I've spent a lot of time studying neural networks, but everyone seems to be talking about ChatGPT. Regarding AI skills, should I keep focusing on Deep Learning fundamentals, or should I pivot entirely to learning how to build with Generative AI tools to get hired in the US?
3 answers
You shouldn't see them as mutually exclusive. In the US market, Generative AI is built on the foundations of Deep Learning. Most high-paying AI skills require an understanding of how transformers and attention mechanisms work, which are core Deep Learning concepts. If you only learn how to use an API, you'll be easily replaced. US recruiters at top-tier companies like Google or Meta look for candidates who understand the "why" behind the model's output. Keep your Deep Learning foundation strong, but definitely add a layer of Generative AI implementation to your resume to stay relevant to the current 2025 hiring trends.
Have you noticed if any specific industries in the US, like healthcare or finance, still prioritize traditional Deep Learning for predictive analytics over GenAI?
I think the best AI skills to have right now involve the ability to bridge the two—using Generative AI to create synthetic data for training Deep Learning models.
Patrick, that’s exactly what my team in Austin is doing! It’s a very niche but incredibly valuable skill set in the US right now.
Rebecca, that's a very sharp observation. In the US healthcare sector, traditional Deep Learning is still king for medical imaging and diagnostic tools where accuracy is non-negotiable. While they are exploring GenAI for administrative tasks, the core AI skills they hire for are still rooted in computer vision and supervised learning.