Data: AI Skills Now Top Global Talent Shortage
For the first time, skills related to Artificial Intelligence and Machine Learning are the most in-demand globally, according to a ManpowerGroup survey of 39,000 employers. The report finds that 72% of employers report difficulty filling roles as the need for AI capabilities overtakes traditional IT and engineering skills.
- In New York City, the most in-demand AI roles at startups are Machine Learning Engineer, Data Scientist, and AI Product Manager, with strong demand for skills in Python, PyTorch, and TensorFlow. Companies like Moonshot AI and other early-stage deep-tech startups are actively hiring for these positions. - The fastest-growing skill specializations within AI include AI video generation and editing, which saw a 329% year-over-year growth in demand on some platforms, along with AI integration and data annotation. For engineers, this translates to a need for proficiency in not just building models, but also in deploying and integrating them into existing business workflows. - For engineers interested in building AI agents and automation, key frameworks to master are LangChain, AutoGen, and CrewAI. Practical skills also include calling LLM APIs from providers like OpenAI and Anthropic, and using vector databases such as Pinecone and Weaviate to manage data for AI applications. - The talent shortage is driving a significant pay increase for those with specialized skills; workers with AI skills are seeing wage premiums of up to 56%. This competition for talent has led 80% of tech recruiters to believe that upskilling and retraining existing employees will be crucial for filling talent gaps. - Venture capital funding for AI startups in NYC remains strong, with firms like Andreessen Horowitz and Khosla Ventures backing local companies. Recent funding rounds include a $101M Series C for Tennr, an AI automation platform for medical documents, and a $34M Series A for Basis, which builds AI agents for accountants, signaling investor confidence in vertical SaaS AI applications. - Beyond core machine learning, employers are seeking engineers who understand MLOps for model deployment and AI architecture to grasp how individual components integrate into a larger system. Strong skills in debugging and reverse engineering are also highly valued as they are considered distinct human strengths in the AI era. - Companies are increasingly investing in upskilling their existing workforce to fill the AI skills gap. Large corporations like Microsoft, Amazon, and IKEA have launched major initiatives to train their employees in AI fundamentals, generative AI tools, and even advanced machine learning, creating new pathways for internal talent to transition into AI-focused roles.
