We need to stop blaming AI for the majority of tech job losses. The data tells a more nuanced story. Since 2022, tech has seen hundreds of thousands of layoffs. Many announcements pointed to AI-driven efficiency. Yet the evidence shows something different:
- Tech job postings in emerging markets remain ~47% above pre-pandemic levels, while the US and most advanced economies are still down roughly 35% (Indeed Hiring Lab).
- Emerging markets’ share of global tech postings rose from 16% in 2020 to 28% by mid-2025.
- India alone now hosts 1,700–1,900+ Global Capability Centers employing close to 2 million people — many of them for the same companies that cut roles in higher-cost countries.
- A widely cited MIT analysis found that 95% of generative AI pilots delivered no measurable impact on profit and loss.
In short, a large portion of the workforce shift looks more like classic cost arbitrage and composition change than pure automation. “AI-washing” has become a recognized term for this pattern.
That doesn’t mean AI is irrelevant. It means the public narrative has often been cleaner than the underlying economics.
So what should professionals do?
Stop treating AI as a distant threat or a media spectacle. The most practical response is to use it daily in your actual work. Learn how to apply it, build with it, and fold it into your specialty — whether that’s engineering, product, design, operations, or analysis. First-hand experience is the fastest way to separate useful signal from noise.
The people who treat AI as a tool they actively shape, rather than a force that simply happens to them, will keep opening doors even as the landscape continues to shift.
I’ll argue that it will actually generate far more “jobs” in the future, but that’s for another post.
What has your own experience with AI tools looked like so far?