If you're a student right now, the narrative about AI and the job market is terrifying. Every other headline says entry-level jobs are disappearing. Junior analyst roles are being automated. Companies are hiring fewer new grads and expecting the ones they do hire to produce at a senior level from day one. The message is clear: AI is eating your future.
I don't buy it. Not because the disruption isn't real — it is — but because the narrative misses something important about what our generation actually brings to the table.
Yes, Entry-Level Jobs Are Changing
Let's be honest about the problem first. The traditional entry-level knowledge work pipeline — where you get hired to do research, build spreadsheets, write memos, and gradually take on more responsibility — is under real pressure. A lot of the tasks that used to fill a junior employee's day can now be done by an LLM in seconds. Data cleaning, first-draft writing, basic analysis, code boilerplate — these were the training ground for a whole generation of professionals, and they're being automated.
This is genuinely disruptive. If the entry-level rung of the ladder disappears, how do you climb? How do you learn the tacit knowledge that only comes from doing the grunt work? These are legitimate questions, and I don't think anyone has fully answered them yet.
But here's what I keep coming back to: the people most worried about this are the people who learned to work without AI. They see AI as something that replaces what they already know how to do. For students, it's different. We're not losing tools we relied on — we're starting with a different toolkit entirely.
AI-Native Isn't Just a Buzzword
There's a real difference between someone who learned to work in a pre-AI world and then adopted AI tools, versus someone who's learning to work with AI from the start. It's similar to the difference between digital immigrants and digital natives. Both can use a smartphone, but they use it differently.
Students today are building their first professional skills with AI as a given. We're not asking "should I use ChatGPT for this?" — we're asking "how do I use it well?" We're developing intuitions about when to trust AI output and when to question it. We're learning to prompt effectively, to iterate, to use AI as a thinking partner rather than a magic answer machine. These are skills that most senior professionals are still figuring out.
I've seen this in my own work. When I'm building something — a report, a data pipeline, an analysis — my workflow naturally incorporates AI tools in ways that feel native, not bolted on. I'm not translating from a pre-AI workflow; I'm building the workflow with AI baked in from the start. This matters more than people realize.
The Leverage Is Enormous
Here's the optimistic case: AI doesn't just eliminate entry-level tasks, it gives entry-level people senior-level leverage. A student with good judgment and strong AI skills can now produce work that would have required years of experience to create. Not because the student is smarter, but because the tools are more powerful.
A junior analyst who knows how to use AI well can process and synthesize information at a scale that was previously impossible. A student developer can build and ship products that would have taken a team. A young marketer can produce and test campaigns at a speed that veterans couldn't match manually.
The catch, obviously, is judgment. Tools give you leverage, but leverage without judgment is dangerous. You can produce a lot of bad work very quickly with AI. The students who will thrive are the ones who combine AI fluency with genuine critical thinking — who use AI to accelerate good judgment rather than to substitute for it.
What We Should Actually Be Worried About
The real risk for students isn't that AI makes us irrelevant. It's that we mistake AI fluency for actual expertise. Knowing how to prompt an LLM doesn't mean you understand the domain. Being able to generate a financial analysis with AI doesn't mean you understand finance. The tool can write the memo, but it can't tell you whether the memo is asking the right question.
This is where the "entry-level experience" problem becomes real. If you never do the grunt work, you might miss the deep domain understanding that the grunt work used to provide. The answer isn't to avoid AI and go back to doing everything manually — that's just nostalgia dressed up as wisdom. The answer is to be intentional about what you're learning. Use AI to skip the repetitive parts, but don't skip the understanding.
I try to do this myself. When AI generates code for me, I read it and make sure I understand what it's doing. When it produces an analysis, I check the reasoning, not just the output. The goal isn't to do everything the hard way — it's to build real understanding even when the tools make it tempting to skip ahead.
The Generational Bet
Every generation enters the workforce during some kind of disruption. Millennials entered during the financial crisis. Gen X entered during the dot-com transition. Our generation is entering during the AI transition. It feels overwhelming right now, but the people who enter a new paradigm early tend to be the ones who shape it.
We're not competing with AI. We're the first generation that gets to build our entire careers with it. That's not a disadvantage — it's a head start. The students who recognize this and invest in both AI fluency and genuine expertise will not just survive the AI world. They'll define it.