{"id":9892,"date":"2023-01-22T15:41:09","date_gmt":"2023-01-22T15:41:09","guid":{"rendered":"https:\/\/new-blog.artlist.io\/blog\/?p=9892"},"modified":"2026-03-24T07:57:06","modified_gmt":"2026-03-24T07:57:06","slug":"chatgpt-dall-e-ai-and-digital-creators","status":"publish","type":"post","link":"https:\/\/new-blog.artlist.io\/blog\/chatgpt-dall-e-ai-and-digital-creators\/","title":{"rendered":"How AI Tools like ChatGPT help modern video creators"},"content":{"rendered":"\n

AI tools like ChatGPT are now part of how many video creators work, not as a replacement for creative thinking, but as a way to explore ideas, create quick rough drafts, generate production-ready outputs, and solve problems as early as possible in the process. Here\u2019s a quick overview of how creators actually use AI tools across pre-production, production, and post-production, where they help most, and where a human point of view still matters.<\/p>\n\n\n\n

What is generative AI?<\/h2>\n\n\n\n

Generative AI<\/a> is a type of AI that can create new content, like text, images, audio, or video, based on patterns in existing data. You give it a prompt, and it generates a version of how it interprets that prompt. <\/p>\n\n\n\n

For video creators, this is helpful (and time saving) across the entire workflow. You could use AI to outline a script before a shoot, test a thumbnail idea before deciding on a style, or work through a pacing or structure problem before you start editing your work. <\/p>\n\n\n\n

In some projects, AI-generated images and videos might also be used as final outputs, but only after close review and editing. For example, you might use a text tool to shape an idea, an image model to visualize a scene or thumbnail, then move into your editing software to refine the final result if your projects needs it. What matters most is knowing when to keep iterating and when to move on.<\/p>\n\n\n\n

The history and evolution of generative AI<\/h2>\n\n\n\n

Early tools like DALL\u00b7E helped introduce the idea that AI could generate images from text, but the technology has moved quickly since then. Models have improved in quality, flexibility, and control, with newer versions like DALL\u00b7E 3  and GPT Image 1.5<\/a> building on those early foundations. <\/p>\n\n\n\n

Today\u2019s creators don\u2019t rely on a single model or platform. Instead, they work with a growing pool of text, image, and video models, each one fitting different stages of their creative process. Some are better for rough ideas and exploration (such as Kling 1.6 for text to video<\/a>), while others are designed for higher-quality outputs that feed directly into production (such as Nano Banana Pro<\/a> for text to image<\/a>).<\/p>\n\n\n\n

AI isn\u2019t a novelty anymore, it\u2019s a set of tools to be used based on the specific tasks and projects, and is similar to the way you\u2019d choose to use a certain camera, editing software, or plugin.<\/p>\n\n\n\n

What are the risks of generative AI?<\/strong><\/h2>\n\n\n\n
\n
<\/div>\n \"Artlist\n \"Artlist\n \"Artlist\n \"Artlist\n \"Artlist\n<\/div>\n\n\n\n\n

Where AI can cause problems in real creative work<\/strong><\/h2>\n\n\n\n

The biggest risks around generative AI today are practical ones. Generative AI can create text, images, video, and audio that look convincing, even when parts of the output are inaccurate.<\/p>\n\n\n\n

In day-to-day creative work, this usually shows up in smaller, easier to miss ways. Details might be wrong, text can include errors, or images and video could include things that aren’t accurate. For video creators, this matters the most when using AI outputs for references, background elements, or starting points that are rushed to production without taking a closer look. <\/p>\n\n\n\n

It\u2019s always good practice to double-check AI-generated outputs in the same way as you\u2019d review a rough draft, even if it\u2019s tempting to treat everything as production-ready. <\/p>\n\n\n\n

Built-in bias<\/strong><\/h2>\n\n\n\n

Bias can still show up in AI-generated results because models learn from existing images, text, and media. Since AI models are trained on existing data, it means gaps and bias might still appear in the final output. Newer models are improving in areas like skin tone and representation, but they still rely on clear prompts and human review. <\/p>\n\n\n\n

For video creators, this bias can show up in subtle ways. Generated characters may default to familiar roles, appearances, or settings, especially in thumbnails or background images. The easiest way to fix this is to be specific in your prompts, review your output carefully, and look at AI outputs as drafts, not final work.<\/p>\n\n\n\n

Whose rights?<\/strong><\/h2>\n\n\n\n

Creatives rely on copyright law to protect their work and make a living from it.  Most generative AI models \u2013 whether they\u2019re creating text, music, images or video \u2014 are trained on publicly available data. As such, questions around training data and ownership are still being debated.<\/p>\n\n\n\n

For video creators, this matters as a practical risk. If you\u2019re using AI-generated content in professional or commercial work, you need to understand what you can safely publish, monetize, or deliver to clients. Knowing where ownership isn\u2019t clear helps you decide when you can use AI output for a reference or a draft, and when it’s maybe better to use licensed assets<\/a> or original material instead.<\/p>\n\n\n\n

The regulation around AI<\/a> is constantly evolving, and rules vary by platform, region, and use case. In practice, ownership often comes down to how much human input is involved and how the content is used. If you\u2019re publishing work professionally, it also helps to understand what counts as commercial use<\/a> and how licensing applies across different types of creative assets.<\/p>\n\n\n\n

Working with AI\u2019s limits<\/strong><\/h2>\n\n\n\n

Getting usable results often takes iteration, testing, and small adjustments. Understanding how AI image tools work<\/a>, from prompt structure to edits and references, makes the trial-and-error process more efficient over time.<\/p>\n\n\n\n

Clear, specific prompting<\/a> is one of the most effective ways to reduce bias, improve representation, and get results that match your intent.<\/p>\n\n\n\n

 Even then, the results may not work for you.<\/p>\n\n\n\n\n\n

How creators actually use AI in their workflows today<\/strong><\/h2>\n\n\n\n

Audiences are more aware of AI-generated content now, and they often expect to see clear human input, effort, and creative direction behind the work.<\/p>\n\n\n\n

For video creators specifically, AI is increasingly used to generate visual starting points that can then be refined, animated, or folded into larger projects. But visual exploration is only one way that creators use AI tools. They can be used at all stages of the workflow, at the beginning when you\u2019re writing scripts or planning shots, and also later when you\u2019re working through edits, creating alternate versions, or making fixes before a final export. On Artlist<\/a>, AI is used to support real creative work, from smarter search<\/a> to tools that help you explore, generate visuals, and edit your work more efficiently using multiple image and video models that are updated as the newer models are released.<\/p>\n\n\n\n

Inspiration at your fingertips<\/strong><\/h2>\n\n\n\n

When you\u2019re stuck or need to explore new directions, AI can help you generate starting points quickly. Creators often use AI tools to ask broad, exploratory questions early on, especially when planning shoots, researching locations, or shaping an idea.<\/p>\n\n\n\n

For example, you could use AI to generate:<\/p>\n\n\n\n