After receiving access to OpenAI’s Sora and spending two weeks experimenting with it, I’ve become convinced that we’re standing on the edge of a transformation so deep it’s difficult to fully grasp, even as it happens in real time. The debut and rapid advancement of generative AI video tools, led by Sora, have sparked two powerful reactions at once: awe and genuine anxiety.
I found making short videos with Sora to be strikingly easy and intuitive. I simply described a scene where I stepped up to bat at Yankee Stadium in Game 7 of the World Series and hit the walk-off home run for my beloved Yankees. Within minutes, Sora produced a cinematic rendition of that moment.
The realism of the lighting, motion, and crowd response makes it feel as though I truly lived it. The clips demonstrate a level of creative capability that once demanded a full production crew, now achievable with nothing more than a prompt and a few clicks.
On one side, the magic is undeniable — typing a few words and instantly generating entire worlds. On the other, the risks are obvious and immediate. Recent research has shown that tools like Sora 2 can be guided to produce false or misleading videos with disturbing effectiveness, forming what one expert described as “industrial-scale misinformation pipelines.”
Alongside this is the deeply personal and ethical threat of “likeness theft,” where our faces and identities are no longer under our control. As a chilling Wall Street Journal report described, a meteorologist had her image stolen and used to generate deepfakes that defrauded her followers.
These dangers are real and represent the most urgent challenge ahead. Yet beyond this ethical and security battleground, a deeper structural disruption is forming. Generative AI is directly targeting the entire entertainment production model, with the long-term potential to completely upend a multibillion-dollar industry.
From Capture to Creation
We’ve seen a version of this before, though on a smaller scale. Over the past 15 years, smartphones — especially premium devices like Apple’s Pro iPhones and Samsung’s Galaxy models — democratized video creation.
Through computational video, once-complex techniques such as stabilization, real-time color grading, and portrait-style depth effects became automated. Tools that once cost $100,000 suddenly fit in a pocket, fueling the creator economy. It’s unlikely even Steve Jobs fully anticipated this shift.
AI video, however, represents the next exponential jump. Smartphones democratized the capture of reality; generative AI democratizes its creation.
Consider what’s required for a simple scene: a 1950s detective walking down a rain-soaked street at night. That demands scouting, permits, vintage vehicles, costumes, rain machines, elaborate lighting, and a full camera crew. With generative AI, it requires only a prompt.
Or imagine an “impossible” shot — a drone flying through a skyscraper window, down a hallway, and into a teacup. Traditionally, that would involve expert pilots, constructed sets, and advanced visual effects. Now, it’s achieved through description alone.
This technology severs visual storytelling from physical constraints. It eliminates the need for locations, practical effects, and, in many cases, even human performers.
The 50-Person Blockbuster
This creative freedom triggers a massive economic shock.
For the past quarter-century, a typical professionally produced Hollywood film has employed roughly 300 to 500 people, including actors and the extensive production infrastructure: grips, gaffers, cinematographers, sound teams, location managers, transport crews, catering, and post-production staff.
In a generative AI-driven future, that figure could easily drop below 50. The idea of a traditional production crew will be fundamentally reshaped. AI will absorb much of the specialized labor tied to physical production, and AI-generated video will slash costs, pushing filmmaking further into the hands of individuals.
An independent filmmaker working from a garage could suddenly create visuals on par with a $200 million blockbuster. Capital will no longer be the barrier — imagination will.
Rise of the AI-VFX Director
This doesn’t mean talent disappears; it means talent evolves. In AI-driven filmmaking, visionaries — directors and cinematographers — become even more central. Their role intensifies as they serve as the primary bridge between human intent and AI execution.
Precision becomes the essential skill. Prompts will need to be detailed, technical, and deliberate. A director won’t say, “I want a sad man.” They’ll define the shot like a master cinematographer:
“Close-up of a 60-year-old man with deeply lined features. A single flickering fluorescent light overhead as the key light. Blue television glow as fill light from off-screen. Shot on an 85mm lens with shallow depth of field, racking focus from his eyes to the wedding ring on his hand. Isolated mood, inspired by Edward Hopper. Film grain to mimic Kodak Vision3 500T.”
The director becomes the sole source of intent, and their ability to express that intent with precision determines the final result.
The Computational Bottleneck
So why isn’t this happening immediately? Why are AI-generated clips still limited to a minute or two? The reason is simple: computation.
Producing seconds of high-quality, physically consistent video requires enormous processing power. Scaling that to a full two-hour film is currently too expensive for mainstream use.
But this limitation won’t last. Overcoming it is the focus of one of the fiercest races in technology. Companies like Nvidia, AMD, and Qualcomm are competing to build more efficient, scalable hardware specifically optimized for AI video workloads.
As data center costs drop and hardware improves, the one-minute barrier will fall, making full-length AI-generated films economically inevitable.
Toward an AI “Toy Story”
This leads directly to Hollywood, where resistance is already forming. The Screen Actors Guild and other unions can sense the threat. The ethical concerns are not hypothetical — they are real cases of actors and public figures whose digital likenesses are already being exploited.
There is also widespread doubt that AI-generated films can match traditional cinema in quality, emotion, and storytelling. Can an algorithm truly convey the human soul?
This is where the revolution needs a Trojan Horse.
A full-scale AI film designed to replace working actors today would face immediate and unified opposition. A more plausible path is one that uses AI not to replace, but to restore. Imagine revisiting a classic like the Broadway musical Man of La Mancha. The 1972 film adaptation failed critically and commercially due to casting and creative choices. But what if AI could create a new version that digitally recreates the legendary 1965 Broadway cast?
Picture seeing Richard Kiley and Joan Diener in their prime within a fully realized cinematic environment, preserving performances lost to time. That feels less like exploitation and more like cultural preservation.
This is the likely entry point. But for true acceptance, AI-generated film needs what computer animation needed in 1995: its own Toy Story moment.
When Toy Story debuted, it transformed the industry. It didn’t just earn a Special Achievement Academy Award — it proved that a fully computer-generated film could be genuine art, emotionally resonant and culturally impactful. It legitimized an entire medium.
AI-generated long-form storytelling won’t be fully accepted until it has its own equivalent — one undeniable film that forces skeptics and unions alike to admit a new art form has arrived. When an AI-created film wins its first Oscar — not for technical achievement, but for Best Picture — the shift will be complete.