Is AI Taking Your Job Or Creating Jobs?

Governments say AI is creating jobs. Reports say AI is creating jobs. Tech companies say AI is creating jobs.

So why does it feel like the job market is getting harder, not easier?

Layoffs are happening. Engineers with solid experience are spending months job hunting. People are applying to dozens of roles and hearing nothing back. The on-the-ground reality for a lot of people in tech does not match the headline.

We wanted to understand that gap. Why, if AI is supposedly creating all these opportunities, is finding a job in tech in 2026 so frustrating? That question led us to dig into what's actually happening in the market.

AI Is Creating Jobs. But It's Also Cutting Them.

The honest picture is messier than either side of the debate admits.

Yes, AI is eliminating certain roles. Junior copywriting, basic data labelling, routine code review, entry-level customer support: these tasks are being done faster and cheaper by AI tools, and companies have noticed. For engineers, one person with Cursor or GitHub Copilot today can do the work that previously required three. Teams are shipping faster with smaller headcounts, and some roles, especially at smaller startups, are being cut entirely.

Watch this video where Natasha and Saloni debate about whether AI is taking or creating jobs!

We've seen this at startup pitch nights in Singapore, where more and more founders are building MVPs solo with no CTO in sight. We wrote about this in our CTO piece.

What makes this particularly uncomfortable is a finding from Harvard Business Review: companies are laying off workers because of AI's potential, not its performance. Workers are paying the price for a prediction, not a reality that's fully arrived yet.

So Where Are All These New Jobs?

At the same time, the demand for engineers is going up, not down. Every AI product needs someone to train it, fine-tune it, deploy it, and keep it running. Every company adding AI to their workflow needs ML Engineers, Data Scientists, AI Platform Engineers, and Fullstack Engineers to build the interfaces that bring these systems to users. The AI boom isn't just creating AI companies. It's adding AI functions to every kind of company, in every industry.

Image from LinkedIn data reported by the World Economic Forum

According to LinkedIn data reported by the World Economic Forum, AI has already created over 1.3 million new jobs, with AI Engineer among the fastest growing roles globally. The supply of qualified engineers hasn't kept pace. Senior ML Engineers and AI Research Scientists are rare, and companies need people who understand both what AI can do and what it can't: a harder profile to find than a standard engineering hire.

Closer to home, Singapore's IMDA has announced plans to build an AI-fluent workforce to accelerate the country's national AI ambitions. Google has been hiring 150 AI-focused roles in Singapore. These aren't small signals.

And as CACM points out, many of the jobs AI is creating are hidden ones: roles that don't have clear job titles yet, that sit at the intersection of domain expertise and AI capability, and that aren't showing up loudly on traditional job boards.

So yes, AI is creating jobs. Lots of them. The problem is that finding those jobs, and being found for them, is harder than it should be.

The Job Market Is Noisier Than Ever

Even if the jobs are there, finding them is getting harder.

Image from January 2025 Clarify Capital study

A January 2025 Clarify Capital study found nearly 1 in 3 employers admit to posting job listings with no intention of hiring. Greenhouse, a major hiring platform, separately reported that 18-22% of online job ads in 2024 were unfilled roles. Whatever the reason, engineers end up spending real time on applications that lead nowhere.

There's also a signal problem. Most job boards sort you by keywords and filter you through automated systems that can't tell the difference between a strong candidate and someone who just knows how to optimise their resume. If you're genuinely good, that's frustrating. You're competing not just on merit but on how well you've gamed the system.

On the company side, hiring managers are drowning too. Every open role gets flooded with applicants, sorting takes months, and the best candidates are often long gone by the time a decision is made. It's a bad experience on both ends.

So What Can You Actually Do?

If you're an engineer actively looking, or just keeping your options open, here's what we'd suggest.

Be selective about where you apply. Spraying applications across every job board is exhausting and mostly ineffective. Focus on platforms and communities where the roles are verified and the companies are genuinely hiring. Some newer hiring platforms only list roles from companies that have been vetted and are actively hiring, which cuts out a lot of wasted effort.

Make your signal clear and specific. Recruiters and hiring managers at AI companies aren't just scanning for job titles. They want to see what you've actually built. A strong GitHub, documented projects, and specific achievements (latency reduced by X%, model accuracy improved by Y%) matter far more than a polished resume. Treat your portfolio as your primary pitch.

Know your number before you start. One of the most common ways job searches drag on is when salary expectations only come up late in the process. Some platforms now ask companies to attach a salary range to their interest upfront, before you agree to any interviews. If a platform doesn't give you this, ask for it early. It saves everyone time.

Look for processes that give you feedback. Most hiring pipelines ghost candidates completely or send a single-line rejection. A process that tells you why you didn't progress is genuinely valuable, both for improving your approach and for not wasting time wondering. If a recruiter or platform offers structured feedback, that's a good sign they take the process seriously.

Consider curated hiring events. The traditional approach of applying one job at a time is slow and demoralising. Some platforms now run structured hiring windows where multiple companies review your profile at once and reach out if there's a fit. It completely flips the power dynamic: instead of you chasing companies, companies are expressing interest in you, with salary ranges attached upfront. This format tends to compress months of back-and-forth into 2 to 3 weeks.

Watch our episode where Saloni and Natasha interview Joanna, a recruiter, to find out the perspective from hirers!

Our Take

Whether AI is ultimately a net creator or destroyer of jobs, the process of finding your next role shouldn't be as broken as it currently is.

The engineers we've spoken to who've had the best experiences in this market share a few things in common: they were selective about where they showed up, they made their actual work visible, and they found processes that respected their time.

If you're navigating this right now, we hope some of this is useful. The market is noisy, but the signal is still there if you know where to look.

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ragTech is a podcast by Natasha Ann Lum, Saloni Kaur, and Victoria Lo where real people talk about real life in tech. Our mission is to simplify technology and make it accessible to everyone. We believe that tech shouldn't be intimidating – it should be fun, engaging, and easy to understand!

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