Companies like Seeing Machines build a powerful competitive advantage through decades of collecting naturalistic, real-world data (e.g., hours of driving footage). This proprietary dataset proves superior to modern synthetic data models, which often fail when applied to unpredictable, real-world environments.

The strategy's focus on AI simulation acknowledges a key risk: AI systems can develop winning tactics by exploiting unrealistic aspects of a simulation. If simulation physics or capabilities don't perfectly match reality, these AI-derived strategies could fail catastrophically when deployed.

Overly prolonged lab test and evaluation cycles prevent AI capabilities from ever experiencing real-world field conditions. Because enemies, environments, and warfighters dynamically alter operational realities, pre-deployment models cannot be tuned to absolute perfection in advance. Teams must bound evaluation to ensuring basic safety and understanding risks, deploying models into the field quickly so real exposure drives adaptation.

Military exercises rarely mirror the realities of actual combat, making models trained strictly on operational exercises unreliable for the next fight. Rather than trying to produce finished, highly tuned models before deployment, defense agencies must focus on the speed and reliability of the end-to-end pipeline. Practicing how quickly organizations can collect real data, retrain, evaluate, and redeploy models determines military advantage.

Synthetic data serves as an efficient first step for training specialized AI, particularly when a larger model teaches a smaller one. However, it is insufficient on its own. The final, crucial stage always requires expensive "human signal"—feedback from subject matter experts—to achieve true performance.

Neither high-fidelity game engines nor pure world models fully solve the "sim-to-real" gap for robotics training. Antioch advocates a hybrid approach: use classical simulation for what it does well, but then use real-world data to train a model that specifically learns and corrects for the simulation's inaccuracies and gaps.

Generalist CEO Pete Florence provides a tier list for robotics training data. He ranks "lived experience of the physical world" as S-tier, emphasizing the irreplaceable value of high-quality, real-world data. In contrast, he rates synthetic data from world models as F-tier, suggesting it is far less effective.

A common misconception is that simulation perfectly represents reality. In practice, it's a continuous loop: real-world data is required to tune simulator parameters, and this validation must be repeated until the gap between simulation and reality is small enough to trust the results.

The era of building frontier AI models on easily scraped internet data is ending. The next competitive advantage lies in securing unique, proprietary, real-world datasets that reflect complex physical interactions, such as endoscopy videos or 3D object data. Synthetic data is proving insufficient, making access to this "reality" data the key differentiator.