Google DeepMind's Veo 3 video generation model has demonstrated an unexpected ability: it can recreate realistic physical phenomena like buoyancy without being explicitly trained to do so. However, a major new Stanford University report suggests this apparent understanding may be more superficial than it appears, raising important questions about what AI models actually know versus what they merely simulate .

What Can Veo 3 Actually Do With Physics?

Veo 3, Google's latest video generation model, has garnered attention for successfully recreating physical phenomena in generated videos without specific training on those concepts. The model appears to understand how objects behave in water, how gravity affects motion, and other fundamental physical principles. This capability emerged naturally from the model's training on vast amounts of video data, suggesting the system had somehow internalized rules about how the physical world works .

The achievement seemed to indicate that modern AI video generators might be developing genuine understanding of physical laws. Researchers and observers wondered if this represented a breakthrough in AI reasoning about the real world. Yet the Stanford report offers a more cautious interpretation of what's actually happening inside these models.

Why Experts Say AI Understanding Remains Limited?

Stanford University's Institute for Human-Centered AI released its 2026 AI Index Report, which specifically examined how well AI agents perform on complex reasoning tasks. The findings paint a sobering picture of AI's actual capabilities, even as models like Veo 3 produce impressive outputs. The report assessed AI performance on the PaperArena benchmark, which measures the ability to analyze research papers and derive correct answers. Even the top-performing AI agent achieved only 39% accuracy, roughly half the level of a Ph.D.-level human expert .

More troubling, top-tier AI models were found to misread analog clocks with a 50% error rate, suggesting that basic visual understanding remains unreliable. These findings suggest that while Veo 3 can generate convincing videos of physical phenomena, the model may not possess the kind of deep, generalizable understanding that humans have. Instead, it may be pattern-matching based on statistical regularities in training data rather than grasping underlying physical principles .

"Agents are great, but we still have a long way to go to understand how to use them effectively," stated Yolanda Gil, a computer scientist at the University of Southern California who led the report.

Yolanda Gil, Computer Scientist at the University of Southern California

How to Interpret AI's Apparent Understanding

• Pattern Recognition vs. True Understanding: AI models like Veo 3 excel at recognizing statistical patterns in training data, which can create the illusion of understanding physical laws. When the model generates realistic buoyancy effects, it may be reproducing patterns it observed in thousands of videos rather than applying learned physics principles.
• Benchmark Performance Gaps: The significant gap between AI performance on complex reasoning tasks (39% accuracy) and human expert performance (roughly 78% accuracy) suggests that apparent understanding in one domain does not translate to robust reasoning across domains.
• Generalization Limitations: While Veo 3 handles familiar scenarios well, the Stanford report indicates AI systems struggle with novel situations or tasks that require applying principles in new contexts, a hallmark of genuine understanding.

The distinction matters because it affects how we should use and trust AI systems. If Veo 3's physics capabilities are pattern-based rather than principle-based, the model might fail in edge cases or unusual scenarios that humans would handle intuitively. This has real implications for using AI in scientific research, engineering, or any field where understanding the underlying principles is critical .

The Stanford report noted that while AI-generated content has surpassed human-written content online for the first time, and the number of scientific papers mentioning AI exceeded 80,000 in 2025, a 26% increase from 2024, the actual research performance of AI agents falls short of expectations. Physics had the most AI-related publications at 33,000, while earth sciences had the highest proportion of papers mentioning AI at 9% of the total .

"There is a heated debate about whether this explosive growth in AI use is meaningful. It is happening so fast that scientific norms don't have time to adapt, which is leading to a decline in research quality," noted Arvind Narayanan, a computer science researcher at Princeton University.

Arvind Narayanan, Computer Science Researcher at Princeton University

The takeaway is nuanced. Veo 3 and similar models represent genuine advances in generating realistic video content, and their ability to produce physically plausible scenarios is impressive from an engineering standpoint. But the Stanford report suggests we should be cautious about interpreting this capability as evidence that AI has achieved true understanding of physics or complex reasoning. The models are sophisticated pattern-matchers, not physicists. As AI adoption accelerates across scientific fields, distinguishing between apparent understanding and genuine comprehension becomes increasingly important for researchers and organizations relying on these tools .