AI-generated video technology
AI Video Creation: Sora, Best Practices, and the Future of the Technology
The generation of videos through artificial intelligence (AI) is emerging as one of today’s major technological revolutions. Following the rise of tools that create text (such as ChatGPT) and images from descriptions, there are now models capable of producing full videos from written prompts. These tools promise to reduce costs and accelerate timelines in audiovisual production, while also raising new challenges regarding ethical use and content accuracy. In this article, we will explore the current state of this technology, delve into Sora—OpenAI’s video model—share best practices for using these AIs, warn about fraudulent uses (such as deepfakes and fake news), and analyze the future impact on fields like marketing and audiovisual production.
Current AI Video Generation Technology
Generative AI video tools have advanced rapidly. It is now possible to turn a descriptive text into a video clip without the need for cameras or real actors. Various platforms offer different approaches: from videos featuring realistic virtual avatars (for example, synthetic presenters reading a script) to fully imagined videos generated scene by scene from a text prompt. These tools allow businesses and creators to save up to 70% on production costs and reduce production time by 60%. In fact, the AI video generator market is projected to grow from \$534.4 million in 2024 to \$2.56 billion by 2032, transforming the way visual content is created. In other words, tasks that once required recording studios and large budgets are now being democratized, available to any creator with a computer.
One of the most remarkable advances is video generation from natural language text. In early 2025, OpenAI (creators of ChatGPT and DALL-E) introduced Sora, their AI model capable of generating videos from textual descriptions. Sora represents a milestone similar to its predecessors: just as ChatGPT produces coherent text and DALL-E creates images from a prompt, Sora can generate a video sequence based solely on written instructions. This is made possible through deep learning technologies that combine language models (to understand our descriptions) with generative vision models trained on vast collections of videos. The AI “understands” what we ask in natural language and turns those instructions into moving scenes, marking a significant leap beyond static image generation.
OpenAI’s Sora: Text-to-Video Generation
Sora is OpenAI’s artificial intelligence system specifically designed to create short videos from text prompts. Trained on a vast library of videos, Sora has learned to recognize movements, contexts, and visual details from the real world, allowing it to recreate them based on the user’s description. In other words, if we ask for “a dog running on the beach at sunset,” the AI identifies concepts like “dog,” “running,” “beach,” and “sunset light” and generates a clip where those ideas come to life in a sequence of images.
One of Sora’s strengths is its ability to generate complex scenes. We can describe multiple elements within the same shot (characters, objects, environment) and even specify the type of movement or action they will perform, and the model will attempt to render them with impressive accuracy. For example, in internal tests, it successfully created a video of “an elegant woman walking down a neon-lit street in Tokyo,” with the prompt detailing specifics such as her clothing (black leather jacket, red dress, sunglasses), her walking attitude, and even that “the street is wet and reflective, creating a mirror effect with the colorful lights.” The result showed exactly the described person wearing the specified outfit, moving with the requested attitude, in a nighttime urban setting with wet ground reflections and neon lights just as instructed. This level of precision illustrates how far AI video generation has come in interpreting and recreating users’ creative visions.
That said, Sora is still in an early development phase. Initially accessible only to researchers, towards the end of 2024 OpenAI released a version called Sora Turbo for a broader group of users. Currently, Sora is available as part of ChatGPT Plus benefits, allowing subscribers to generate videos up to 20 seconds long in 1080p resolution. The platform offers different aspect ratios (horizontal, vertical, square) to suit social media or cinematic formats. Additionally, Sora includes tools to enhance creativity: for example, a storyboard mode that lets users define scene by scene what should happen in each keyframe. It is even possible to “provide your own assets” — such as images or short video clips — to remix or combine existing content with AI-generated footage, creating hybrid videos.
As part of its gradual rollout, OpenAI included Sora in ChatGPT Plus at no additional cost, though with monthly limits (for example, up to 50 videos in 480p per month included in the basic subscription). For those needing greater capacity, a Pro plan is offered with 10 times the usage, support for higher resolutions, and longer clips. It is important to note that Sora still has technical limitations: the company itself acknowledges that it sometimes “generates unrealistic physics and struggles with complex, long-duration actions.” For now, the created videos tend to be short (originally up to 60 seconds were discussed in the research prototype, although the commercial version releases 20-second clips) and don’t always nail every detail 100%, especially in very intricate scenarios. Still, the visual quality achieved and coherence with the user’s prompt are astonishing for a technology that was just a few years ago barely science fiction.
Best Practices for Using AI Video Generators
As with other generative AIs, the user’s ability to communicate with the tool is crucial to obtaining good results. In the case of Sora (and similar models), it is recommended to follow some best practices:
Iterate and refine: It is unlikely to get the perfect video on the first try. A good practice is to iterate: test a prompt, observe the result, and then adjust the description to correct or improve details. We can add missing elements, remove unwanted details, or rephrase confusing phrases. This step-by-step interaction allows us to converge on the video we initially imagined.
Clear and detailed prompts: The more relevant information we provide in the description, the more accurate the resulting video will be. It is advisable to specify the environment, lighting, characters (appearance, clothing, age, etc.), the actions they perform, and even the desired visual style. OpenAI itself states that “the more detailed the prompt description, the more detailed the image (or video) displayed will be.” For example, instead of requesting “a car on the street,” we could specify “a red sports car driving down an urban street at night in the rain, with neon lights reflecting on the wet asphalt.” A prompt rich in nuances helps the AI understand our vision more precisely.
Know the technical limitations: Although impressive, these AIs have their limits. For example, Sora currently generates short clips (a few seconds) and may fail with very prolonged temporal logic or complex physical details. It is important to be aware that, for now, it may not faithfully reproduce the face of a real person or hyperrealistic crowd scenes. Adapting our expectations (and prompts) to what the technology can do will help avoid frustrations. Over time, these limitations will diminish, but at present it is better to keep requests within scenarios manageable for the AI.
Take advantage of platform tools: If the AI offers advanced features (such as Sora’s mentioned storyboard), it is advisable to use them for greater control. Breaking our video into scenes or shots and describing each separately can improve narrative coherence. Likewise, if reference images or predefined styles can be uploaded, it is helpful to do so to guide the aesthetics of the result.
Respect for policies and others’ rights: When using AI video generators, we must comply with the tool’s usage policies. Sora, for example, blocks certain abusive uses: OpenAI expressly prohibits generating child pornography, sexual deepfakes, or other seriously harmful content. Initially, they have also restricted uploading images of real faces to prevent people from making deepfakes of individuals without permission. Following this approach, we as users must avoid requesting videos that violate privacy, copyright, or the integrity of others. It is neither appropriate (nor usually legal) to try to recreate a real person in compromising situations or to pass off falsehoods as truth. AI gives us enormous creative power but entails the responsibility to use it without violating ethical and legal standards.
Responsible and ethical use: A fundamental best practice is not to use these videos to deceive or cause harm. If we create fictional content with AI, especially if it imitates real people, it is advisable to make clear that it is an artificial creation. In the case of Sora, OpenAI has automatically implemented certain safeguards, such as visible watermarks on videos generated by default, and embedded metadata following the C2PA standard that allows verification of the AI origin of the material. These measures aim to provide transparency, so anyone (with the appropriate tools) can identify that the video comes from AI and not a traditional camera. As users, we must preserve these origin marks and act honestly: for example, if we share a video created with Sora on social media, we should clarify that it is an AI-generated animation, avoiding presenting it as authentic. The creator’s intention is key: using AI for creativity, education, or entertainment is valid and exciting; using it to manipulate or defraud, on the other hand, is condemnable.
Deepfakes and Misinformation: Risks of Misuse
Examples of fake videos created with AI that mimic breaking news on social media (labeled as “False” by fact-checkers). These videos use human-like digital avatars to spread misleading information.
As mentioned, one of the most serious concerns surrounding AI video generation is its malicious use for deception. This is where the concept of deepfake comes in. A deepfake is basically audiovisual content falsified using AI: highly convincing but misleading images, audio, and videos can be created by mixing or replacing identities to make them appear real. In fact, the term “deepfake” comes from “deep learning” (the underlying technology) + “fake” (false). In video, a typical deepfake might be a person’s face placed on another’s body in a video, also syncing lip movements with fabricated audio. The result: someone could appear to say or do something that never actually happened.
On social media, concerning cases of deepfakes and fraudulent videos circulating as if they were real have already been detected. For example, in Latin America, dozens of fake videos featuring the well-known journalist Jorge Ramos were identified, where he supposedly makes controversial statements he never actually said. In one case, the presenter was seen announcing the (false) “deportation of Donald Trump’s family,” something that obviously never happened nor was reported by the network he works for—it was a very well-crafted digital montage. There have also been “news broadcasts” with virtual anchors created entirely by AI: people who do not exist, with believable appearance and voice, reading fabricated news. The fact-checking organization Factchequeado warned that on TikTok, the use of AI-generated avatars to deliver “breaking news” about the U.S. was becoming common, many of which turned out to be pure misinformation. These videos did not clarify that the presenter was a synthetic avatar, which could lead the audience to believe they were watching a real journalist reporting truthful facts.
The risks of these forgeries are obvious: they can damage reputations, influence public opinions with false news, and even be used for fraud (imagine a deepfake video of a CEO making a false financial announcement, or a politician “admitting” something scandalous). Misused AI video technology could amplify so-called “fake news” to new levels of plausibility.
In response to this situation, both technology platforms and society at large are seeking solutions. One approach is to develop deepfake detection systems: algorithms that analyze videos and find subtle signs of digital alteration (flaws in face rendering, strange movements, imperfect lip synchronization, etc.). In fact, fact-checkers recommend the public stay alert to “warning signs” in these videos: repetitive or rigid body movements, unnatural or unsynchronized facial expressions with the voice, monotone voices… any detail that reveals it’s not a genuine human. In the examples detected on TikTok, many always used the same avatar with the same background and mechanical gestures—indicative of artificial generation.
Another approach is to promote transparency from the source. Initiatives like OpenAI’s with Sora, incorporating watermarks and origin metadata in AI content, follow this path. Likewise, nonprofit organizations and some governments are discussing regulations: for example, laws that require labeling deepfakes or penalize their use for illicit purposes. Some platforms already explicitly prohibit deceptive deepfakes in their terms of service. The emerging consensus is that, just as AI offers new tools, rules and practices must be established to prevent abuse, ensuring that the line between reality and fiction is not blurred without our consent.
Future Impact on Marketing and Audiovisual Production
Looking ahead, AI video creation promises to be a game-changer in creative industries, advertising, and entertainment. In marketing, for example, the advantages are clear: lower costs, faster speed, and more personalization. A drastic drop in audiovisual production prices is already being seen thanks to these tools—cost reductions by factors of 100 or 1000 are being discussed, meaning something that used to cost \$1000 could now cost \$1 using AI, along with enormous acceleration in ideation and editing times (tasks that used to take days or hours can now be done in minutes by AI). This means marketing teams will be able to produce much more content in the same timeframe, multiplying creative iterations and quickly adapting to trends.
Additionally, AI “levels the playing field” for small creators versus large companies. Historically, producing high-quality videos required resources only big brands had (professional teams, studios, actors, etc.), but now a small startup or independent creator can compete almost on equal footing using AI video tools. Just as social media democratized content distribution, AI democratizes its production. It wouldn’t be surprising to see emerging brands launching campaigns with highly engaging AI-generated videos, competing creatively with corporate giants.
Another exciting trend is content personalization. Traditional advertising made the same ad for millions of people; with AI video, it will be possible to create versions tailored to different segments and even specific individuals. For example, a brand could automatically generate variations of a promotional video by changing certain elements (language, cultural references, the main character) so that each audience feels more connected. Algorithms can adapt videos to users’ tastes, preferences, or demographics, achieving greater engagement. Imagine offer videos where the avatar calls you by name, or a virtual tour of a new car where you see it in your favorite colors; those personalized experiences at massive scale will be possible thanks to generative AI.
In the field of audiovisual production (film, series, music), enormous possibilities also arise. AI video tools can assist in pre-production by generating animated storyboards from scripts or visualizing how a scene would look before actually filming it. Directors and creators could quickly test multiple visual approaches, facilitating creative experimentation. In the longer term, it is conceivable that audiovisual works entirely created by AI or with minimal human intervention will emerge: on-demand animated short films, personalized music videos, etc. In fact, musicians and visual artists are already collaborating with AI to produce hybrid content. In education and training, companies like Synthesia and HeyGen offer AI avatars that present content, allowing the creation of corporate training videos in dozens of languages without hiring actors. Many global companies are adopting these “virtual presenters” to streamline internal communications and save thousands of dollars per video in the process.
Of course, the emergence of these tools also poses labor and creative challenges. Video editors, cameramen, animators, and actors will need to adapt to an environment where some routine tasks will be automated. However, rather than completely replacing the human factor, it is most likely that AI will become an ally that enhances creativity: freeing up time from technical production, allowing focus on strategy, storytelling, and the human aspects of stories. Traditional audiovisual production companies will need to rethink their methods and find ways to add value in an ecosystem where anyone can generate decent content with minimal resources. Imagination, artistic talent, and original vision will be more important than ever to stand out amid a sea of automatically generated content.
In summary, video creation with AI represents a revolutionary leap that is already underway. Tools like OpenAI’s Sora give us a glimpse of a future where audiovisual creativity is more accessible, faster, and more versatile. From advertising to film and education, we will see AI-generated content increasingly integrated into our daily lives. The challenge will be to harness these technologies in a positive and responsible way: marveling at their creative possibilities, but also setting clear limits to prevent deception and abuse. If anything is clear, it is that AI is not just a passing trend but a powerful new tool—much like the video camera or computer once were—that is destined to transform how we tell stories in the digital age. And in that transformation, all of us (creators, consumers, and regulators) have a role to play to ensure the final outcome is a more innovative, democratized, and trustworthy audiovisual ecosystem.