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.

Solving the Navier Stokes Millennium Prize problem is a significant milestone for AI capabilities. However, its practical impact on engineering is minimal, as engineers already use numerical approximations. The solution's main value lies in demonstrating AI progress and generating hype.

The current enterprise AI boom is a symptom of AI teams lacking product designers and the limitations of text-based models. A true consumer AI revolution awaits mature image and video generation, which will unlock the immersive, visual interfaces necessary for breakout consumer apps.

While public discourse on AI models often focuses on incremental improvements in common tasks like writing emails, the most profound advancements are happening in specialized fields like science and mathematics. This capability gap creates a disconnect in perceived progress.

For a generative video model like OpenAI's Sora 2 to achieve viral adoption, it needs a universally appealing, simple-to-execute prompt, much like DALL-E's "Studio Ghibli moment." A feature like "upload your profile picture and turn it into a video" would engage a mass audience far more effectively than just showcasing raw technical capabilities.

To win public trust, AI leaders should follow DeepMind's playbook: showcase power through understandable achievements (like AlphaGo) rather than citing technical benchmarks. Tangible demonstrations are more effective for storytelling than metrics that are meaningless to a non-expert audience.

Abstract benchmarks like math scores fail to resonate emotionally with the public. The true "feel the AGI" moments come from AI automating tasks that people personally understand to be difficult and time-consuming, such as 3D modeling. This experiential validation is becoming more powerful than quantitative metrics in shaping public opinion.

Moving beyond solving existing problems like the Millennium Prize problems, the true test of advanced AI in mathematics will be its ability to generate novel, interesting conjectures and create new, unifying definitions. This represents a higher tier of mathematical creativity, akin to the work of the greatest mathematicians who frame the questions for others to solve.

The public is more impressed by AI applications they can see and understand, like generating 3D models of their house, than by abstract achievements like solving complex math problems. Visceral demonstrations feel more like a real breakthrough to the average person and generate more excitement.