Can LLMs Ever Grasp Physics and Cause-and-Effect Without Embodiment?

Can LLMs Ever Grasp Physics and Cause-and-Effect Without Embodiment?

In a widely shared post on Reddit’s r/ChatGPT, user /u/IonHawk posed a provocative question that has reignited a foundational debate in artificial intelligence: Can large language models (LLMs) ever understand basic physics or cause-and-effect relationships without embodied experience?

The post, accompanied by an image of a kettlebell falling off a shelf with the caption “Woops,” humorously highlights a common AI failure — the inability to infer physical consequences from static data. While LLMs can generate fluent, even eloquent responses about Newtonian mechanics or thermodynamics, they often fail when asked to predict what happens when an object is dropped, or why a chain reaction occurs in a real-world scenario. The user’s underlying concern, echoed by dozens of commenters, is that no amount of computational power or training data can compensate for the absence of sensory interaction with the physical world.

The Embodiment Gap in AI

Current LLMs, including GPT-4, Claude 3, and Gemini, are trained on vast corpora of text, learning statistical patterns rather than causal relationships. They can describe how gravity works based on textbooks, but they cannot “feel” the weight of an object, observe its trajectory, or remember the outcome of a similar event from a previous interaction. This is what researchers refer to as the “embodiment gap.”

“Language models are brilliant mimics,” says Dr. Elena Rodriguez, a cognitive scientist at MIT’s Center for Artificial Intelligence and Society. “They can recite every law of motion, but they lack the sensorimotor feedback loop that humans and animals use to build intuitive physics. That’s not a bug — it’s a design constraint.”

The Reddit user’s reference to “embodied semantic cognition” aligns with decades of research in cognitive science, particularly the work of George Lakoff and Rafael Núñez, who argue that abstract thought is grounded in physical experience. Without a body — without touch, balance, spatial awareness, or temporal memory — AI systems remain detached from the phenomenological reality they attempt to describe.

Energy Alone Won’t Solve the Problem

The post also questions whether scaling up model size and computational energy — a trend that has defined AI progress over the past decade — can bridge this gap. “More parameters, more GPUs, more training data,” writes /u/IonHawk, “won’t give an LLM the lived experience of dropping a kettlebell and watching it hit the floor.”

Indeed, while energy-efficient architectures and multimodal models (which process images, audio, and text) are improving contextual awareness, they still operate on symbolic representations. A model that sees a video of a kettlebell falling doesn’t “understand” gravity; it recognizes patterns in pixel changes correlated with the label “falling object.”

Contrast this with a human child who, after dropping a toy several times, begins to anticipate its motion — not because they memorized equations, but because their nervous system internalized the relationship between force, mass, and acceleration through repeated physical interaction.

Where Do We Go From Here?

Some researchers are exploring hybrid systems that combine LLMs with robotic agents and simulated environments — such as Google’s RT-2 or OpenAI’s GPT-4V interacting with physics engines. These systems show promise in linking language with action, but they remain narrow, task-specific, and far from generalizable intelligence.

For now, LLMs remain powerful tools for information synthesis, creative writing, and logical reasoning within abstract domains. But when it comes to understanding the physical world — the very world humans inhabit — they remain, as one Reddit user put it, “brilliant parrots in a room full of falling kettlebells.”

The question isn’t whether AI will get smarter — it already is, in many ways. The deeper question is whether intelligence without embodiment can ever be truly intelligent — or merely sophisticated illusion.

Source: Reddit post by /u/IonHawk, r/ChatGPT, https://www.reddit.com/r/ChatGPT/comments/1r87rr1/woops_i_wonder_if_llms_will_ever_be_smart_enough/