As artificial intelligence becomes a core part of modern software, many companies are running into a growing problem. They cannot find enough developers who know how to work with AI effectively. While interest in AI-powered products is rising quickly, the workforce needed to build, deploy, and maintain these systems is not growing at the same pace.
At first glance, this appears to be a familiar tech hiring challenge. Demand is high, supply is low, and competition is fierce. But many employers say the issue goes beyond a simple shortage of developers. Working with AI requires a different way of thinking than traditional software development. Developers must understand data, statistics, and model behavior, along with how AI systems make decisions and where they can fail.
Many developers are skilled coders but have limited experience working with machine learning in real-world environments. AI systems depend on large amounts of data that can change over time, and their outputs are often probabilistic rather than predictable. When something goes wrong, there is rarely a clear error message. Debugging AI often involves identifying bias, data quality issues, or unexpected model behavior, which makes the work more complex than writing conventional application logic.
Education and training have struggled to keep pace with these demands. Many university programs still focus heavily on theory, leaving graduates with limited hands-on experience deploying AI systems in production. At the same time, the tools and platforms most companies rely on today are relatively new and evolve rapidly. This has left employers searching for developers who have learned AI skills through self-study, on-the-job experience, or alternative training paths rather than traditional degrees.
In response, some nontraditional programs have emerged to address this gap. Brian Peret, Director of CodeBoxx Academy, created CodeBoxx Academy as an intensive 16-week program designed to train participants to become proficient in AI. The program focuses on practical, job-ready skills and is aimed at helping people who have been overlooked by conventional education and hiring systems gain access to careers in technology. While not a solution on its own, initiatives like this reflect a broader shift toward rethinking how AI talent is developed.
Competition for experienced AI developers remains intense. Large technology companies, startups, and organizations in industries such as healthcare, finance, retail, and manufacturing are all recruiting from the same limited talent pool. As a result, salaries have risen sharply, and developers with AI experience often have multiple offers. Smaller companies and organizations outside major tech hubs can find it especially difficult to compete.
Hiring challenges are also made worse by internal organizational issues. Job descriptions often ask for an unrealistic combination of skills, including years of experience with tools that have only existed for a short time. In many cases, companies are unclear about what kind of AI expertise they actually need. Roles may blend software engineering, data science, and AI research into a single position that few candidates realistically match.
Even when companies succeed in hiring AI-capable developers, retaining them can be difficult. Many organizations lack the processes, infrastructure, and leadership understanding required to support AI work. Traditional development practices do not always translate well to systems driven by constantly changing data. Developers may become frustrated when expectations are unclear or when AI limitations are not well understood by decision-makers.
Some companies are beginning to adapt by investing in internal training and focusing on AI literacy rather than narrow specialization. Others are building teams with complementary skills instead of searching for rare individuals who can do everything. These approaches recognize that effective AI development is often a collaborative effort rather than the responsibility of a single expert.
The struggle to find developers who can work effectively with AI reflects a larger transition across the tech industry. Just as the rise of the internet reshaped software development decades ago, AI is redefining what it means to be a developer today. For companies, educators, and policymakers, the next step is to move beyond awareness and take concrete action by supporting new training models, setting realistic expectations, and investing in long-term skill development to ensure the AI talent gap does not become a lasting barrier to innovation.