OpenAI intentionally releases powerful technologies like Sora in stages, viewing it as the "GPT-3.5 moment for video." This approach avoids "dropping bombshells" and allows society to gradually understand, adapt to, and establish norms for the technology's long-term impact.

AI generating high-quality animation is more impressive than photorealism because of the extreme scarcity of training data (thousands of hours vs. millions for video). Sora 2's success suggests a fundamental improvement in its learning efficiency, not just a brute-force data advantage.

Sora 2's most significant advancement is not its visual quality, but its ability to understand and simulate physics. The model accurately portrays how water splashes or vehicles kick up snow, demonstrating a grasp of cause and effect crucial for true world-building.

Upcoming tools like Sora automate the script-to-video workflow, commoditizing the technical production process. This forces creative agencies to evolve. Their value will no longer be in execution but in their ability to generate a high volume of brilliant, brand-aligned ideas and manage creative strategy.

While today's focus is on text-based LLMs, the true, defensible AI battleground will be in complex modalities like video. Generating video requires multiple interacting models and unique architectures, creating far greater potential for differentiation and a wider competitive moat than text-based interfaces, which will become commoditized.

The Sora team views video as having lower "intelligence per bit" compared to text. However, the total volume of available video data is vastly larger and less tapped. This suggests that, unlike LLMs facing a data crunch, video models can scale with more data for a very long time.

Proficiency with AI video generators is a strategic business advantage, not just a content skill. Like early mastery of YouTube or Instagram, it creates a defensible distribution channel by allowing individuals and startups to own audience attention, which is an unfair advantage in the market.

Traditional video models process an entire clip at once, causing delays. Descartes' Mirage model is autoregressive, predicting only the next frame based on the input stream and previously generated frames. This LLM-like approach is what enables its real-time, low-latency performance.

The OpenAI team believes generative video won't just create traditional feature films more easily. It will give rise to entirely new mediums and creator classes, much like the film camera created cinema, a medium distinct from the recorded stage plays it was first used for.