Why “AI Engineers” Will Be Harder to Hire Than Software Engineers

Everyone is talking about AI adoption. Boards demand it, investors expect it and product teams are racing to integrate AI features before their competitors do. New tools launch weekly and the promise is enormous: faster workflows, smarter products and entirely new business models. But there is a reality that far fewer leaders are prepared for: the biggest bottleneck in AI won’t be technology. It will be talent.

For the last decade, the core challenge for most companies was hiring strong software engineers. The market was competitive, but the role itself was relatively well understood. Backend, frontend, DevOps, cloud infrastructure: organizations more or less knew what they needed and where to find it.

AI changes that.

“Software engineer” is suddenly too broad. Companies don’t just need people who can build applications. They need engineers who understand how to build with AI at the core: integrating large language models, designing data pipelines, deploying models reliably, monitoring outputs, managing bias and ensuring compliance.

These are not just incremental skills. They represent an entirely new engineering layer. The emerging demand is for AI-native engineers. AI engineer are people who can combine traditional software excellence with applied machine learning, model operations and fast-evolving AI tooling.

The Supply Problem Is Real

Here’s the issue: there simply aren’t enough AI engineers and that will not change.

True AI engineers are rare, not because others aren’t capable, but because the field is still young. Universities are not producing talent fast enough. Most experienced engineers were trained before this wave of generative AI existed. And while many are upskilling, expertise takes time. The result is an extreme talent imbalance: demand is exploding, while supply is still forming. Unlike traditional software development—where talent pools have matured over decades, AI engineering is only now becoming a defined profession.

Everyone Is Competing for the Same Tiny Pool

The competition is brutal.

Tech giants are hiring aggressively. Startups are offering equity and mission-driven excitement. Enterprises are building AI divisions overnight. Governments are funding national AI initiatives. Everyone wants the same profiles and everyone wants them now.

In this environment, companies relying on conventional hiring methods are quickly falling behind. Posting a job opening and waiting for applicants is not a strategy. It’s wishful thinking.

The best AI engineers are not browsing job boards. They are already employed, heavily recruited and increasingly selective about where they invest their time.

The Hiring Model Must Evolve

This creates a new reality for CEOs and tech leaders: AI capability will not be constrained by tools, it will be constrained by access to expertise.

To win, companies must think beyond local hiring markets. Restricting recruitment to one city or one country dramatically narrows an already scarce pool.

The organizations that succeed will be those that can tap into distributed talent ecosystems such as nearshore regions, emerging AI hubs and cross-border engineering networks where high-quality expertise is growing faster than in saturated Western markets.

Accessing a variety of talent pools becomes not just a staffing solution, but a strategic enabler: a way to access rare skills, scale faster and stay competitive in a world where AI cycles move at unprecedented speed.

The Difference is in Doing

Every company will have access to similar AI tools. The differentiator will be execution. And execution depends on people.

Winning with AI will depend less on which model you choose, and more on whether you can build the teams capable of applying it responsibly, quickly and at scale. In the AI era, talent is not just a resource. It is the constraint. And it is the advantage.