Meta’s VL-JEPA vs LLMs | The Next Shift in AI

In this video, we explore VL-JEPA (Vision-Language Joint Embedding Predictive Architecture), a groundbreaking research paper released by Meta and co-authored by Yan LeCun, Meta’s Chief AI Scientist. We break down: How today’s LLMs actually work Why autoregressive, token-by-token prediction has fundamental limitations What semantic space vs token space really means How VL-JEPA reasons using meaning instead of words Why Yan LeCun believes predicting the next word is not intelligence Whether this signals the end of LLMs — or a new direction for AI 📸 Follow me on Instagram: @codewithaarohihindi 🔗 https://instagram.com/codewithaarohihindi 📧 You can also reach me at: aarohisingla1987@gmail.com