LLMs have hit a wall by scraping nearly all available public data. The next phase of AI development and competitive differentiation will come from training models on high-quality, proprietary data generated by human experts. This creates a booming "data as a service" industry for companies like Micro One that recruit and manage these experts.

Pre-training on internet text data is hitting a wall. The next major advancements will come from reinforcement learning (RL), where models learn by interacting with simulated environments (like games or fake e-commerce sites). This post-training phase is in its infancy but will soon consume the majority of compute.

The core of an effective AI data flywheel is a process that captures human corrections not as simple fixes, but as perfectly formatted training examples. This structured data, containing the original input, the AI's error, and the human's ground truth, becomes a portable, fine-tuning-ready asset that directly improves the next model iteration.

Training AI agents to execute multi-step business workflows demands a new data paradigm. Companies create reinforcement learning (RL) environments—mini world models of business processes—where agents learn by attempting tasks, a more advanced method than simple prompt-completion training (SFT/RLHF).

Beyond supervised fine-tuning (SFT) and human feedback (RLHF), reinforcement learning (RL) in simulated environments is the next evolution. These "playgrounds" teach models to handle messy, multi-step, real-world tasks where current models often fail catastrophically.

The frontier of AI training is moving beyond humans ranking model outputs (RLHF). Now, high-skilled experts create detailed success criteria (like rubrics or unit tests), which an AI then uses to provide feedback to the main model at scale, a process called RLAIF.

Training models like GPT-4 involves two stages. First, "pre-training" consumes the internet to create a powerful but unfocused base model (“raw brain mass”). Second, "post-training" uses expert human feedback (SFT and RLHF) to align this raw intelligence into a useful, harmless assistant like ChatGPT.

Microsoft's research found that training smaller models on high-quality, synthetic, and carefully filtered data produces better results than training larger models on unfiltered web data. Data quality and curation, not just model size, are the new drivers of performance.

Static data scraped from the web is becoming less central to AI training. The new frontier is "dynamic data," where models learn through trial-and-error in synthetic environments (like solving math problems), effectively creating their own training material via reinforcement learning.