The AI Job Posting Paradox: When Buzzwords Meet Reality
I’ve been noticing something increasingly frustrating in my corner of the IT world lately. Every job posting I come across, even for the most mundane technical roles, seems to have “AI experience” slapped on as a requirement. It’s like someone in HR discovered a new magic word and decided to sprinkle it on everything like fairy dust.
The whole situation reminds me of those early 2000s job ads that demanded “5 years of experience in a technology that had only existed for 2 years.” Except now it’s worse, because at least back then people generally understood what they were asking for, even if the timeline was unrealistic.
What really gets under my skin is the disconnect between what companies are asking for and what they actually understand about AI. I’ve been in enough DevOps environments to know that most organisations can barely manage their existing tech stack, let alone implement meaningful AI solutions. Yet somehow, every junior developer position now needs someone who can “leverage machine learning for business outcomes” or some equally vague nonsense.
The reality is that most of these companies have no clue what they want AI to do for them. They just know that their competitors are talking about it, their shareholders are asking about it, and there was probably a consultant who told them they needed it. It’s the classic case of solution looking for a problem, except now it’s affecting real people trying to find real jobs.
I’ve seen firsthand how this plays out in Melbourne’s tech scene. Companies are hiring “AI specialists” to essentially use ChatGPT for writing documentation or automating basic tasks that could be done with a simple Python script. Meanwhile, genuinely skilled developers are being passed over because they can’t demonstrate experience with whatever flavour-of-the-month AI tool the hiring manager read about on LinkedIn.
The most concerning part is when this AI-washing extends into areas where it genuinely shouldn’t. Medical diagnosis, financial advice, safety-critical systems – all areas where the stakes are too high for the current generation of AI tools, particularly the large language models that most people think of when they hear “AI.” There’s a world of difference between using computer vision to assist radiologists (which has been happening successfully for years) and letting a chatbot make medical decisions.
What frustrates me most is the short-term thinking driving all this. Companies are making hiring decisions based on AI buzzwords because it looks good to investors, not because they have any coherent strategy for using these tools effectively. It’s the same mindset that led to the dot-com bubble, except this time we’re potentially compromising the job market for an entire generation of workers.
The irony is that the most valuable “AI experience” right now might be knowing when not to use AI. Understanding the limitations, recognising the appropriate use cases, and being able to implement traditional solutions when they’re more suitable – these are the skills that will actually matter in the long run. But try putting “Expert in knowing when AI is inappropriate” on your CV and see how far that gets you.
Maybe what we need is a bit more honesty in these job postings. Instead of demanding vague “AI experience,” companies should specify what they actually want. Do they need someone who can fine-tune a model? Someone who can integrate with existing APIs? Or do they just want someone who knows how to use ChatGPT without embarrassing the company?
Until we get that clarity, I suspect we’ll keep seeing this mismatch between what companies think they want and what they actually need. The good news is that bubbles eventually burst, and when this one does, we might finally get back to hiring people based on their ability to solve real problems rather than their familiarity with the latest tech trend.
For now, I suppose we’ll all just have to learn to speak AI fluent enough to get through the interview process, then quietly get on with doing the actual work that needs doing.