Recent months have been filled with demonstrations of insanely powerful AI video generators.
Polished clips from models like OpenAI’s Sora have flooded social media, showing photorealistic scenes generated from simple text prompts.
Watching a digital mammoth trudge through a snowy landscape or a cinematic shot of a woman walking through Tokyo, it’s easy to believe that Hollywood is on the verge of being replaced by a prompt box.
Beneath these flashy demos, however, lies a much more complex and surprising reality.
While the technology is undeniably advancing at an exponential pace, its current capabilities are riddled with fundamental limitations—technical, legal, and even physical—that are rarely showcased. These aren’t minor glitches; they are core challenges that define the practical use of AI video today.
This article cuts through the hype to distill the most impactful and counter-intuitive truths about the current state of AI video generation. From consistency and copyright to physics and fair use, these are the surprising facts that demos don’t show you.
The Real Challenge Isn’t Just Making a Video—It’s Making It Consistent
The single biggest technical hurdle for AI video today is maintaining consistency. While generating a single, realistic 10-second clip is now possible, the models “do not remember any details about the scenes they just generated.” This lack of memory means that creating a longer narrative or even extending a single scene is a monumental challenge.
Imagine generating a clip of Darth Vader walking down a hallway. Now, try to create the next clip where he raises his lightsaber. The model will likely generate a completely different-looking character, with a different voice, in a completely changed background. This “character inconsistency” is a perfect example of the problem. Because the AI has no memory of the first clip, it regenerates everything from scratch, breaking the illusion of a continuous scene.
This isn’t a one-click process. A typical workflow requires a tool like Midjourney or Whisk for the initial character image, another tool to place it in a starting frame, a video generator like Flow, and finally an audio tool like ElevenLabs for the voice—all before a final edit.
However, this challenge has become the central battleground where top models are competing. Companies are actively developing features to solve it, such as Runway Gen-4’s “Multi-scene consistency” and “character preservation” functions or Sora 2’s “Exceptional scene continuity.”
AI Can’t Copyright Its Own Creations
A fundamental truth that often gets lost in the creative excitement is that works created solely by artificial intelligence are not protected by copyright in the United States. This isn’t a new legal gray area but a long-standing position from the U.S. Copyright Office, which maintains that copyright protection requires a human author.
The issue was famously tested with the graphic novel Zarya of the Dawn. While the author, Kris Kashtanova, wrote the text and arranged the images, the images themselves were generated by the AI tool Midjourney. After initially granting a copyright, the U.S. Copyright Office partially canceled the registration, stating that the images were “not the product of human authorship.”
The text and the arrangement of the elements remained protected, but the AI-generated images themselves entered the public domain. This ruling carries significant implications: any studio or creator looking to build valuable, defensible intellectual property—like a franchise character—cannot rely solely on AI-generated visuals, as those core assets would be unprotectable.
The official guidance is clear: if the “traditional elements of authorship” are executed by a machine in response to a prompt, the work lacks human authorship. A human giving instructions is not enough to be considered the author, meaning the resulting creation cannot be registered for copyright.
AI Is a Terrible Physicist
Despite their ability to create visually stunning scenes, AI video models have a surprisingly poor grasp of real-world physics.
This isn’t just an anecdotal issue; a comprehensive benchmark study called PhyWorldBench systematically evaluates models on their adherence to the laws of physics, finding that even top models prioritize “cinematic aesthetics over strict physical realism.” They are trained to create videos that look good, not ones that necessarily obey the laws of nature.
This leads to a host of subtle but jarring physical errors. Objects often move with “unnatural acceleration” or descend too smoothly, floating when they should be falling under the force of gravity. Models particularly struggle to depict “erratic visual changes.” For example, one test showed a glass cup falling from a table. Instead of shattering upon impact, the cup remained intact and was “deformed like plastic.”
Even top-tier models like Sora generate videos with “exaggerated” movement that enhances dramatic quality but breaks physical plausibility. The goal is often a stylized, choreographed look, which is great for artistic effect but a significant hurdle for applications requiring accurate physical simulation.
The Biggest Legal Battle Is Over AI’s “Education,” Not Its Creations
While the copyright status of AI-generated content is settled for now, the most intense legal and ethical conflict revolves around how these models are “educated.”
Generative AI tools rely on vast datasets, much of which includes copyrighted material scraped from the internet without a license. This practice has sparked numerous class-action lawsuits from artists, writers, and publishers against AI developers like Stability AI and Midjourney, alleging widespread copyright infringement.
The central legal defense used by AI companies is the doctrine of “fair use,” arguing that training models is a transformative act. However, creators argue that these tools directly harm their market.
The financial stakes in this battle are astronomical, exemplified by the landmark $1.5 billion class-action settlement in the Bartz v. Anthropic case over alleged piracy in its training data. An AI can be prompted to generate new works “in the style” of a specific artist, creating direct competition and siphoning away potential commissions without any compensation to the original creator.
The harm to artists is not hypothetical — works generated by AI image products ‘in the style’ of a particular artist are already sold on the internet, siphoning commissions from the artists themselves.
The U.S. Copyright Office weighed in on this debate in a mAy 2025 report, concluding that a fair use defense is unlikely to apply when AI outputs “closely resemble and compete with original works in their existing markets.” This suggests that the battle over training data is far from over and will be a defining legal conflict for the AI industry.
Conclusion: A Creative Partner, Not a Replacement
The reality of AI video generation is far more nuanced than the polished demos suggest. Behind the curtain of seemingly magical creation lie profound challenges of consistency, copyright, physical realism, and data rights. These are not just technical bugs to be patched but fundamental hurdles that touch upon the very nature of creativity, authorship, and law.
The current state of AI video is that of a powerful but flawed tool, one that requires a complex, multi-step workflow to wrangle its capabilities into a coherent final product.
The core tension, therefore, lies in whether these models are learning to understand the world or simply becoming peerless statistical chameleons. As these tools overcome their current limitations, will they become true creative partners that augment human artistry, or will their very nature—built on imitating existing data—forever limit them to being sophisticated mimics?
