The history of AI, such as the 2012 AlexNet breakthrough, demonstrates that scaling compute and data on simpler, older algorithms often yields greater advances than designing intricate new ones. This "bitter lesson" suggests prioritizing scalability over algorithmic complexity for future progress.
AR and robotics are bottlenecked by software's inability to truly understand the 3D world. Spatial intelligence is positioned as the fundamental operating system that connects a device's digital "brain" to physical reality. This layer is crucial for enabling meaningful interaction and maturing the hardware platforms.
Current multimodal models shoehorn visual data into a 1D text-based sequence. True spatial intelligence is different. It requires a native 3D/4D representation to understand a world governed by physics, not just human-generated language. This is a foundational architectural shift, not an extension of LLMs.
Creating rich, interactive 3D worlds is currently so expensive it's reserved for AAA games with mass appeal. Generative spatial AI dramatically reduces this cost, paving the way for hyper-personalized 3D media for niche applications—like education or training—that were previously economically unviable.
Historically, computer vision treated 3D reconstruction (capturing reality) and generation (creating content) as separate fields. New techniques like NeRFs are merging them, creating a unified approach where models can seamlessly move between perceiving and imagining 3D spaces. This represents a major paradigm shift.
AI's evolution can be seen in two eras. The first, the "ImageNet era," required massive human effort for supervised labeling within a fixed ontology. The modern era unlocked exponential growth by developing algorithms that learn from the implicit structure of vast, unlabeled internet data, removing the human bottleneck.
When LLMs became too computationally expensive for universities, AI research pivoted. Academics flocked to areas like 3D vision, where breakthroughs like NeRF allowed for state-of-the-art results on a single GPU. This resource constraint created a vibrant, accessible, and innovative research ecosystem away from giant models.