Is the AI Era of Pretraining Truly Ending, or Just Evolving? 🤔

On one side, Google DeepMind is ramping up efforts to create generative models that simulate the physical world. They’re betting on large-scale pretraining with video and multimodal data as a critical path toward AGI. Their team is focusing on:

✅ Visual reasoning and real-world simulation 🏙️
✅ Planning for “embodied” agents (think robots and beyond) 🤖
✅ Interactive real-time experiences (gaming, AR/VR) 🎮

Meanwhile, Ilya Sutskever suggests we may be hitting the ceiling of current pretraining methods:

⚠️ The internet isn’t an infinite data source 🌐
⚠️ We’re nearing the limits of available information for large-scale models
⚠️ Existing approaches might need a radical shift to go further

Yet both perspectives converge on key themes:

💡 Importance of World Simulation: Whether it’s DeepMind’s “world models” or Sutskever’s emphasis on synthetic data and autonomous agents, simulating the environment is a major goal.

🚀 A Critical Moment for AI: We’re at a tipping point, demanding new paradigms and breakthroughs in how we train and deploy intelligent systems.

What Could the Future Hold?

A synthesis of both approaches! We’ll see ongoing development of current pretraining methods (especially on richer data sources like video and multimodal inputs), coupled with fresh paradigms:

🔹 Self-governing AI agents 🏃
🔹 Synthetic data generation 🧪
🔹 New ways to handle and interpret information 🧠

DeepMind’s world models might become the perfect bridge between established techniques and the new frontiers that Sutskever envisions—autonomous AI systems with genuine reasoning and self-directed learning.

So, Are We Looking at the “End” of Pretraining?

Maybe not the end, but definitely the next chapter. There’s a good chance both approaches are stepping stones toward more powerful and flexible AI.