Advanced generative media workflows are not simple text-to-video prompts. Top customers chain an average of 14 different models for tasks like image generation, upscaling, and image-to-video transitions. This multi-model complexity is a key reason developers prefer open-source for its granular control over each step.

While solo creators can wear all hats, scaling professional AI video production requires specialization. The most effective agencies use dedicated writers, directors, and a distinct role of "AI cinematographer" to focus on generating and refining the visual assets based on the director's treatment.

Successful AI video production doesn't jump from text to video. The optimal process involves scripting, using ChatGPT for a shot list, generating still images for each shot with tools like Rev, animating those images with models like VEO3, and finally, editing them together.

While today's focus is on text-based LLMs, the true, defensible AI battleground will be in complex modalities like video. Generating video requires multiple interacting models and unique architectures, creating far greater potential for differentiation and a wider competitive moat than text-based interfaces, which will become commoditized.

Instead of using generic stock footage, Roberto Nickson uses AI image and video tools like FreePik (Nano Banana) and Kling. This allows him to create perfectly contextual B-roll that is more visually compelling and directly relevant to his narrative, a practice he considers superior to stock libraries.

Traditional video models process an entire clip at once, causing delays. Descartes' Mirage model is autoregressive, predicting only the next frame based on the input stream and previously generated frames. This LLM-like approach is what enables its real-time, low-latency performance.

Avoid the "slot machine" approach of direct text-to-video. Instead, use image generation tools that offer multiple variations for each prompt. This allows you to conversationally refine scenes, select the best camera angles, and build out a shot sequence before moving to the animation phase.

Exceptional AI content comes not from mastering one tool, but from orchestrating a workflow of specialized models for research, image generation, voice synthesis, and video creation. AI agent platforms automate this complex process, yielding results far beyond what a single tool can achieve.

Instead of manually writing prompts for a video AI like Sora 2, delegate the task to a language model like Claude. Instruct it to first research Sora's specific capabilities and then generate prompts that are explicitly optimized for that platform's strengths, leading to higher-quality, more effective outputs.