AI's New Frontier: Post-Training Proves More Valuable Than Ever

In the rapidly evolving world of artificial intelligence, a significant shift is underway. Recent developments suggest that the focus on post-training—refining raw AI intelligence into practical, productive output—is yielding higher returns than the traditional emphasis on pre-training. This revelation, spurred by innovations from China's Deepseek, could redefine how we approach the journey to achieving Artificial General Intelligence (AGI). As we delve into this transformative trend, let's explore what it means for the future of AI and its impact on the tech landscape.

The Crude Oil of AI: Understanding Pre-Training

Imagine the internet as a vast ocean of data. Pre-training is akin to turning this ocean into tokens, the building blocks of AI's understanding. These tokens, essentially the parameters that dictate the resolution of an AI model's knowledge, are the raw, unrefined intelligence—the crude oil of AI. Without direction or refinement, pre-training alone is like having gasoline without an engine; it's all potential, but no combustion.

The Engine of Progress: The Power of Post-Training

Enter post-training, the process of extracting and directing this raw intelligence into productive output. It's the engine that converts gasoline into motion. Recent insights from experts like Jaz suggest that the investment in post-training is proving more fruitful than ever. The release of Deepseek's R1 model in China highlighted this trend, showcasing how reinforcement learning and reasoning can significantly enhance an AI's capabilities. This shift is not just a minor adjustment but a pivotal moment in AI development.

Breaking Through the Progress Wall

Historically, there's been a fear that AI development would hit a "progress wall," a point where additional resources would no longer yield proportional improvements. However, as Josh points out, the latest data suggests we're still ascending this curve without hitting that wall. The leak of Deepseek's R2 model further underscores this progress, demonstrating that despite constraints, China's approach to AI is pushing boundaries.

The implications of this shift are profound. For one, it suggests that the path to AGI might be more about refining what we already have rather than scaling up pre-training efforts. This could lead to more efficient use of resources and faster advancements in AI technology.

Market analysts are watching closely. The tech industry, already buzzing with AI developments, is likely to see a surge in investments focused on post-training solutions. Companies that can harness this trend effectively could gain a significant competitive edge, potentially reshaping the AI landscape.

Looking ahead, the future of AI seems to be less about accumulating more data and more about how we use it. As we continue to refine our engines of progress, the journey to AGI might be closer than we think. The key will be in how well we can navigate this new frontier of post-training, turning raw intelligence into the innovations that will define the next decade.