Should I Learn AI in 2026?

Should you learn AI in 2026? Here's our take: Yes — with an 88% confidence rating. The job market demand is undeniable. AI and ML job postings grew 42% year-over-year in 2025, and even non-AI roles now require AI literacy for evaluating tools, managing AI-powered workflows, and making informed product decisions. The salary premium is real too — mid-level AI practitioners command 25 to 40% higher salaries than equivalent roles without AI skills, even in adjacent functions like product management or analytics. But here's the catch: the junior market is saturated with bootcamp graduates. Certificates alone won't cut it anymore. You need real project depth and mathematical foundations. Linear algebra, probability, and statistics aren't optional if you want to go beyond API calls and prompt engineering. Our scorecard weighs 8 factors across demand, difficulty, and opportunity. Job market demand scores highest at 9 out of 10. Learning curve is the biggest drag — AI frameworks and best practices shift every 6 to 12 months, demanding continuous re-education. Let's talk scenarios. Best case: you invest 10 hours a week for 6 months, build 3 portfolio projects using open-source models, and land a hybrid product-AI role at a 35% salary increase within a year. Realistic case: you spend 4 to 6 months on fundamentals, integrate AI into your current role through internal projects, and position yourself for a promotion or lateral move within 12 to 18 months. Worst case: you complete two online courses, struggle with math foundations, don't build projects, and end up with certificates but no practical skills. Here are your next steps: Start with Andrew Ng's AI For Everyone on Coursera — it's free to audit and takes 3 weeks. Set up Python, Jupyter, and Hugging Face Transformers for hands-on practice. Then commit to a structured specialization on DeepLearning.AI for deeper ML and DL skills. The key rule: don't chase hype — build skills you can demonstrate with working projects.