When companies like iQiyi talk about using AI to produce the majority of their content within the next five years, the pitch almost sells itself. Faster production, lower costs, more scalable creativity, endless content supply. It is the sort of vision executives love because it sounds futuristic and efficient at the same time.
But there is a problem with that narrative: it assumes AI-generated video is naturally cheaper than traditional production. Right now, that feels more like a slogan than a proven business reality.
The idea makes intuitive sense at first. Replace parts of the filmmaking pipeline with AI, cut labor costs, speed everything up, and suddenly content creation becomes more efficient. Simple, right? Well, not exactly. Video is one of the most resource-hungry forms of media AI can attempt to generate, and once you examine the technical demands, the “AI is cheaper” argument starts wobbling pretty quickly.
The Core Assumption Deserves More Skepticism
A lot of people hear “AI-generated content” and immediately think “lower cost.” That might be true in some areas like text, image mockups, or basic design assistance. Video is a different beast entirely.
A single second of video at 24 frames per second means generating 24 images. Not just 24 unrelated images, either, but 24 images that remain visually coherent from one frame to the next. Characters need to stay consistent. Lighting needs to behave. Motion needs to make sense. Objects cannot randomly melt, multiply, or phase into the void halfway through a shot, which, to be fair, is still something AI video occasionally treats as a creative choice.
Stretch that to one minute, and now you are talking about 1,440 coherent frames.
That is where the “cheap” narrative starts to look suspiciously incomplete.
Generating one striking AI image is already computationally expensive. Generating a full minute of moving, coherent video is a much bigger challenge. And that is before we even get into output quality, editing control, rendering time, or the resolution demands expected by real streaming audiences.
Video Is Not Just More Frames. It Is More Complexity
The problem is not simply the quantity of frames. It is the relationship between them.
AI video does not just need to create images. It needs to preserve continuity across time. That means stable faces, consistent body proportions, believable motion, object permanence, spatial logic, and enough understanding of physics that viewers do not feel like they are watching a dream assembled by an exhausted machine.
That is still a major weakness in current AI video systems.
Even now, generated video often suffers from:
- deformities
- visual anomalies
- flickering details
- inconsistent motion
- broken physics
- unstable objects and environments
And once quality expectations go up, the resource demands go up with them.
A 1080p video requires more compute than 720p. Push toward higher fidelity, longer duration, better motion consistency, or more directorial control, and the costs rise again. So while AI advocates often talk about production savings, they sometimes skip over the part where video generation remains one of the most technically expensive things you can ask a model to do.
The OpenAI Problem Is Hard to Ignore
If AI video were already such an obviously viable business, you would expect the biggest and best-funded AI labs to have turned it into a clear commercial win by now. But that does not seem to be the case.
A while back, OpenAI shut down broad public access to Sora video generation in the way many people expected it to scale, which raises a pretty uncomfortable question for the rest of the industry: if even OpenAI, with elite researchers, massive investment, top-tier infrastructure, and access to cutting-edge GPUs, has struggled to make AI video work as a broadly viable business, why should everyone else assume they can do better?
That does not mean AI video is impossible. It does mean the economics may be far less favorable than the hype suggests.
And that matters when companies talk as if AI-generated entertainment is just around the corner and ready to replace large chunks of conventional production.
Even Big Tech Ownership Does Not Automatically Solve the Problem
iQiyi does have one major advantage: backing from Baidu, one of China’s biggest technology companies. That gives it resources, infrastructure, and strategic support that many smaller players would kill for.
But even that does not erase the broader concern.
Having a powerful parent company is not the same as having world-leading AI video capability. Search, streaming, and generative video are not interchangeable skill sets. AI video sits at the intersection of massive compute, model research, infrastructure optimization, and production workflow integration. That is a specialized challenge.
So yes, iQiyi has serious backing. But compared with companies whose entire identity revolves around frontier AI, it is fair to question whether it has the same depth of expertise, especially in one of the hardest corners of generative media.
And if the top labs themselves are still wrestling with cost, scale, and quality, companies further down the stack have even less margin for error.
Quality Is Where the Cheapness Argument Starts Falling Apart
Even if a company can generate video at scale, there is still the matter of whether the result is actually good enough.
This is the part that often gets glossed over in AI business discussions. People say AI will reduce production costs, but reduced relative to what standard of quality?
Because producing a rough, surreal, glitch-prone video is one thing. Producing a polished, commercially useful, audience-ready video is something else entirely.
To get from “the model generated a video” to “this is a high-quality film or show people would pay to watch,” you may still need:
- repeated generations
- prompt iteration
- cleanup work
- editing and post-production
- human supervision
- quality control
- style correction
- scene selection and reworking
At that point, the supposedly cheaper workflow starts accumulating hidden costs. Compute costs. Labor costs. Time costs. Creative revision costs.
So the real comparison is not AI versus nothing. It is AI versus a conventional production pipeline that, for all its expense, is at least stable, predictable, and capable of delivering quality without random anatomical disasters.
Checkout my other article: AI Didn’t Kill Journalism—Humans Did
Real-World Filmmaking Tools Have a Huge Advantage: They Already Work
Traditional filmmaking has costs, yes. You need actors, crew, cameras, lighting, locations, editing tools, and all the usual logistical headaches. Nobody is pretending production is cheap.
But real-world tools have one major advantage over AI video: they do not need to invent reality from scratch.
A camera capturing a human actor does not suddenly decide the actor has six fingers in one take and four in the next. Lighting setups do not hallucinate. A physical lens does not forget what a chair is halfway through a scene.
Even more importantly, increasing recording quality in traditional production is often far less painful than increasing quality in AI generation. Shooting in 4K instead of 1080p with real cameras is generally a manageable technical upgrade. In AI generation, pushing quality upward can mean significantly more computational burden, more instability, and more cost.
That creates an awkward contrast for the “AI is cheaper” camp.
In some cases, using real cameras and real people to produce a decent short film may actually be more cost-effective than trying to force a model to generate high-quality video that still needs human cleanup afterwards.
Human Production May Still Be the Better Deal
This is the uncomfortable conclusion that AI evangelists often avoid.
At the current stage of the technology, hiring human actors, crews, and production staff may still be the more economical path for making high-quality video, especially if the goal is consistency and commercial usability rather than novelty.
That does not mean AI has no role. It clearly does.
AI can help with concept work, storyboards, previsualization, editing assistance, effects prototyping, background generation, localization, and other support functions. In those areas, the economics are much easier to defend.
But jumping from “AI is useful in parts of production” to “AI will soon make most films and shows more cheaply than humans” is a much bigger leap. Right now, that leap looks more aspirational than proven.
From a Business Perspective, the Risk Is Obvious
From the business side, companies like iQiyi are not crazy for exploring AI. They are under pressure to cut costs, increase output, and stay relevant in an increasingly fragmented entertainment market. That incentive is real.
But the danger is that they may be chasing a future whose economics are still deeply uncertain.
If AI video remains expensive to compute, difficult to control, and inconsistent in quality, then betting too heavily on it could create the opposite of efficiency. Instead of lowering costs, companies may end up adding a new layer of expensive experimentation on top of existing production challenges.
That is especially risky in streaming, where margins are already tight and audience attention is brutally competitive.
A technology can be impressive and still not be a good business.
From the User Perspective, Quality Matters More Than Process
Most viewers do not care whether a scene was made by a camera crew or generated in a data center. They care whether it looks good, feels coherent, and tells a compelling story.
If AI video cannot consistently reach that standard, then the production method becomes irrelevant. Cheap content that looks strange is not saved by the fact that it came from an advanced model.
In fact, users may be less forgiving of AI-generated entertainment precisely because it is being sold as the future. If the output still contains weird motion, broken details, and visual instability, audiences will notice quickly.
So even from the viewer’s perspective, the real issue is not whether AI can produce more content. It is whether it can produce content good enough to justify the effort and the cost.
The Smarter Near-Term Use of AI Is Probably Assistance, Not Replacement
That is why the most realistic near-term role for AI in entertainment is probably not full replacement of human production. It is targeted assistance.
AI makes more sense as a tool for:
- previsualization
- planning
- rapid prototyping
- asset ideation
- production support
- post-production enhancement
That is a much more grounded path than assuming AI video can already outcompete traditional filmmaking on cost and quality.
Because at the moment, the strongest argument for AI in content is not “it can replace everything.” It is “it can help humans do some things faster.”
That is useful. It is also a lot less revolutionary than the marketing decks suggest.
Final Thought
The idea that AI-generated video will be dramatically cheaper than conventional filmmaking has become one of the industry’s favorite assumptions. But assumptions are doing a lot of heavy lifting there.
Video is one of the hardest media formats for AI to generate well. It demands not just volume, but continuity, coherence, quality, and control. Those things require enormous resources. And if companies with the best funding and deepest expertise still struggle to make the economics work cleanly, the case for everyone else becomes even shakier.
So yes, platforms like iQiyi can bet big on AI video. But whether that bet is actually cheaper, scalable, and commercially sensible is a much tougher question than the hype cycle usually admits.
At least for now, the old-fashioned method of pointing a camera at real people in the real world still has one enormous advantage: it works.
