AIs will achieve superhuman skill in novel domains like business or politics not by training on specific data from those fields, but by mastering the general skill of rapid learning and adaptation across millions of diverse, simulated RL environments. This skill then transfers to the real world.

The boom from LLMs was a 'shortcut' that mined intelligence from existing human data. This has limits. To achieve novel breakthroughs beyond that corpus, the field now re-integrates the original DeepMind philosophy of agents learning through interaction (like reinforcement learning) to generate truly new knowledge.

Current AI models require thousands of interactions to learn a new skill, making direct learning from real-time human feedback impractical. This inefficiency forces labs to simulate tasks and human interactions within a data center to generate the necessary volume of training data. As sample efficiency improves, learning from live deployment will become more viable.

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.

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.

It is relatively easy to create difficult, puzzle-like environments for AI training. The much harder task is to simulate realism—the complex, multi-turn, multi-objective nature of real-world interactions. Frontier labs that succeed are those that push heavily on the realism axis, which is harder to replicate than benchmark performance.

Knowledge work will shift from performing repetitive tasks to teaching AI agents how to do them. Workers will identify agent mistakes and turn them into reinforcement learning (RL) environments, creating a high-leverage, fixed-cost asset similar to software.

As reinforcement learning (RL) techniques mature, the core challenge shifts from the algorithm to the problem definition. The competitive moat for AI companies will be their ability to create high-fidelity environments and benchmarks that accurately represent complex, real-world tasks, effectively teaching the AI what matters.