.
Not inside yet: Push harder. Build projects, and learn to think like a senior.
A few years in: AI is going to make you fast, and it can make you forget. Keep doing the hard tasks yourself.
Already a senior: Understand the whole company, and prove the data is right. That is the human job.
The headlines: Run the three checks before you panic.
And for everyone: use AI every day. But understand what it gives you, don’t just copy and paste it.
Thanks for reading ❤️
Baraa
Also, here are 4 complete roadmap videos if you’re figuring out where to start:
Hey friends,
Hey, I’m Baraa, a Data Engineer with over 17 years experience, Ex-Mercedes Benz, where I led and built one of the biggest data platforms for analytics and AI.
Now I’m here to share it all through visually explained courses, real-world projects, and the skills that will get you hired. I’ve helped millions of students transform their careers.
Also, here are 4 complete roadmap videos if you’re figuring out where to start:
📌 Data Engineering Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap
📌 Data Science Roadmap
Hey friends —
Hey, I’m Baraa, a Data Engineer with over 17 years experience, Ex-Mercedes Benz, where I led and built one of the biggest data platforms for analytics and AI.
Now I’m here to share it all through visually explained courses, real-world projects, and the skills that will get you hired. I’ve helped millions of students transform their careers.
An absolutely amazing read!